Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

PDF (3 MB)

Get e-Alerts

Supporting Info (2)»Supporting Information Supporting Information

SUBJECTS:

Abstract

Mass spectrometry-based proteomics coupled to liquid chromatography has matured into an automatized, high-throughput technology, producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality analysis (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream analysis. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC–MS data generated by the MaxQuant1 software pipeline. PTXQC creates a QC report containing a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant’s Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC.

KEYWORDS:

SPECIAL ISSUE

This article is part of the Large-Scale Computational Mass Spectrometry and Multi-Omics special issue.

Introduction

ARTICLE SECTIONS

Jump To

The importance of quality control (QC) and quality assessment (QA) has been long acknowledged. Data quality is a cornerstone of solid research, demanding repeatability and reproducibility.(2) Ideally, small deviations in performance are observable, and their origin can be tracked down.

Adverse effects of missing QC can be found in early proteomics research,(3) which could have been prevented if proper QC was in place.(4)

In 2009, the Amsterdam Principles(5) called for the development of universally applicable quality metrics to ensure that only high-quality data is used in publications and released to public repositories. In 2012, a corollary was published, detailing potential metrics.(6) Additionally, the National Institute of Standards and Technology (NIST) proposed a set of 46 QC metrics in 2010.(7)

Since then, many QC packages have been developed. NIST provide their own pipeline called MSQC(7, 8) for Microsoft Windows, which can read Thermo and Agilent TOF data and uses the Open Mass Spectrometry Search Algorithm (OMSSA)(9) or SpectraST(10) as the identification engine. Unfortunately, the output of MSQC is text-based, i.e., no visualization is provided, and development of OMSSA has been discontinued. A similar approach, extending the NIST metrics, is pursued in QuaMeter,(11) relying on pepXML or mzIdentML file formats, which are not supported by all software packages. Another tool named Metriculator(12) uses the NIST pipeline as the backend and provides plots and tracking of samples via a web interface. Recently, QC workflows were introduced for OpenMS/KNIME, along with an XML-based file exchange format named qcML.(13) SIMPATIQCO(14) is being actively developed and extracts QC metrics like injection times, peptide spectral matches over retention time (RT), protein coverage, etc. directly from Thermo Raw or Agilent Wiff files, running on a dedicated server. Identification requires a local Mascot server or MS Amanda.(15) SIMPATIQCO also offers a qcML export. Amidan et al.(16) reported a machine learning approach to automatically classify standard QC samples that determines overall instrument performance. The classifier should be trained on a per-laboratory basis to account for high lab-to-lab variability. Another tool, which collects and plots QC metrics like chromatograms, source current, and injections times directly from single Thermo Raw files, is RawMeat (http://vastsci.com/rawmeat/). However, since RawMeat performs no actual data processing (e.g., peptide identification), only limited conclusions can be drawn. Extensive data visualization capabilities including QC plots for MS/MS-based proteomics data are offered by various software packages written in R(17) (see ref 18 for an exhaustive review).

To our knowledge, there is no published QC software capable of processing MaxQuant(1) results. However, MaxQuant has a large user base within the proteomics community and would thus benefit greatly from QC software to ensure unbiased downstream analysis.

In principle, raw data could be checked using any QC software as described above, even before running MaxQuant. Unfortunately, some of the above tools are not actively maintained, offer only a command line interface, or produce only text-based results. Additionally, some QC tools (e.g., MSQC) are meant to compare performance only among dedicated QC samples, i.e., they cannot be used for samples of biological interest. However, the major drawback of using such an external QC is the lacking guarantee that MaxQuant will deliver the same performance. This might simply be due to deviating parameter settings (e.g., MS² search tolerance), internal algorithm specifics (e.g., possibility for calibration of RT and m/z, Andromeda(19) search engine, false discovery rate (FDR) model), or the support for special experimental designs (e.g., phosphorylation enrichment). In addition, MaxQuant features algorithms, such as mass recalibration and second peptide search, which might enable data recovery to an extent that is not possible with other tools. Thus, it is paramount to perform QC checks on the results of MaxQuant and report a set of comprehensive metrics.

We developed Proteomics Quality Control (PTXQC), which is capable of reading MaxQuant output and generating a comprehensive report using a wide range of QC metrics. In total, PTXQC reports up to 24 quality metrics (Table 1), of which 19 can be automatically scored using dedicated scoring functions. The scores are collated to create an overview chart (heatmap) that displays up to 19 scores per Raw file for a compressed overview of the whole experiment. The user can subsequently follow up on detailed quality metric plots of interest in the remainder of the report.

Table 1. Overview of Quality Metrics and Plots

file source (.txt)	abbreviation in plots	data source	heatmap score basis	scoring functiona
Parameters	PAR	•General parameters settings (MaxQuant version, modifications, ppm tol., FASTA database, FDR cutoff, etc.)	NA
Summary	SM	•MS² identification rate	(1) Distance to “great” threshold	•LinRef
ProteinGroups	PG	•Protein Intensity (MS¹, iTRAQ reporter, [LFQ])	NA (not suitable for Raw file based heatmap, since an experimental group might correspond to more than one Raw file)
		•Fraction of contaminants
		•User-defined contaminants
		•{SILAC only} Ratio distributions
		•PCA plot
Evidence	EVD	•Peptide Intensity	(2) Intensity threshold	•LinRef
		•Number of protein and peptides per condition (w and w/o matched)	(3) Count threshold	•LinRef
		•MBR RT alignment	(4) Interfile pair distance	•AlignDistc
		•MBR RT matching	(5) Intrafile group distance	•MatchDist
		•Charge distributions	(6) Deviation from prototype	•MedianDistb
		•IDs over RT	(7) Equal counts per RT bin	•Uniform
		•MS¹ decalibration	(8) Proximity to max. tol.	•CenteredRefc
		•MS¹ recalibration error	(9) Centeredness around 0	•GaussDev
		•Contaminants	(10) Summed intensity	•LinRef
		•RT peak width distribution	(11) Deviation from prototype	•BestKSb
		•Twin sequence fraction (oversampling estimation)	(12) % of single MS/MS per Peak	•LinRef
Msms	MSMS	•Missed cleavage	(13) Fraction of MC > 0	•Percent
		•Missed cleavages variance	(14) Deviation from prototype	•MedianDistb
		•MS² fragment mass error	(15) Centeredness around 0	•Centered
MsmsScans	MSMSscans	•TopN over RT	(16) Equal saturation over RT	•Uniform
		•TopN	(17) Reaching highest N consistently	•MaxN
		•% identified by TopN	(18) Equal ID rate for all N	•Uniform
		•Ion Injection time	(19) Fraction of scans > time threshold	•Percent

See Supporting Information for details.

The quality function computes scores per Raw file using other Raw files as reference. All other functions will return an absolute score, which depends only on the Raw file itself.

The quality function relies on parameter settings in MaxQuant, which must be matched in PTXQC. If the mqpar.xml file is present, these settings are extracted automatically.

Methods

ARTICLE SECTIONS

Jump To

A typical shotgun LC–MS experiment in bottom-up proteomics encompasses the following main steps: digestion, separation by high-performance liquid chromatography, and subsequent acquisition of mass spectra (MS) and tandem mass spectra (MS²) data by a mass spectrometer (Figure 1). Subsequently, a processing suite for proteomics data (here, MaxQuant) is used to identify and quantify proteins, typically comparing different biological conditions. Intermediate results (e.g., peptide–spectrum matches) are commonly available as well. Subsequently, it is highly recommended to carry out a QC assessment (e.g., using PTXQC). If quality is satisfactory, then the data is cleared for downstream analysis. Upon rejection, previous steps of the pipeline require optimization. Depending on the severity of the detected problem, a change in MaxQuant software parameters (e.g., calibration tolerance or alignment window) might suffice to pass the QC. Ultimately, QC failure can trigger a complete remeasurement of the samples (e.g., upon unsatisfactory protein digestion).

A good approximation for overall performance of the pipeline is the number of quantified proteins per sample. However, this reflects only the average performance of the pipeline as a whole. If one could benchmark individual stages, then not only QC but also optimization becomes possible. Thus, QC tools that report metrics on individual steps of the shotgun proteomics pipeline can be used to identify poorly performing parts and identify targets for optimization (Figure 1).

Figure 1. Experimental and software workflow for bottom-up shotgun proteomics experiments. First, the protein sample is digested, typically using trypsin, to yield peptides. Subsequently, the sample is subjected to HPLC, separating the peptides by their physicochemical properties. The eluent is then ionizied using electrospray ionization, and the mass/charge ratio of the peptides is measured. The quality of the resulting spectra is influenced by all preceding steps. Spectra are then submitted to MaxQuant for analysis. The resulting output is assessed by PTXQC, and upon passing the quality criteria, it is cleared for downstream analysis. If quality is not satisfactory, then either remeasurement is required or (preferably) MaxQuant parameters are adapted to remove the bias detected by PTXQC.

PTXQC makes use of two distinct but related concepts, namely, quality metrics and quality scores. Quality metrics (such as digestion performance) are shown in the report using different kinds of plots, usually detailing the performance of multiple Raw files concurrently. On the basis of the data underlying these metrics, PTXQC computes a quality score using a scoring function (see below). The scores represent the basis for the overview heatmap, which is presented at the beginning of the report. In summary, quality metrics offer a visual guide to user to judge quality, whereas scores computed from the underlying data represent a mathematically more rigid way to automatically flag data sets as failed or successful.

To conveniently visualize the metrics, PTXQC makes use of different types of plots, multiplotting, and color schemes. If thresholds are known (e.g., MS² fragment ion search tolerance), then they are added for visual guidance.

QC Metrics

PTXQC’s metrics can be assigned to four categories, corresponding to steps in the experimental workflow (sample preparation, LC, MS, and general performance). An abbreviation of the data source (Table 1) is provided with every plot, allowing the user to trace the origin of the information, e.g., PG indicates the MaxQuant’s protein groups table, EVD points to the evidence table, etc.

In the following paragraphs, we introduce three novel and powerful QC metrics exclusively found in PTXQC, namely, custom contaminants and metrics for RT alignment and transfer of spectrum identifications across Raw files. Details on common metrics like digestion efficiency, charge distribution, and ion injection times can be found in the Supporting Information.

Customizable Contaminant Search

While MaxQuant supports customizable contaminant lists, it is sometimes not desirable to modify this file, especially when multiple operators utilize the same MaxQuant installation. On the other hand, flagging a protein posthoc as contaminant is possible only by manually editing the MaxQuant output. Thus, PTXQC offers configurable lists of custom protein contaminants, supplied as a regular expression applied on the protein name or description. If a larger set of proteins with nonoverlapping names is sought, then the user can employ custom FASTA files amended with protein name tags during the MaxQuant analysis or provide a more complex regular expression. The latter allows the PTXQC analysis to be run without rerunning MaxQuant. We compute two abundance measures for contaminants from the evidence table: one based on intensity and the other based on spectral counts. PTXQC reports the sum-of-intensity/proportion of peptides matching the regular expression compared to all peptides. Non-unique peptides and hits to the reverse database are discarded in advance.

Retention Time Alignment and ID-Transfer

MaxQuant’s match-between-runs (briefly described in refs 20 and 21) will align the retention times across Raw files using 3D peaks with identical peptide IDs as landmarks. The alignment function is nonlinear and can correct retention time differences up to a certain extent (by default, 20 min). In a second step, MaxQuant will transfer MS² identifications across Raw files using corrected retention times and an accurate mass, thus assigning a peptide ID to hitherto unlabeled 3D peaks. For samples where MS² coverage was not sufficient to identify all peptides, MBR can significantly increase the number of annotated 3D peaks, therefore providing more data for downstream quantification of proteins. The MaxQuant developers recommend using MBR only on samples with comparable LC gradient conditions.

In the remainder of the this article, we will refer to peptide identifications as genuine if the identification was obtained from an MS² spectrum and passes the FDR filtering, whereas we call an identification transferred if the corresponding 3D peak is annotated via MBR. Additionally, a peptide sequence implicitly includes modifications (e.g., carbamidomethyl), i.e., two identical sequences are regarded as unequal if they have different modifications.

Retention Time Alignment

MaxQuant’s RT alignment function can be reconstructed from the evidence table. Note that MaxQuant’s retention time correction is reported relative to the first Raw file, even though files were aligned using a guide tree for the alignment.(20) Thus, reported shifts may exceed the given 20 min tolerance since RT shifts can accumulate when walking the alignment tree. We found the shape of the alignment function not to be feasible for assessing the success of the alignment quantitatively. Instead, we introduce two new metrics: the first metric is aimed at alignment quality (and is thus an inter-Raw file metric) and the second is aimed at the actual transfer of identifications between Raw files (constructed as an intra-Raw file metric; see the ID Transfer section below).

In order to estimate the alignment quality, PTXQC compares the residual retention time difference of two RT-aligned 3D peaks with identical identifications across Raw files (i.e., using corrected retention times). For example, after alignment, a peptide with sequence DFINGAR with charge state 2, genuinely identified both in files A and B, should have a very similar corrected RT in both files. Such pairs of peptides, genuinely identified in both the reference Raw file and another Raw file, with identical sequence and charge state, are called ID-pairs. For each Raw file, we compute the RT difference of every ID-pair using the calibrated retention time. Ideally, most differences are within MaxQuant’s matching tolerance (see ID Transfer section below). The reasoning is as follows: only if ID-pairs, i.e., landmarks, are aligned well can we expect the subsequent ID transfer to be successful. If ID-pairs are not well-aligned for a certain stretch in RT, then every ID-transfer within this stretch will be a random hit and thus a false positive. PTXQC plots results for each Raw file (Figure 4) and reports the alignment score “EVD: MBR Align” in the heatmap as the percentage of ID-pairs that are within the matching tolerance.

The alignment metric also estimates the maximally required RT alignment window (in rare cases, more than 20 min is needed), allowing the user to make a data-based decision on how to change MaxQuant parameters to obtain a better alignment.

For experimental designs using a prefractionation strategy, PTXQC picks one reference file per fraction and compares it to all proximal Raw files (i.e., the immediate fraction neighbors). This is required since the overlap between distant fractions will usually be small or empty and MaxQuant uses only proximal fractions for transferring IDs. See Figure S1 for an example.

ID Transfer

After retention times have been calibrated using genuine MS² identifications, MaxQuant transfers peptide IDs from any Raw file to any other Raw file (if MaxQuant’s “match-from-and-to” setting was unchanged). Further restrictions apply for fractionated samples (see above). An unidentified 3D peak (target) receives an annotation if a genuinely identified counterpart from another Raw file (source) has a similar calibrated retention time (0.7 to 2 min deviation by default, depending on the MaxQuant version) and if its m/z matches the theoretical m/z of the peptide to be transferred (within 4.5–7 ppm by default, depending on the MaxQuant version).

MaxQuant reports the RT difference between the source identification and its target in the “match-time-difference” column of the evidence table. However, small values do not indicate that this matching is correct, since any unannotated 3D peak with similar RT and m/z is a putative target candidate. Therefore, a robust alignment is paramount for the ID-transfer.

To gauge the correctness of the transfer step, the PTXQC metric compares all transferred identifications to the genuine identifications within each Raw file. If the transfer was correct, then no identification should occur more than once. In particular, a genuine ID (locally confirmed by MS²) and a transferred ID in the same Raw file indicate that the matching targeted the wrong 3D peak (generating a false positive) because the same peptide was already identified genuinely. Alternatively, the MaxQuant feature finding algorithm accidentally split a 3D peak into two separate entities, where only one was identified by MS². In this case, the genuine and transferred IDs will have similar corrected retention times.

PTXQC assigns every 3D peak into one of three classes: “Single”, “Group – in width”, and “Group – out width”. The “Single” class covers all 3D peaks whose peptide sequence and charge state is unique for the Raw file at hand. The other two classes represent 3D peaks that are part of a group, i.e., that have siblings with identical sequence and charge in the same Raw file. Within each peak group, PTXQC uses the retention time deviations to decide if the group is valid (“in width”) or invalid (“out width”). The threshold to decide if a peak group is “in width” is the median RT peak width of the respective Raw file. If the RT span of the group is larger than the typical RT peak, then the evidence is considered segmented and it is assigned to the out-width class, i.e., it is unlikely that the out-width group represents a split 3D peak but, rather, two (or more) entirely different 3D peaks.

Depending on which subset of peaks is used to assign the three classes, different conclusions can be drawn. Considering only genuine 3D peaks and assigning them to a class, we can determine the intrinsic segmentation of a Raw file. The proportion of out-width peaks is usually very small, since a peptide usually elutes only once from the LC column. If we consider only the subset of 3D peaks that were identified via MBR plus all genuine 3D peaks that have the same identification, then we can draw conclusions about the success of the ID-transfer. PTXQC reports the fraction of singlets plus in-width group as the quality score for ID-transfer (see yellow arrows inserted into Figure 5). The out-width fraction can rise considerably, depending on the success of the alignment, resulting in a lower score. Finally, if we consider all 3D peaks (irrespective of if they are genuine or transferred) and assign a class to them, then we obtain an overall view on the segmentation issue.

We emphasize that the plain number of transferred IDs per Raw file is a rather inaccurate indicator for correct ID transfers since (a) samples with high genuine MS² coverage and good alignments to other samples are expected to yield few ID transfers and (b) high-complexity samples with low genuine MS² coverage and bad alignment to other samples are expected to yield many false positive ID transfers.

The MBR-FDR calculation mentioned in Geiger et al.(21) is a valid alternative to our ID-transfer scoring function as described above. However, MBR-FDR values are not calculated by default and cannot be activated within the MaxQuant application. This feature needs to be manually enabled using the “matchBetweenRunsFdr” entry in the MaxQuant XML configuration file. Subsequently, the “match.q.value” column of the evidence table will contain q-values for matched evidence. However, the validity of this intrinsic MaxQuant metric critically depends on alignments of good quality.

If either alignment or matching is unsatisfactory, then MBR should be disabled partially or completely.

QC Scores

For 19 out of 24 quality metrics supported by PTXQC, we have devised a set of mathematical equations (Table 1 and Supporting Information) that allows one quality score to be computed per Raw file and metric. The remaining five metrics remain unscored since they are based on the protein groups table where a 1:1 relationship between the experimental groups and Raw files cannot be guaranteed. Each quality score ranges between zero and one. The exact mathematical formula is listed in the Supporting Information. A heatmap summarizes up to 19 quality metrics per Raw file, with quality scores represented by a color gradient. The columns (metrics) are ordered according to the analytical flow (Figure 1). Each row represents one Raw file. Green tiles indicate good quality, red indicates failure, and black marks indicate intermediate performance.

The majority of scores (16 of 19) are reference-less, i.e., their value depends only on the particular Raw file at hand. Thus, all Raw files can potentially achieve very good performance, i.e., there is no relative scaling. The three remaining quality metrics are scored relative to the data available in the study: “Charge distribution”, “RT peak width over time”, and “Missed cleavages variance”. These metrics depend on the data at hand, and target values are hard to formalize. In particular, the charge distribution should be similar across all Raw files, whereas the exact share of doubly charged peptides is of secondary concern. The scoring function is therefore selecting the most representative Raw file and penalizes deviations from this reference. A similar argument applies for the variance of missed peptide cleavages. Recent research shows that missed cleavages do not negatively influence protein quantification if all samples share the same degree of digestion.(22) The degree of digestion itself is additionally represented by a “Missed Cleavage” score. Finally, the RT peak width strongly depends on the LC setup. The quality score penalizes Raw files that deviate strongly in their RT peak width distribution compared to a representative Raw file of the same study.

Results

ARTICLE SECTIONS

Jump To

In this section, we will provide an example of the overview heatmap and in-depth examples of the three metrics described in the Methods section. Please refer to the Supporting Information for a detailed description of the remaining metrics, example figures, full QC reports, and a description of the data sets.

Overview Heatmap

We use a prefractionated, TMT-labeled data set(23) consisting of 24 Raw files as the basis for the heatmap show in Figure 2. The MaxQuant result folder was obtained from the Pride Archive,(24) ID PXD000427. Expected protein and peptide counts per Raw file were adapted from 3500 and 15 000 to 1000 and 3000, respectively, via PTXQC’s YAML configuration file (see below) to account for the reduced protein content due to prefractionation.

Figure 2. Heatmap overview of a TMT-labeled data set. Columns denote the metric; rows correspond to Raw files. The color gradient for each cell ranges from best (green), to underperforming (black), and, finally, fail (red). Column names are sorted and color-coded (gray or black, alternating) by the four main steps in the analytical workflow.

Sample preparation quality is shown in the first five columns of the heatmap: The first column, “EVD: Contaminants”, represents common laboratory contaminants (e.g., keratins), as annotated by MaxQuant. For the TMT-labeled data set, most samples contain low amounts of contaminants; only the last two fractions show a minor increase. Peptide intensity (column 2) is as expected, except for fractions 1, 15, 18, and 19. Fractions with low overall intensity show a poor ion injection time (column 11), MS² identification rate (column 17), and number of identified peptides (column 20). Digestion was very thorough with few missed cleavages (“MSMS: MC”, column 3), except for fractions 6 and 19–21. Not surprisingly, the same files are also negatively indicated in the MC variation column since they deviate from the majority of files with good digestion (column 4).

LC performance is shown in columns 6 to 10. Along the LC gradient, peptides do not seem to elute uniformly over time (“MSMScans: TopN over RT”, column 6), also affecting the number of successful identifications over time (“EVD: ID rate over RT”, column 7). The first fraction shows unusual RT peak width (column 8). The alignment step of Match-between-runs has failed for fractions 1 and 13 (column 9); fraction 1 simply shares no landmarks with its immediate neighbors (fraction 2); thus, MaxQuant could not align them. Fraction 13 should align very well, but it was unintentionally labeled as fraction 3 in the MaxQuant configuration. Hence, MaxQuant (and PTXQC) cannot find any landmarks for alignment. However, PTXQC’s ID transfer metric (column 10) suggests that fraction 13 behaved very well. The reason is simply that all IDs transferred to fraction 13 (from fraction 2 and 4) have sequences that are not expected at such a late fractionation stage. Hence, transferred IDs are singlets, not conflicting with genuine MS² IDs from fraction 13. Fraction 1, on the other hand, shows an NA score since it received no transferred IDs.

Instrument performance is reflected in columns 11–19: MS¹ calibration was very good on the instrument-level already (“EVD: MS Cal-Pre”, column 13, with 20 ppm tolerance). MaxQuant’s internal mass recalibration cannot be scored (“EVD: MS Cal-Post” is NA, column 14) since mass deltas for chemically modified peptides (such as TMT and iTRAQ) are reported incorrectly by MaxQuant (see Supporting Information for details). The instrument mostly reached its TopN limit (“MSMSScans: TopN high”, column 18).

General parameters, reflecting overall performance, are shown last: Not surprisingly, the overall protein and peptide counts per Raw file vary widely (columns 20 and 21), with the richest fraction containing at most 900 proteins.

In summary, a few fractions show extremely low peptide abundance, which causes dependent metrics such as ion injection time and MS/MS ID rate to underperform. MBR across neighboring fractions has worked very reliably and should remain enabled, on average contributing 36% increased ID counts per fraction. If resources permit, then MaxQuant should be rerun with a corrected fraction assignment for fraction 13, which received 13 wrong peptide assignments (from 34 PSMs) in addition to the genuinely identified 1438 PSMs. Conversely, real fractions 2, 3, and 4 also received false positive identifications from fraction 13 (since it was labeled as fraction 3). For subsequent studies of similar sample complexity, we would recommend combining low-abundance fractions with neighboring fractions prior to LC–MS measurement, reducing the number of sample injections, and avoiding most problems mentioned here.

Custom Contaminant Detection (Mycoplasma)

To demonstrate the flexibility of PTXQC’s custom contaminant approach, we searched for Mycoplasma hyorhinis in a study using HEK293 cell lines. Mycoplasma contamination should be avoided at all costs since infection can alter cell metabolism and physiology. Infection of tissue culture cell lines was first described over 50 years ago, and to this day, it remains a persistent problem since it is subtle and hard to detect unless specific measures are taken. Sources of contamination range from animal-derived media products and laboratory personnel to cross-contamination, with estimated cell culture contamination rates up to 35%.(25) LC–MS data can serve as a basis for a confirmatory experiment. Creating a suitable protein database is straightforward. Since mycoplasma contamination can usually be attributed to a few mycoplasma strains (here, M. hyorhinis), the choice of strains is paramount for successful detection. We advise against using a mycoplasma database containing all strains. Instead, the search should be restricted to likely candidate strains. Adding a full-blown database will unnecessarily increase the peptide search space and most likely reduce the number of successfully identified peptide spectra at a fixed FDR.(26) For example, the UniRef90(27) database contains a remarkably high number (27 535) of mycoplasma protein clusters.

Figure 3 shows a plot with results from a mycoplasma query. For this analysis, we included an unmodified M. hyorhinis FASTA database during the MaxQuant run and instructed PTXQC to search for protein hits containing the string “mycoplasma”. Two samples can be clearly identified as being contaminated by M. hyorhinis, contributing almost 5% of the total sample content. These files should be excluded from downstream analysis. Furthermore, the source of contamination needs to be tracked down and eliminated.

Figure 3. A custom database containing proteins from *Mycoplasma hyorhinis* was included during the MaxQuant analysis of an in-house human QC data set. PTXQC was configured to search for mycoplasma proteins. (A) Summary of the relative abundance (red) and spectral counts (blue) of proteins (or protein descriptions) containing the string “MYCOPLASMA”. The first two Raw files (file 1, file 2) serve as negative controls, in addition to two Raw files with known contamination (file 3, file 4), as confirmed by both intensity and spectral counting. The default threshold of 1% is plotted by PTXQC as a horizontal dashed line for visual guidance. Exceeding the threshold will report the respective Raw file as failed in the overview heatmap. (B) Corresponding heatmap summarizing the whole study. The second column shows the scores for the mycoplasma query. This column is present only if a custom contaminant query is requested via the PTXQC configuration file.

Retention Time Alignment

MaxQuant’s Match-between-runs represents a valuable mechanism to boost the protein coverage and increase the number of quantifiable proteins. However, it should be used only under comparable column conditions for all samples involved. However, the exact degree of comparability is hard to quantify by manual analysis. Using a set of four files from an in-house HEK293 QC study, we demonstrate the sensitivity of our alignment and ID-transfer metrics. Files 1 and 2 were measured on the same day, file 3, the following day, and file 4, under different column conditions a few months earlier.

Figure 4 shows the alignment plot and the corresponding scores for the RT alignment. The RT calibration function reported by MaxQuant is normalized with respect to file 1. File 2 aligns perfectly, whereas file 3 can be only partially aligned. File 4 used a different column, resulting in a failed alignment. All ID-pairs between files 1 and 4 show a large residual delta RT (ΔRT) after alignment, reaching up to 75 min, which is much larger than MaxQuant’s target value of <0.7 min (the default for MaxQuant v1.5, which was used here). However, MaxQuant, by default, searches only within a 20 min RT tolerance window for an optimal alignment. The required width of the RT tolerance window can be gauged visually using the maximum vertical distance between the ID-pairs (red/green) and the alignment function (blue). Here, we can estimate the maximal RT difference between the two columns to be around 85 min (see yellow arrow, Figure 4a), indeed suggesting very different column conditions considering the total gradient length of 300 min.

In summary, the data indicate that an optimization of MaxQuant parameters could rescue the failed alignment. This is a promising solution since it avoids a costly remeasurement of data on one hand and loss of information by disabling the failed MBR on the other hand. One minor drawback is a potential increase in MaxQuant processing time due to the larger search space for alignment. However, for this data set, we did not observe a longer runtime when increasing the RT tolerance window settings from 20 to 100 min. Indeed, the new setting yields a partially successful alignment (Figure 4b). However, overlap of only 11% of ID-pairs in file 4 still renders this alignment unacceptable. Performance of file 3 even decreases from 58 to 40%. Figure 4c shows alignment scores extracted from the heatmap plot corresponding to Figure 4a,b. In conclusion, only files 1 and 2 should be aligned. Files 3 and 4 do not align well to any other file and should be excluded from MBR.

Figure 4. Retention time correction using Match-between-runs. Alignment performance is judged using the residual RT difference (ΔRT) of identical genuine 3D peak pairs after alignment with respect to a reference file (file 1). Each ID-pair is represented by a dot: green dots indicate that the underlying 3D peaks are successfully aligned, with a residual RT difference of less than 0.7 min. Red dots indicate that the alignment was unable to bring the 3D peaks close enough in RT (>0.7 min). The RT correction function of MaxQuant is shown in blue. The fraction of good pairs is given in the panel title, e.g., 99% of the pairs between the reference (file 1) and file 2 are successfully aligned. (A) Four Raw files of human QC samples with varying degrees of alignment success (decreasing). MaxQuant’s RT alignment tolerance window was set to the default of 20 min. The horizontal yellow arrow indicates the required RT alignment tolerance (∼85 min). (B) The same files as in (A) but with a larger RT alignment tolerance of 100 min. Note the increased fraction of good ID-pairs for file 4 (11%) due to a small region between 200 and 250 min that was now successfully aligned. (C) Side-by-side representation of the MBR alignment scores for the analyses in A (left column) and B (right column) as shown in the heatmap. The actual heatmap has many more columns; we show only the column of interest, “EVD: MBR Align”. File 3 shows a trend toward being colored red (due to the score decreasing from 58 to 40%); file 4 shows a slight improvement (from 0 to 11%).

Larger data sets with distinct subsets of column conditions usually benefit from introducing an artificial fraction assignment during the configuration of MaxQuant. By assigning non-neighboring fractions to each subset (e.g., fraction 1 for all samples with LC-column A; fraction 5 for all samples with LC-column B, etc.), MaxQuant will apply MBR only within the groups of similar column conditions. On one hand, samples in the same group align well and benefit mutually from transferred IDs. On the other hand, false positive ID-transfers due to failed alignments are avoided.

ID Transfer

Our second metric visualizes the performance of the second step during MaxQuant’s Match-between-runs, i.e., the transfer of identifications across Raw files after retention times have been aligned. Failed alignments usually lead to (a) few identifications being transferred and (b) a large proportion of false positive annotations. In order to quantify the ID-transfer performance, we inspect the increase in segmentation by Match-between-runs (i.e., increase of identically annotated 3D peaks with widely different retention times within one Raw file).

Figure 5 shows the ID-transfer corresponding to the analysis conducted above for the RT alignment (Figure 4). Figure 5a clearly shows false positive identification transfers to file 4 (red bar), affecting 100% – 26% = 74% of all transferred IDs. The overall impact (“all” column, row 4) is not severe: file 4 received only 507 transferred IDs (vs 26 298 genuine IDs locally identified). Consequently, 507 peptide quantifications (∼2%) are certainly based on wrong identifications since we already know that alignment completely failed for file 4 (compare to Figure 4a). Thus, 74% for out-width peaks is an underestimation and, conversely, singlets are overestimated (i.e., false positive ID transfers that are unique to the target Raw file and cannot be detected as false positives since they have no genuine counterpart to create an out-width group).

Using a partially successful alignment (100 min RT tolerance; compare to Figure 4b), we also find slightly improved ID-transfer performance (compare Figure 5, panel A vs B, increase from 26 to 50%).

In conclusion, files 1 and 2 should be included during MBR (requiring a change in MaxQuant’s match-from-and-to settings and a subsequent rerun of MaxQuant) since they show sufficient gain in IDs and almost no segmentation. After the restriction of MBR to files 1 and 2, the (already good) ID-transfer scores for files 1 and 2 increase further (from 90 to 97% for both files; data not shown) since false positive transfers from files 3 and 4 are avoided.

In most data sets that we examined (data not shown), we find MBR to increase the segmentation, i.e., create out-width groups. The severity, however, depends highly on the alignment quality. This is not surprising since a good alignment will prevent false ID transfers. In general, both the alignment score and ID-transfer score should be >95% for each Raw file participating in MBR.

Figure 5. ID-transfer performance of Match-between-runs. Per Raw file (rows), three different aspects of evidence are shown (columns): “genuine” uses only 3D peaks that have genuine MS² identifications, “transferred” ignores 3D peak groups that are purely genuine, and “all” considers all evidence (genuine + transferred). Each stacked bar contains three peak classes, together summing to 100% of peaks: single, group (in width), and group (out width). (A) Four Raw files of human QC samples. Files 1 and 2 were measured on the same day, file 3, the following day, and file 4, under different column conditions (aging) a few months earlier. MaxQuant’s RT alignment tolerance was set to the default of 20 min. Most IDs transferred to file 4 are false positives (large red bar in the “transferred” column). The overall effect is not drastic (“all” column) since most IDs in file 4 are genuine and only few IDs were transferred to file 4. (B) The same files as in (A) but with a larger RT alignment tolerance of 100 min. Note the decreased contribution of the “group (out-width)” for file 4, indicating fewer false positive matches. (C) Side-by-side representation of the MBR ID-transfer scores for the analyses in A (left column) and B (right column) as shown in the heatmap. The actual heatmap has many more columns; we show only the column of interest, “EVD: MBR ID-Transfer”. The first three files show almost no change, whereas file 4 shows an improvement (dark red to black).

Report Configuration

PTXQC is capable of extracting parameters from the MaxQuant configuration file (mqpar.xml) automatically, thus reducing the user’s configuration effort to a minimum. Other parameters, e.g., individual target thresholds for the number of identified proteins, can be configured via a configuration file in YAML format [http://www.yaml.org]; see Table 2 for an example. The default configuration has sensible defaults for high-complexity samples acquired on an LTQ-Velos Orbitrap and a long nano-LC gradient of 4 h. The user is free to specify new default settings for different setups (e.g., fractionated samples, long/short LC gradients) and apply them depending on the data set at hand.

Table 2. Shortened PTXQC Configuration File in YAML Formata

^{Table a}

To disable all plots based on proteinGroups.txt, the parameter “File → ProteinGroups → enabled” should be changed from yes to no. A detailed manual of parameters and their values is provided with the PTXQC package.

Additionally, the configuration file allows only a subset of metrics to be enabled, permitting the evaluation of incomplete MaxQuant result folders or reducing the size of reports. Input file names are automatically shortened or renamed to allow the figure axis annotation within plots to be compact. If desired, the user can modify the name mapping and assign new file names globally.

Discussion

ARTICLE SECTIONS

Jump To

We have introduced PTXQC, a tool that greatly facilitates and automates QC checks of proteomics data. The QC tool was developed to compare samples from the same batch but also from different batches to allow for comparisons of multiple parameters in an easy and structured way. This ultimately became necessary when working with large sample batches regardless if SILAC was used or label-free quantitation was applied. Differences in the sample input, digestion efficiency, or machine performance ultimately influence identification and quantification of peptides. Thus, besides the (desired) biological changes in protein abundance, technical limitations can introduce significant bias into the data. If no QC is applied, then it is hard to find the origin of the variance within the data. At the same time, a positive QC increases the confidence of the experimental results and marks an important step before data publication.

We have introduced two new metrics (RT alignment and ID-transfer) to judge the Match-between-runs functionality of MaxQuant. Also, visualization and scoring of contaminations, as demonstrated on the example of mycoplasma, have proven to be useful in our day-to-day routine. PTXQC has additional convenience functionality (e.g., detecting mass calibration issues), which is described in the Supporting Information.

We believe PTXQC is useful to a wide audience and can drastically shorten the number of data evaluation/remeasurement iterations since quality can be checked directly without scripting/programming experience. The heatmap provides a wealth of information on a single page at the beginning of the report, allowing underperforming parts of the pipeline to be quickly tracked or failed samples to be detected. The underlying quality scores are automatically exported to a text file and can be readily used for automated annotation of data sets and to trigger notifications. Since PTXQC supports QC measures for many checkpoints along the shotgun proteomics pipeline, it is also suitable for performance optimization.

Additionally, but not less important, a structured QC is necessary for every proteomics platform to ensure a constant level of performance. For example, we use PTXQC to monitor instrument performance over time using a human cell line standard.

Future extensions of PTXQC include support for qcML, an XML-based reporting format for QC and addition of other quality metrics, such as reporter-ion fragmentation efficiency.

Software Information

ARTICLE SECTIONS

Jump To

Runtime

If the MaxQuant result folder is placed on a local spinning hard disk, then a small number of samples is processed on the order of minutes on a standard desktop PC. A larger study comprising 350 Raw files, featuring a MaxQuant result folder of 25 GB, was processed in 75 min. On average, each Raw file requires about 15 s for processing.

System Requirements

PTXQC will run on any modern operating system (Windows, Linux, or MacOSX) where the R software(17) can be installed and usually requires less than 2 GB of RAM. For larger studies with more than 100 Raw files, we recommend a 64 bit operating system with at least 8 GB of RAM.

MaxQuant Support

PTXQC was designed to support a wide range of MaxQuant versions, starting from MaxQuant 1.0.13 to the current version, 1.5. Recent versions of MaxQuant provide additional functionality (e.g., Match-between-runs). PTXQC will automatically detect their presence and incorporate the data into the report. Note that PTXQC can currently read only MaxQuant txt files. Output from software packages other than MaxQuant would require appropriate reformatting into a MaxQuant-like CSV format to enable processing by PTXQC.

Target Audience

PTXQC is designed for a wide audience (including technicians operating the instrument, biologists providing the sample, or bioinformaticians conducting downstream analysis) and can be run from within R (all operating systems) or using a convenient drag-and-drop functionality (Windows only), requiring basic computer skills only.

Software Availability and Documentation

The software is available open source under a GPL license at https://github.com/cbielow/PTXQC, along with documentation (for users and developers) and the sample data used here. PTXQC is actively used in our lab, ensuring future maintenance. We welcome suggestions and contributions from the community.

Sample Data

All data used in this article was obtained from (PXD000427) or uploaded to (PXD003133, PXD003134) the PRIDE archive.(24) For more information, see Supporting Information 1.

Supporting Information

ARTICLE SECTIONS

Jump To

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.5b00780.

Complete reports for the data sets generated by PTXQC (ZIP)
Summary of data sets, including PRIDE archive identifiers, Figure S1, and a detailed description of all metrics and scoring functions (PDF)

The authors declare no competing financial interest.

Terms & Conditions

Electronic Supporting Information files are available without a subscription to ACS Web Editions. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society. For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system. Information about how to use the RightsLink permission system can be found at http://pubs.acs.org/page/copyright/permissions.html.

Acknowledgment

ARTICLE SECTIONS

Jump To

We would like to thank Olga Vvedenskaya for critically reading the manuscript prior to submission. C.B. was supported by the HepatomaSys project (grant no. 0316172B), funded by the German Federal Ministry of Education and Research (BMBF). G.M. and S.K. gratefully acknowledge funding by BMBF and the Senate of Berlin via the Berlin Institute for Medical Systems Biology.

Abbreviations

iTRAQ	isobaric tag for relative and absolute quantitation
SILAC	stable isotope labeling by amino acids in cell culture
QA	quality analysis
QC	quality control
NIST	National Institute of Standards and Technology
OMSSA	Open Mass Spectrometry Search Algorithm
PTXQC	Proteomics Quality Control
LFQ	label-free quantification
RT	retention time
FDR	false discovery rate
MC	missed cleavages
MBR	Match-between-runs
ppm	parts per million
RSD	relative standard deviation
TMT	tandem mass tag
PCA	principal component analysis

References

ARTICLE SECTIONS

Jump To

This article references 27 other publications.

1
Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification Nat. Biotechnol. 2008, 26, 1367– 1372 DOI: 10.1038/nbt.1511
[Crossref], [PubMed], [CAS], Google Scholar
1
MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification
Cox, Juergen; Mann, Matthias
Nature Biotechnology (2008), 26 (12), 1367-1372CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
Efficient anal. of very large amts. of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resoln., quant. MS data. Using correlation anal. and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over std. techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome expt. and allows statistically robust identification and quantification of >4000 proteins in mammalian cell lysates.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhsVWjtLzJ&md5=675d31ca84e3a7e4fb9bdd601d8075ea
2
Tabb, D. L. Quality assessment for clinical proteomics Clin. Biochem. 2013, 46, 411– 420 DOI: 10.1016/j.clinbiochem.2012.12.003
[Crossref], [PubMed], [CAS], Google Scholar
2
Quality assessment for clinical proteomics
Tabb, David L.
Clinical Biochemistry (2013), 46 (6), 411-420CODEN: CLBIAS; ISSN:0009-9120. (Elsevier B.V.)
A review. Proteomics has emerged from the labs of technologists to enter widespread application in clin. contexts. This transition, however, has been hindered by overstated early claims of accuracy, concerns about reproducibility, and the challenges of handling batch effects properly. New efforts have produced sets of performance metrics and measurements of variability that establish sound expectations for expts. in clin. proteomics. As researchers begin incorporating these metrics in a quality by design paradigm, the variability of individual steps in exptl. pipelines will be reduced, regularizing overall outcomes. This review discusses the evolution of quality assessment in 2D gel electrophoresis, mass spectrometry-based proteomic profiling, tandem mass spectrometry-based protein inventories, and proteomic quantitation. Taken together, the advances in each of these technologies are establishing databases that will be increasingly useful for decision-making in clin. experimentation.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFyquw%253D%253D&md5=413a5ec5acd42ed37327dd0813a64175
3
Petricoin, E. F., III; Ardekani, A. M.; Hitt, B. A.; Levine, P. J.; Fusaro, V. A.; Steinberg, S. M.; Mills, G. B.; Simone, C.; Fishman, D. A.; Kohn, E. C.; Liotta, L. A. Use of proteomic patterns in serum to identify ovarian cancer Lancet 2002, 359, 572– 577 DOI: 10.1016/S0140-6736(02)07746-2
[Crossref], [PubMed], [CAS], Google Scholar
3
Use of proteomic patterns in serum to identify ovarian cancer
Petricoin, Emanuel F., III; Ardekani, Ali M.; Hitt, Ben A.; Levine, Peter J.; Fusaro, Vincent A.; Steinberg, Seth M.; Mills, Gordon B.; Simone, Charles; Fishman, David A.; Kohn, Elise C.; Liotta, Lance A.
Lancet (2002), 359 (9306), 572-577CODEN: LANCAO; ISSN:0140-6736. (Lancet Publishing Group)
New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathol. changes within an organ might be reflected in proteomic patterns in serum. The authors developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionization). A preliminary "training" set of spectra derived from anal. of serum from 50 unaffected women and 50 patients with ovarian cancer were analyzed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders. The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognized as not cancer. This result yielded a sensitivity of 100% (95% CI 93-100), specificity of 95% (87-99), and pos. predictive value of 94% (84-99). These findings justify a prospective population-based assessment of proteomic pattern technol. as a screening tool for all stages of ovarian cancer in high-risk and general populations.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XhsFansbw%253D&md5=7b23ec6a32f27dd67e874d30fdfc6220
4
Baggerly, K. A.; Morris, J. S.; Coombes, K. R. Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments Bioinformatics 2004, 20, 777– 785 DOI: 10.1093/bioinformatics/btg484
[Crossref], [PubMed], [CAS], Google Scholar
4
Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments
Baggerly, Keith A.; Morris, Jeffrey S.; Coombes, Kevin R.
Bioinformatics (2004), 20 (5), 777-785CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
Motivation: There has been much interest in using patterns derived from surface-enhanced laser desorption and ionization (SELDI) protein mass spectra from serum to differentiate samples from patients both with and without disease. Such patterns have been used without identification of the underlying proteins responsible. However, there are questions as to the stability of this procedure over multiple expts. Results: We compared SELDI proteomic spectra from serum from three expts. by the same group on sepg. ovarian cancer from normal tissue. These spectra are available on the web at. In general, the results were not reproducible across expts. Baseline correction prevents reprodn. of the results for two of the expts. In one expt., there is evidence of a major shift in protocol mid-expt. which could bias the results. In another, structure in the noise regions of the spectra allows us to distinguish normal from cancer, suggesting that the normals and cancers were processed differently. Sets of features found to discriminate well in one expt. do not generalize to other expts. Finally, the mass calibration in all three expts. appears suspect. Taken together, these and other concerns suggest that much of the structure uncovered in these expts. could be due to artifacts of sample processing, not to the underlying biol. of cancer. We provide some guidelines for design and anal. in expts. like these to ensure better reproducible, biol. meaningfully results.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXitlyhs7Y%253D&md5=6d13a31b22d983b72b31efa9d4246d77
5
Rodriguez, H.; Snyder, M.; Uhlén, M.; Andrews, P.; Beavis, R.; Borchers, C.; Chalkley, R. J.; Cho, S. Y.; Cottingham, K.; Dunn, M. Recommendations from the 2008 international summit on proteomics data release and sharing policy: The Amsterdam principles J. Proteome Res. 2009, 8, 3689– 3692 DOI: 10.1021/pr900023z
[ACS Full Text ], [CAS], Google Scholar
5
Recommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy: The Amsterdam Principles
Rodriguez, Henry; Snyder, Mike; Uhlen, Mathias; Andrews, Phil; Beavis, Ronald; Borchers, Christoph; Chalkley, Robert J.; Cho, Sang Yun; Cottingham, Katie; Dunn, Michael; Dylag, Tomasz; Edgar, Ron; Hare, Peter; Heck, Albert J. R.; Hirsch, Roland F.; Kennedy, Karen; Kolar, Patrik; Kraus, Hans-Joachim; Mallick, Parag; Nesvizhskii, Alexey; Ping, Peipei; Ponten, Fredrik; Yang, Liming; Yates, John R.; Stein, Stephen E.; Hermjakob, Henning; Kinsinger, Christopher R.; Apweiler, Rolf
Journal of Proteome Research (2009), 8 (7), 3689-3692CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
Policies supporting the rapid and open sharing of genomic data have directly fueled the accelerated pace of discovery in large-scale genomics research. The proteomics community is starting to implement analogous policies and infrastructure for making large-scale proteomics data widely available on a precompetitive basis. On August 14, 2008, the National Cancer Institute (NCI) convened the "International Summit on Proteomics Data Release and Sharing Policy" in Amsterdam, The Netherlands, to identify and address potential roadblocks to rapid and open access to data. The six principles agreed upon by key stakeholders at the summit addressed issues surrounding (1) timing, (2) comprehensiveness, (3) format, (4) deposition to repositories, (5) quality metrics, and (6) responsibility for proteomics data release. This summit report explores various approaches to develop a framework of data release and sharing principles that will most effectively fulfill the needs of the funding agencies and the research community.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXmtVGrsLw%253D&md5=9f55d19f947d886bb522677024eda199
6
Kinsinger, C. R.; Apffel, J.; Baker, M.; Bian, X.; Borchers, C. H.; Bradshaw, R.; Brusniak, M.-Y.; Chan, D. W.; Deutsch, E. W.; Domon, B. Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles) J. Proteome Res. 2012, 11, 1412– 1419 DOI: 10.1021/pr201071t
[ACS Full Text ], [CAS], Google Scholar
6
Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles)
Kinsinger, Christopher R.; Apffel, James; Baker, Mark; Bian, Xiaopeng; Borchers, Christoph H.; Bradshaw, Ralph; Brusniak, Mi-Youn; Chan, Daniel W.; Deutsch, Eric W.; Domon, Bruno; Gorman, Jeff; Grimm, Rudolf; Hancock, William; Hermjakob, Henning; Horn, David; Hunter, Christie; Kolar, Patrik; Kraus, Hans-Joachim; Langen, Hanno; Linding, Rune; Moritz, Robert L.; Omenn, Gilbert S.; Orlando, Ron; Pandey, Akhilesh; Ping, Peipei; Rahbar, Amir; Rivers, Robert; Seymour, Sean L.; Simpson, Richard J.; Slotta, Douglas; Smith, Richard D.; Stein, Stephen E.; Tabb, David L.; Tagle, Danilo; Yates, John R., III; Rodriguez, Henry
Journal of Proteome Research (2012), 11 (2), 1412-1419CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On Sept. 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: (1) an evolving list of comprehensive quality metrics and (2) stds. accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data.By agreement, this article is published simultaneously in the Journal of Proteome Research, Mol. and Cellular Proteomics, Proteomics, and Proteomics Clin. Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhsVWmsrvM&md5=0cb2de0296ec2af55330b60f61df84a9
7
Rudnick, P. A.; Clauser, K. R.; Kilpatrick, L. E.; Tchekhovskoi, D. V.; Neta, P.; Blonder, N.; Billheimer, D. D.; Blackman, R. K.; Bunk, D. M.; Cardasis, H. L. Performance Metrics for Liquid Chromatography-Tandem Mass Spectrometry Systems in Proteomics Analyses Mol. Cell. Proteomics 2010, 9, 225– 241 DOI: 10.1074/mcp.M900223-MCP200
[Crossref], [PubMed], [CAS], Google Scholar
7
Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses
Rudnick, Paul A.; Clauser, Karl R.; Kilpatrick, Lisa E.; Tchekhovskoi, Dmitrii V.; Neta, Pedatsur; Blonder, Niksa; Billheimer, Dean D.; Blackman, Ronald K.; Bunk, David M.; Cardasis, Helene L.; Ham, Amy-Joan L.; Jaffe, Jacob D.; Kinsinger, Christopher R.; Mesri, Mehdi; Neubert, Thomas A.; Schilling, Birgit; Tabb, David L.; Tegeler, Tony J.; Vega-Montoto, Lorenzo; Variyath, Asokan Mulayath; Wang, Mu; Wang, Pei; Whiteaker, Jeffrey R.; Zimmerman, Lisa J.; Carr, Steven A.; Fisher, Susan J.; Gibson, Bradford W.; Paulovich, Amanda G.; Regnier, Fred E.; Rodriguez, Henry; Spiegelman, Cliff; Tempst, Paul; Liebler, Daniel C.; Stein, Stephen E.
Molecular and Cellular Proteomics (2010), 9 (2), 225-241CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
A major unmet need in LC-MS/MS-based proteomics analyses is a set of tools for quant. assessment of system performance and evaluation of tech. variability. Here we describe 46 system performance metrics for monitoring chromatog. performance, electrospray source stability, MS1 and MS2 signals, dynamic sampling of ions for MS/MS, and peptide identification. Applied to data sets from replicate LC-MS/MS analyses, these metrics displayed consistent, reasonable responses to controlled perturbations. The metrics typically displayed variations less than 10% and thus can reveal even subtle differences in performance of system components. Analyses of data from interlab. studies conducted under a common std. operating procedure identified outlier data and provided clues to specific causes. Moreover, interlab. variation reflected by the metrics indicates which system components vary the most between labs. Application of these metrics enables rational, quant. quality assessment for proteomics and other LC-MS/MS anal. applications.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjtFWhsb8%253D&md5=7c79551182bac7134467c423509f72ce
8
Paulovich, A. G. Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance Mol. Cell. Proteomics 2010, 9, 242– 254 DOI: 10.1074/mcp.M900222-MCP200
[Crossref], [PubMed], [CAS], Google Scholar
8
Interlaboratory study characterizing a yeast performance standard for benchmarking LC-MS platform performance
Paulovich, Amanda G.; Billheimer, Dean; Ham, Amy-Joan L.; Vega-Montoto, Lorenzo; Rudnick, Paul A.; Tabb, David L.; Wang, Pei; Blackman, Ronald K.; Bunk, David M.; Cardasis, Helene L.; Clauser, Karl R.; Kinsinger, Christopher R.; Schilling, Birgit; Tegeler, Tony J.; Variyath, Asokan Mulayath; Wang, Mu; Whiteaker, Jeffrey R.; Zimmerman, Lisa J.; Fenyo, David; Carr, Steven A.; Fisher, Susan J.; Gibson, Bradford W.; Mesri, Mehdi; Neubert, Thomas A.; Regnier, Fred E.; Rodriguez, Henry; Spiegelman, Cliff; Stein, Stephen E.; Tempst, Paul; Liebler, Daniel C.
Molecular and Cellular Proteomics (2010), 9 (2), 242-254CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
Optimal performance of LC-MS/MS platforms is crit. to generating high quality proteomics data. Although individual labs. have developed quality control samples, there is no widely available performance std. of biol. complexity (and assocd. ref. data sets) for benchmarking of platform performance for anal. of complex biol. proteomes across different labs. in the community. Individual prepns. of the yeast Saccharomyces cerevisiae proteome have been used extensively by labs. in the proteomics community to characterize LC-MS platform performance. The yeast proteome is uniquely attractive as a performance std. because it is the most extensively characterized complex biol. proteome and the only one assocd. with several large scale studies estg. the abundance of all detectable proteins. In this study, we describe a std. operating protocol for large scale prodn. of the yeast performance std. and offer aliquots to the community through the National Institute of Stds. and Technol. where the yeast proteome is under development as a certified ref. material to meet the long term needs of the community. Using a series of metrics that characterize LC-MS performance, we provide a ref. data set demonstrating typical performance of commonly used ion trap instrument platforms in expert labs.; the results provide a basis for labs. to benchmark their own performance, to improve upon current methods, and to evaluate new technologies. Addnl., we demonstrate how the yeast ref., spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concn. in a complex matrix, thereby providing a metric to evaluate and minimize preanal. and anal. variation in comparative proteomics expts.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjtFWhsbw%253D&md5=2057d48a1a5edd1c7ba8c21e29dd6065
9
Geer, L. Y.; Markey, S. P.; Kowalak, J. A.; Wagner, L.; Xu, M.; Maynard, D. M.; Yang, X.; Shi, W.; Bryant, S. H. Open Mass Spectrometry Search Algorithm J. Proteome Res. 2004, 3, 958– 964 DOI: 10.1021/pr0499491
[ACS Full Text ], [CAS], Google Scholar
9
Open Mass Spectrometry Search Algorithm
Geer, Lewis Y.; Markey, Sanford P.; Kowalak, Jeffrey A.; Wagner, Lukas; Xu, Ming; Maynard, Dawn M.; Yang, Xiaoyu; Shi, Wenyao; Bryant, Stephen H.
Journal of Proteome Research (2004), 3 (5), 958-964CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
Large nos. of MS/MS peptide spectra generated in proteomics expts. require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm (OMSSA), specificity is calcd. by a classic probability score using an explicit model for matching exptl. spectra to sequences. At default thresholds, OMSSA matches more spectra from a std. protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXltl2lur8%253D&md5=811b62e2caae44f6e4f83cbbff48b7b6
10
Lam, H.; Deutsch, E.; Eddes, J.; Eng, J.; King, N.; Yang, S.; Roth, J.; Kilpatrick, L.; Neta, P.; Stein, S. SpectraST: An open-source MS/MS spectramatching library search tool for targeted proteomics, 54th ASMS Conference on Mass Spectrometry, Seattle, Washington, May 28–June 1, 2006.
Google Scholar
There is no corresponding record for this reference.
11
Ma, Z.-Q.; Polzin, K. O.; Dasari, S.; Chambers, M. C.; Schilling, B.; Gibson, B. W.; Tran, B. Q.; Vega-Montoto, L.; Liebler, D. C.; Tabb, D. L. QuaMeter: multivendor performance metrics for LC–MS/MS proteomics instrumentation Anal. Chem. 2012, 84, 5845– 5850 DOI: 10.1021/ac300629p
[ACS Full Text ], [CAS], Google Scholar
11
QuaMeter: Multivendor Performance Metrics for LC-MS/MS Proteomics Instrumentation
Ma, Ze-Qiang; Polzin, Kenneth O.; Dasari, Surendra; Chambers, Matthew C.; Schilling, Birgit; Gibson, Bradford W.; Tran, Bao Q.; Vega-Montoto, Lorenzo; Liebler, Daniel C.; Tabb, David L.
Analytical Chemistry (Washington, DC, United States) (2012), 84 (14), 5845-5850CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
LC-MS/MS-based proteomics studies rely on stable anal. system performance that can be evaluated by objective criteria. The National Institute of Stds. and Technol. (NIST) introduced the MSQC software to compute diverse metrics from exptl. LC-MS/MS data, enabling quality anal. and quality control (QA/QC) of proteomics instrumentation. In practice, however, several attributes of the MSQC software prevent its use for routine instrument monitoring. Here, we present QuaMeter, an open-source tool that improves MSQC in several aspects. QuaMeter can directly read raw data from instruments manufd. by different vendors. The software can work with a wide variety of peptide identification software for improved reliability and flexibility. Finally, QC metrics implemented in QuaMeter are rigorously defined and tested. The source code and binary versions of QuaMeter are available under Apache 2.0 License at http://fenchurch.mc.vanderbilt.edu.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XosFelsr0%253D&md5=99c35883d058b2e2a892ce9f06a4a370
12
Taylor, R. M.; Dance, J.; Taylor, R. J.; Prince, J. T. Metriculator: quality assessment for mass spectrometry-based proteomics Bioinformatics 2013, 29, 2948– 2949 DOI: 10.1093/bioinformatics/btt510
[Crossref], [PubMed], [CAS], Google Scholar
12
Metriculator: quality assessment for mass spectrometry-based proteomics
Taylor, Ryan M.; Dance, Jamison; Taylor, Russ J.; Prince, John T.
Bioinformatics (2013), 29 (22), 2948-2949CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
Quality control in mass spectrometry-based proteomics remains subjective, labor-intensive and inconsistent between labs. We introduce Metriculator, a software designed to facilitate long-term storage of extensive performance metrics as introduced by NIST in 2010. Metriculator features a web interface that generates interactive comparison plots for contextual understanding of metric values and an automated metric generation toolkit. The comparison plots are designed for at-a-glance detn. of outliers and trends in the datasets, together with relevant statistical comparisons. Easy-to-use quant. comparisons and a framework for integration plugins will encourage a culture of quality assurance within the proteomics community.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhslWnsbzO&md5=0bce88fda1411349f7a3df756a9a7d9b
13
Walzer, M. qcML: An Exchange Format for Quality Control Metrics from Mass Spectrometry Experiments Mol. Cell. Proteomics 2014, 13, 1905– 1913 DOI: 10.1074/mcp.M113.035907
[Crossref], [PubMed], [CAS], Google Scholar
13
qcML: An Exchange Format for Quality Control Metrics from Mass Spectrometry Experiments
Walzer, Mathias; Pernas, Lucia Espona; Nasso, Sara; Bittremieux, Wout; Nahnsen, Sven; Kelchtermans, Pieter; Pichler, Peter; van den Toorn, Henk W. P.; Staes, An; Vandenbussche, Jonathan; Mazanek, Michael; Taus, Thomas; Scheltema, Richard A.; Kelstrup, Christian D.; Gatto, Laurent; van Breukelen, Bas; Aiche, Stephan; Valkenborg, Dirk; Laukens, Kris; Lilley, Kathryn S.; Olsen, Jesper V.; Heck, Albert J. R.; Mechtler, Karl; Aebersold, Ruedi; Gevaert, Kris; Vizcaino, Juan Antonio; Hermjakob, Henning; Kohlbacher, Oliver; Martens, Lennart
Molecular & Cellular Proteomics (2014), 13 (8), 1905-1913CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to ext. these from the instrumental raw data. What has been missing, however, is a std. data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based std. that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML stds. from the HUPO-PSI (Proteomics Stds. Initiative). In addn. to the XML format, we also provide tools for the calcn. of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent anal. possibilities. All information about qcML is available at http://code.google.com/p/qcml.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXht1CksL%252FF&md5=b87d256f986786be3f1b6c343410ddfa
14
Pichler, P.; Mazanek, M.; Dusberger, F.; Weilnböck, L.; Huber, C. G.; Stingl, C.; Luider, T. M.; Straube, W. L.; Köcher, T.; Mechtler, K. SIMPATIQCO: a server-based software suite which facilitates monitoring the time course of LC–MS performance metrics on Orbitrap instruments J. Proteome Res. 2012, 11, 5540– 5547 DOI: 10.1021/pr300163u
[ACS Full Text ], [CAS], Google Scholar
14
SIMPATIQCO: A Server-Based Software Suite Which Facilitates Monitoring the Time Course of LC-MS Performance Metrics on Orbitrap Instruments
Pichler, Peter; Mazanek, Michael; Dusberger, Frederico; Weilnboeck, Lisa; Huber, Christian G.; Stingl, Christoph; Luider, Theo M.; Straube, Werner L.; Koecher, Thomas; Mechtler, Karl
Journal of Proteome Research (2012), 11 (11), 5540-5547CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
While the performance of liq. chromatog. (LC) and mass spectrometry (MS) instrumentation continues to increase, applications such as analyses of complete or near-complete proteomes and quant. studies require const. and optimal system performance. For this reason, research labs. and core facilities alike are recommended to implement quality control (QC) measures as part of their routine workflows. Many labs. perform sporadic quality control checks. However, successive and systematic longitudinal monitoring of system performance would be facilitated by dedicated automatic or semiautomatic software solns. that aid an effortless anal. and display of QC metrics over time. We present the software package SIMPATIQCO (SIMPle AuTomatIc Quality COntrol) designed for evaluation of data from LTQ Orbitrap, Q-Exactive, LTQ FT, and LTQ instruments. A centralized SIMPATIQCO server can process QC data from multiple instruments. The software calcs. QC metrics supervising every step of data acquisition from LC and electrospray to MS. For each QC metric the software learns the range indicating adequate system performance from the uploaded data using robust statistics. Results are stored in a database and can be displayed in a comfortable manner from any computer in the lab. via a web browser. QC data can be monitored for individual LC runs as well as plotted over time. SIMPATIQCO thus assists the longitudinal monitoring of important QC metrics such as peptide elution times, peak widths, intensities, total ion current (TIC) as well as sensitivity, and overall LC-MS system performance; in this way the software also helps identify potential problems. The SIMPATIQCO software package is available free of charge.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhsFequrvJ&md5=f3979ab704ec9ad64f2f7ec7c6afb7f0
15
Dorfer, V.; Pichler, P.; Stranzl, T.; Stadlmann, J.; Taus, T.; Winkler, S.; Mechtler, K. MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra J. Proteome Res. 2014, 13, 3679– 3684 DOI: 10.1021/pr500202e
[ACS Full Text ], [CAS], Google Scholar
15
MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra
Dorfer, Viktoria; Pichler, Peter; Stranzl, Thomas; Stadlmann, Johannes; Taus, Thomas; Winkler, Stephan; Mechtler, Karl
Journal of Proteome Research (2014), 13 (8), 3679-3684CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
Today's highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the anal. of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide identification algorithm. While most widely used search engines were developed when high-resoln. mass spectrometry data were not readily available for fragment ion masses, the authors have designed a scoring algorithm particularly suitable for high mass accuracy. The authors' algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examd. high mass accuracy data sets, with excellent overlap and identical peptide sequence identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at http://ms.imp.ac.at/goto=msamanda, is provided free of charge both as standalone version for integration into custom workflows and as a plugin for the Proteome Discoverer platform.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXptl2nsL4%253D&md5=3d3100ed4c0d6c667f1b5fa2c6be18eb
16
Amidan, B. G.; Orton, D. J.; LaMarche, B. L.; Monroe, M. E.; Moore, R. J.; Venzin, A. M.; Smith, R. D.; Sego, L. H.; Tardiff, M. F.; Payne, S. H. Signatures for Mass Spectrometry Data Quality J. Proteome Res. 2014, 13, 2215– 2222 DOI: 10.1021/pr401143e
[ACS Full Text ], [CAS], Google Scholar
16
Signatures for Mass Spectrometry Data Quality
Amidan, Brett G.; Orton, Daniel J.; LaMarche, Brian L.; Monroe, Matthew E.; Moore, Ronald J.; Venzin, Alexander M.; Smith, Richard D.; Sego, Landon H.; Tardiff, Mark F.; Payne, Samuel H.
Journal of Proteome Research (2014), 13 (4), 2215-2222CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
Ensuring data quality and proper instrument functionality is a prerequisite for scientific investigation. Manual quality assurance is time-consuming and subjective. Metrics for describing liq. chromatog. mass spectrometry (LC-MS) data have been developed; however, the wide variety of LC-MS instruments and configurations precludes applying a simple cutoff. Using 1150 manually classified quality control (QC) data sets, we trained logistic regression classification models to predict whether a data set is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the trade-off between false pos. and false neg. errors. The classifier models detected bad data sets with high sensitivity while maintaining high specificity. Moreover, the composite classifier was dramatically more specific than single metrics. Finally, we evaluated the performance of the classifier on a sep. validation set where it performed comparably to the results for the testing/training data sets. By presenting the methods and software used to create the classifier, other groups can create a classifier for their specific QC regimen, which is highly variable lab-to-lab. In total, this manuscript presents 3400 LC-MS data sets for the same QC sample (whole cell lysate of Shewanella oneidensis), deposited to the ProteomeXchange with identifiers PXD000320-PXD000324.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXjslCqsr0%253D&md5=4d540fad07265b1361aef31ed612510f
17
R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014.
Google Scholar
There is no corresponding record for this reference.
18
Gatto, L.; Breckels, L. M.; Naake, T.; Gibb, S. Visualization of proteomics data using R and Bioconductor Proteomics 2015, 15, 1375– 1389 DOI: 10.1002/pmic.201400392
[Crossref], [PubMed], [CAS], Google Scholar
18
Visualization of proteomics data using R and Bioconductor
Gatto, Laurent; Breckels, Lisa M.; Naake, Thomas; Gibb, Sebastian
Proteomics (2015), 15 (8), 1375-1389CODEN: PROTC7; ISSN:1615-9853. (Wiley-VCH Verlag GmbH & Co. KGaA)
Data visualization plays a key role in high-throughput biol. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXmtVarurw%253D&md5=684af78aac1287607d5506be64106558
19
Cox, J.; Neuhauser, N.; Michalski, A.; Scheltema, R. A.; Olsen, J. V.; Mann, M. Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment J. Proteome Res. 2011, 10, 1794– 1805 DOI: 10.1021/pr101065j
[ACS Full Text ], [CAS], Google Scholar
19
Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment
Cox, Juergen; Neuhauser, Nadin; Michalski, Annette; Scheltema, Richard A.; Olsen, Jesper V.; Mann, Matthias
Journal of Proteome Research (2011), 10 (4), 1794-1805CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
A key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Here the authors describe Andromeda, a novel peptide search engine using a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used com. search engine, as judged by sensitivity and specificity anal. based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform and both are freely available at www.maxquant.org. The combination enables anal. of large data sets in a simple anal. workflow on a desktop computer. For searching individual spectra Andromeda is also accessible via a web server. The authors demonstrate the flexibility of the system by implementing the capability to identify cofragmented peptides, significantly improving the total no. of identified peptides.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXit1Gis74%253D&md5=7587da6364fe0ff020e3dbf1d80bb22f
20
Cox, J.; Hein, M. Y.; Luber, C. A.; Paron, I.; Nagaraj, N.; Mann, M. Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ Mol. Cell. Proteomics 2014, 13, 2513– 2526 DOI: 10.1074/mcp.M113.031591
[Crossref], [PubMed], [CAS], Google Scholar
20
Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ
Cox, Juergen; Hein, Marco Y.; Luber, Christian A.; Paron, Igor; Nagaraj, Nagarjuna; Mann, Matthias
Molecular & Cellular Proteomics (2014), 13 (9), 2513-2526CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity detn. and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein sepn. prior to LC-MS anal. Protein abundance profiles are assembled using the max. possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technol. that is readily applicable to many biol. questions; it is compatible with std. statistical anal. workflows, and it has been validated in many and diverse biol. projects. Our algorithms can handle very large expts. of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVynurrI&md5=f3f1c7dc8fbf729c568446968b89f37c
21
Geiger, T.; Wehner, A.; Schaab, C.; Cox, J.; Mann, M. Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins Mol. Cell. Proteomics 2012, 11, M111.014050 DOI: 10.1074/mcp.M111.014050
[Crossref], [PubMed], Google Scholar
There is no corresponding record for this reference.
22
Chiva, C.; Ortega, M.; Sabidó, E. Influence of the Digestion Technique, Protease, and Missed Cleavage Peptides in Protein Quantitation J. Proteome Res. 2014, 13, 3979– 86 DOI: 10.1021/pr500294d
[ACS Full Text ], [CAS], Google Scholar
22
Influence of the Digestion Technique, Protease, and Missed Cleavage Peptides in Protein Quantitation
Chiva, Cristina; Ortega, Mireia; Sabido, Eduard
Journal of Proteome Research (2014), 13 (9), 3979-3986CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
Quant. detn. of abs. and relative protein amts. is an essential requirement for most current bottom-up proteomics applications, but protein quantitation ests. are affected by several sources of variability such as sample prepn., mass spectrometric acquisition, and data anal. Among them, sample digestion has attracted much attention from the proteomics community, as protein quantitation by bottom-up proteomics relies on the efficiency and reproducibility of protein enzymic digestion, with the presence of missed cleavages, nonspecific cleavages, or even the use of different proteases having been postulated as important sources of variation in protein quantitation. Here the authors evaluated both in-soln. and filter-aided digestion protocols and assessed their influence in the estn. of protein abundances using five E. coli mixts. with known amts. of spiked proteins. Replicates of trypsin specificity digestion protocols are highly reproducible in terms of peptide quantitation, with digestion technique and the chosen proteolytic enzyme being the major sources of variability in peptide quantitation. Finally, the authors also evaluated the result of including peptides with missed cleavages in protein quantitation and obsd. no significant differences in precision, accuracy, specificity, and sensitivity compared using fully tryptic peptides.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtVyit7rF&md5=4203c7755a3d98f5f0d905647cc78341
23
Licker, V.; Turck, N.; Kövari, E.; Burkhardt, K.; Côte, M.; Surini-Demiri, M.; Lobrinus, J. A.; Sanchez, J.-C.; Burkhard, P. R. Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson’s disease pathogenesis Proteomics 2014, 14, 784– 794 DOI: 10.1002/pmic.201300342
[Crossref], [PubMed], [CAS], Google Scholar
23
Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson's disease pathogenesis
Licker, Virginie; Turck, Natacha; Koevari, Enikoe; Burkhardt, Karim; Cote, Melanie; Surini-Demiri, Maria; Lobrinus, Johannes A.; Sanchez, Jean-Charles; Burkhard, Pierre R.
Proteomics (2014), 14 (6), 784-794CODEN: PROTC7; ISSN:1615-9853. (Wiley-VCH Verlag GmbH & Co. KGaA)
Parkinson's disease (PD) pathol. spreads throughout the brain following a region-specific process predominantly affecting the substantia nigra (SN) pars compacta. SN exhibits a progressive loss of dopaminergic neurons responsible for the major cardinal motor symptoms, along with the occurrence of Lewy bodies in the surviving neurons. To gain new insights into the underlying pathogenic mechanisms in PD, we studied postmortem nigral tissues dissected from pathol. confirmed PD cases (n = 5) and neurol. intact controls (n = 8). Using a high-throughput shotgun proteomic strategy, we simultaneously identified 1795 proteins with concomitant quant. data. To date, this represents the most extensive catalog of nigral proteins. Of them, 204 proteins displayed significant expression level changes in PD patients vs. controls. These were involved in novel or known pathogenic processes including mitochondrial dysfunction, oxidative stress, or cytoskeleton impairment. We further characterized four candidates that might be relevant to PD pathogenesis. We confirmed the differential expression of ferritin-L and seipin by Western blot and demonstrated the neuronal localization of gamma glutamyl hydrolase and nebulette by immunohistochem. Our preliminary findings suggest a role for nebulette overexpression in PD neurodegeneration, through mechanisms that may involve cytoskeleton dynamics disruption. All MS data have been deposited in the ProteomeXchange with identifier PXD000427. (http://proteomecentral.proteomexchange.org/dataset/PXD000427).
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXisFOrtbc%253D&md5=9631925921afa8b7e50351093bd21cb0
24
Vizcaíno, J. A. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013 Nucleic Acids Res. 2013, 41, D1063– 9 DOI: 10.1093/nar/gks1262
[Crossref], [PubMed], [CAS], Google Scholar
24
The Proteomics Identifications (PRIDE) database and associated tools: status in 2013
Vizcaino, Juan Antonio; Cote, Richard G.; Csordas, Attila; Dianes, Jose A.; Fabregat, Antonio; Foster, Joseph M.; Griss, Johannes; Alpi, Emanuele; Birim, Melih; Contell, Javier; O'Kelly, Gavin; Schoenegger, Andreas; Ovelleiro, David; Perez-Riverol, Yasset; Reisinger, Florian; Rios, Daniel; Wang, Rui; Hermjakob, Henning
Nucleic Acids Research (2013), 41 (D1), D1063-D1069CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
The PRoteomics IDEntifications (PRIDE, http://www.ebi.ac.uk/pride) database at the European Bioinformatics Institute is one of the most prominent data repositories of mass spectrometry (MS)-based proteomics data. Here, we summarize recent developments in the PRIDE database and related tools. First, we provide up-to-date statistics in data content, splitting the figures by groups of organisms and species, including peptide and protein identifications, and post-translational modifications. We then describe the tools that are part of the PRIDE submission pipeline, esp. the recently developed PRIDE Converter 2 (new submission tool) and PRIDE Inspector (visualization and anal. tool). We also give an update about the integration of PRIDE with other MS proteomics resources in the context of the ProteomeXchange consortium. Finally, we briefly review the quality control efforts that are ongoing at present and outline our future plans.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhvV2ksb3P&md5=e49aba656ba6d88418202d9e54f67db0
25
Drexler, H. G.; Uphoff, C. C. Mycoplasma contamination of cell cultures: Incidence, sources, effects, detection, elimination, prevention Cytotechnology 2002, 39, 75– 90 DOI: 10.1023/A:1022913015916
[Crossref], [PubMed], [CAS], Google Scholar
25
Mycoplasma contamination of cell cultures: Incidence, sources, effects, detection, elimination, prevention
Drexler Hans G; Uphoff Cord C
Cytotechnology (2002), 39 (2), 75-90 ISSN:0920-9069.
The contamination of cell cultures by mycoplasmas remains a major problem in cell culture. Mycoplasmas can produce a virtually unlimited variety of effects in the cultures they infect. These organisms are resistant to most antibiotics commonly employed in cell cultures. Here we provide a concise overview of the current knowledge on: (1) the incidence and sources of mycoplasma contamination in cell cultures, the mycoplasma species most commonly detected in cell cultures, and the effects of mycoplasmas on the function and activities of infected cell cultures; (2) the various techniques available for the detection of mycoplasmas with particular emphasis on the most reliable detection methods; (3) the various methods available for the elimination of mycoplasmas highlighting antibiotic treatment; and (4) the recommended procedures and working protocols for the detection, elimination and prevention of mycoplasma contamination. The availability of accurate, sensitive and reliable detection methods and the application of robust and successful elimination methods provide powerful means for overcoming the problem of mycoplasma contamination in cell cultures.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cjjtlKhtQ%253D%253D&md5=5b2d4bfa3f147408814d53d41b35c2ce
26
Noble, W. S. Mass spectrometrists should search only for peptides they care about Nat. Methods 2015, 12, 605– 608 DOI: 10.1038/nmeth.3450
[Crossref], [PubMed], [CAS], Google Scholar
26
Mass spectrometrists should search only for peptides they care about
Noble, William Stafford
Nature Methods (2015), 12 (7), 605-608CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
Anal. pipelines that assign peptides to shotgun proteomics mass spectra often discard identified spectra deemed irrelevant to the scientific hypothesis being tested. To improve statistical power, I propose that researchers remove irrelevant peptides from the database prior to searching rather than assigning these peptides to spectra and then discarding the matches.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXht1ygsb7M&md5=f0a7d9fba72f4d26f79fd4ef0cf58038
27
Suzek, B. E.; Wang, Y.; Huang, H.; McGarvey, P. B.; Wu, C. H. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches Bioinformatics 2015, 31, 926– 932 DOI: 10.1093/bioinformatics/btu739
[Crossref], [PubMed], [CAS], Google Scholar
27
UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches
Suzek Baris E; Wu Cathy H; Wang Yuqi; Huang Hongzhan; McGarvey Peter B
Bioinformatics (Oxford, England) (2015), 31 (6), 926-32 ISSN:.
MOTIVATION: UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. RESULTS: Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (∼7 times shorter hit list before expansion), faster (∼6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation.
>> More from SciFinder ^®
https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2M3otlehtQ%253D%253D&md5=bdc2c0e47870945ae712f3932089f4e5

Cited By

This article is cited by 44 publications.

Katrin Brenig, Leonie Grube, Markus Schwarzländer, Karl Köhrer, Kai Stühler, Gereon Poschmann. The Proteomic Landscape of Cysteine Oxidation That Underpins Retinoic Acid-Induced Neuronal Differentiation. Journal of Proteome Research 2020, 19 (5) , 1923-1940. https://doi.org/10.1021/acs.jproteome.9b00752
Hua Ding, Hossein Fazelinia, Lynn A. Spruce, Dana A. Weiss, Stephen A. Zderic, Steven H. Seeholzer. Urine Proteomics: Evaluation of Different Sample Preparation Workflows for Quantitative, Reproducible, and Improved Depth of Analysis. Journal of Proteome Research 2020, 19 (4) , 1857-1862. https://doi.org/10.1021/acs.jproteome.9b00772
Amanda R. M. Silva, Marcos T. K. Toyoshima, Marisa Passarelli, Paolo Di Mascio, Graziella E. Ronsein. Comparing Data-Independent Acquisition and Parallel Reaction Monitoring in Their Abilities To Differentiate High-Density Lipoprotein Subclasses. Journal of Proteome Research 2020, 19 (1) , 248-259. https://doi.org/10.1021/acs.jproteome.9b00511
Matthew Y. Lim, João A. Paulo, Steven P. Gygi. Evaluating False Transfer Rates from the Match-between-Runs Algorithm with a Two-Proteome Model. Journal of Proteome Research 2019, 18 (11) , 4020-4026. https://doi.org/10.1021/acs.jproteome.9b00492
R. Gray Huffman, Albert Chen, Harrison Specht, Nikolai Slavov. DO-MS: Data-Driven Optimization of Mass Spectrometry Methods. Journal of Proteome Research 2019, 18 (6) , 2493-2500. https://doi.org/10.1021/acs.jproteome.9b00039
Kevin A. Kovalchik, Sophie Moggridge, David D. Y. Chen, Gregg B. Morin, Christopher S. Hughes. Parsing and Quantification of Raw Orbitrap Mass Spectrometer Data Using RawQuant. Journal of Proteome Research 2018, 17 (6) , 2237-2247. https://doi.org/10.1021/acs.jproteome.8b00072
Salvador Martínez-Bartolomé, J. Alberto Medina-Aunon, Miguel Ángel López-García, Carmen González-Tejedo, Gorka Prieto, Rosana Navajas, Emilio Salazar-Donate, Carolina Fernández-Costa, John R. Yates, III, Juan Pablo Albar. PACOM: A Versatile Tool for Integrating, Filtering, Visualizing, and Comparing Multiple Large Mass Spectrometry Proteomics Data Sets. Journal of Proteome Research 2018, 17 (4) , 1547-1558. https://doi.org/10.1021/acs.jproteome.7b00858
Ann-Sophie Schott, Jürgen Behr, Andreas J. Geißler, Bernhard Kuster, Hannes Hahne, and Rudi F. Vogel . Quantitative Proteomics for the Comprehensive Analysis of Stress Responses of Lactobacillus paracasei subsp. paracasei F19. Journal of Proteome Research 2017, 16 (10) , 3816-3829. https://doi.org/10.1021/acs.jproteome.7b00474
Oliver Kohlbacher , Olga Vitek , Susan T. Weintraub . Challenges in Large-Scale Computational Mass Spectrometry and Multiomics. Journal of Proteome Research 2016, 15 (3) , 681-682. https://doi.org/10.1021/acs.jproteome.6b00067
Ling Xiong, Sjef Boeren, Jacques Vervoort, Kasper Hettinga. Effect of milk serum proteins on aggregation, bacteriostatic activity and digestion of lactoferrin after heat treatment. Food Chemistry 2021, 337 , 127973. https://doi.org/10.1016/j.foodchem.2020.127973
Valentin Roustan, Julia Hilscher, Marieluise Weidinger, Siegfried Reipert, Azita Shabrangy, Claudia Gebert, Bianca Dietrich, Georgi Dermendjiev, Madeleine Schnurer, Pierre-Jean Roustan, Eva Stoger, Verena Ibl. Protein sorting into protein bodies during barley endosperm development is putatively regulated by cytoskeleton members, MVBs and the HvSNF7s. Scientific Reports 2020, 10 (1) https://doi.org/10.1038/s41598-020-58740-x
Ioannis Kostopoulos, Janneke Elzinga, Noora Ottman, Jay T. Klievink, Bernadet Blijenberg, Steven Aalvink, Sjef Boeren, Marko Mank, Jan Knol, Willem M. de Vos, Clara Belzer. Akkermansia muciniphila uses human milk oligosaccharides to thrive in the early life conditions in vitro. Scientific Reports 2020, 10 (1) https://doi.org/10.1038/s41598-020-71113-8
Irene Sánchez-Andrea, Iame Alves Guedes, Bastian Hornung, Sjef Boeren, Christopher E. Lawson, Diana Z. Sousa, Arren Bar-Even, Nico J. Claassens, Alfons J. M. Stams. The reductive glycine pathway allows autotrophic growth of Desulfovibrio desulfuricans. Nature Communications 2020, 11 (1) https://doi.org/10.1038/s41467-020-18906-7
Yuhui Dou, Svetlana Kalmykova, Maria Pashkova, Mehrnoosh Oghbaie, Hua Jiang, Kelly R Molloy, Brian T Chait, Michael P Rout, David Fenyö, Torben Heick Jensen, Ilya Altukhov, John LaCava. Affinity proteomic dissection of the human nuclear cap-binding complex interactome. Nucleic Acids Research 2020, 48 (18) , 10456-10469. https://doi.org/10.1093/nar/gkaa743
Siyuan Zhang, Zixuan Zhao, Wenjing Duan, Zhaoxin li, Zhuhui Nan, Hanzhi Du, Mengchang Wang, Juan Yang, Chen Huang. The Influence of Blood Collection Tubes in Biomarkers’ Screening by Mass Spectrometry. PROTEOMICS – Clinical Applications 2020, 14 (5) , 1900113. https://doi.org/10.1002/prca.201900113
Thierry Schmidlin, Maarten Altelaar. Effects of electron-transfer/higher-energy collisional dissociation (EThcD) on phosphopeptide analysis by data-independent acquisition. International Journal of Mass Spectrometry 2020, 452 , 116336. https://doi.org/10.1016/j.ijms.2020.116336
Sky Dominguez, Guadalupe Rodriguez, Hossein Fazelinia, Hua Ding, Lynn Spruce, Steven H. Seeholzer, Hongxin Dong. Sex Differences in the Phosphoproteomic Profiles of APP/PS1 Mice after Chronic Unpredictable Mild Stress. Journal of Alzheimer's Disease 2020, 74 (4) , 1131-1142. https://doi.org/10.3233/JAD-191009
Camille Lombard-Banek, John E. Schiel. Mass Spectrometry Advances and Perspectives for the Characterization of Emerging Adoptive Cell Therapies. Molecules 2020, 25 (6) , 1396. https://doi.org/10.3390/molecules25061396
Mathias Walzer, Juan Antonio Vizcaíno. Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics. 2020,,, 345-371. https://doi.org/10.1007/978-1-4939-9744-2_15
Nikolaus Berndt, Antje Egners, Guido Mastrobuoni, Olga Vvedenskaya, Athanassios Fragoulis, Aurélien Dugourd, Sascha Bulik, Matthias Pietzke, Chris Bielow, Rob van Gassel, Steven W. Olde Damink, Merve Erdem, Julio Saez-Rodriguez, Hermann-Georg Holzhütter, Stefan Kempa, Thorsten Cramer. Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer. British Journal of Cancer 2020, 122 (2) , 233-244. https://doi.org/10.1038/s41416-019-0659-3
Jingyu Wu, Zhifang Hao, Chen Ma, Pengfei Li, Liuyi Dang, Shisheng Sun. Comparative proteogenomics profiling of non-small and small lung carcinoma cell lines using mass spectrometry. PeerJ 2020, 8 , e8779. https://doi.org/10.7717/peerj.8779
Xue-Yan Li, Li-Li Liu, Min Zhang, Li-Fang Zhang, Xiao-Yang Wang, Mi Wang, Ke-Yu Zhang, Ying-Chun Liu, Chun-Mei Wang, Fei-Qun Xue, Chen-Zhong Fei. Proteomic analysis of the second-generation merozoites of Eimeria tenella under nitromezuril and ethanamizuril stress. Parasites & Vectors 2019, 12 (1) https://doi.org/10.1186/s13071-019-3841-9
Zhe Zeng, Eddy J. Smid, Sjef Boeren, Richard A. Notebaart, Tjakko Abee. Bacterial Microcompartment-Dependent 1,2-Propanediol Utilization Stimulates Anaerobic Growth of Listeria monocytogenes EGDe. Frontiers in Microbiology 2019, 10 https://doi.org/10.3389/fmicb.2019.02660
Diego Mora, Rossella Filardi, Stefania Arioli, Sjef Boeren, Steven Aalvink, Willem M. Vos. Development of omics‐based protocols for the microbiological characterization of multi‐strain formulations marketed as probiotics: the case of VSL#3. Microbial Biotechnology 2019, 12 (6) , 1371-1386. https://doi.org/10.1111/1751-7915.13476
Jian Cui, Qiang Chen, Xiaorui Dong, Kai Shang, Xin Qi, Hao Cui. A matching algorithm with isotope distribution pattern in LC-MS based on support vector machine (SVM) learning model. RSC Advances 2019, 9 (48) , 27874-27882. https://doi.org/10.1039/C9RA03789F
Fiona McDonald, Christina Holmes, Mavis Jones, Janice E. Graham. How Do Postgenomic Innovations Emerge? Building Legitimacy by Proteomics Standards and Informing the Next-Generation Technology Policy. OMICS: A Journal of Integrative Biology 2019, 23 (8) , 406-415. https://doi.org/10.1089/omi.2019.0053
Taiyun Kim, Irene Rui Chen, Benjamin L. Parker, Sean J. Humphrey, Ben Crossett, Stuart J. Cordwell, Pengyi Yang, Jean Yee Hwa Yang. QCMAP: An Interactive Web‐Tool for Performance Diagnosis and Prediction of LC‐MS Systems. PROTEOMICS 2019, 37 , 1900068. https://doi.org/10.1002/pmic.201900068
Simon Roehrer, Verena Stork, Christina Ludwig, Mirjana Minceva, Jürgen Behr, . Analyzing bioactive effects of the minor hop compound xanthohumol C on human breast cancer cells using quantitative proteomics. PLOS ONE 2019, 14 (3) , e0213469. https://doi.org/10.1371/journal.pone.0213469
Mark L. Sowers, Jessica Di Re, Paul A. Wadsworth, Alexander S. Shavkunov, Cheryl Lichti, Kangling Zhang, Fernanda Laezza. Sex-Specific Proteomic Changes Induced by Genetic Deletion of Fibroblast Growth Factor 14 (FGF14), a Regulator of Neuronal Ion Channels. Proteomes 2019, 7 (1) , 5. https://doi.org/10.3390/proteomes7010005
Anna P. Florentino, Inês A. C. Pereira, Sjef Boeren, Michael van den Born, Alfons J. M. Stams, Irene Sánchez-Andrea. Insight into the sulfur metabolism of Desulfurella amilsii by differential proteomics. Environmental Microbiology 2019, 21 (1) , 209-225. https://doi.org/10.1111/1462-2920.14442
Diana Z. Sousa, Michael Visser, Antonie H. van Gelder, Sjef Boeren, Mervin M. Pieterse, Martijn W. H. Pinkse, Peter D. E. M. Verhaert, Carsten Vogt, Steffi Franke, Steffen Kümmel, Alfons J. M. Stams. The deep-subsurface sulfate reducer Desulfotomaculum kuznetsovii employs two methanol-degrading pathways. Nature Communications 2018, 9 (1) https://doi.org/10.1038/s41467-017-02518-9
Ying Zhu, Paul D. Piehowski, Rui Zhao, Jing Chen, Yufeng Shen, Ronald J. Moore, Anil K. Shukla, Vladislav A. Petyuk, Martha Campbell-Thompson, Clayton E. Mathews, Richard D. Smith, Wei-Jun Qian, Ryan T. Kelly. Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells. Nature Communications 2018, 9 (1) https://doi.org/10.1038/s41467-018-03367-w
Raphaela Fritsche-Guenther, Christin Zasada, Guido Mastrobuoni, Nadine Royla, Roman Rainer, Florian Roßner, Matthias Pietzke, Edda Klipp, Christine Sers, Stefan Kempa. Alterations of mTOR signaling impact metabolic stress resistance in colorectal carcinomas with BRAF and KRAS mutations. Scientific Reports 2018, 8 (1) https://doi.org/10.1038/s41598-018-27394-1
Valentin Roustan, Pierre-Jean Roustan, Marieluise Weidinger, Siegfried Reipert, Eszter Kapusi, Azita Shabrangy, Eva Stoger, Wolfram Weckwerth, Verena Ibl. Microscopic and Proteomic Analysis of Dissected Developing Barley Endosperm Layers Reveals the Starchy Endosperm as Prominent Storage Tissue for ER-Derived Hordeins Alongside the Accumulation of Barley Protein Disulfide Isomerase (HvPDIL1-1). Frontiers in Plant Science 2018, 9 https://doi.org/10.3389/fpls.2018.01248
Dong-Suk Kim, Poojya Anantharam, Andrea Hoffmann, Mitchell L. Meade, Nadja Grobe, Jeffery M. Gearhart, Elizabeth M. Whitley, Belinda Mahama, Wilson K. Rumbeiha. Broad spectrum proteomics analysis of the inferior colliculus following acute hydrogen sulfide exposure. Toxicology and Applied Pharmacology 2018, 355 , 28-42. https://doi.org/10.1016/j.taap.2018.06.001
Bryan A. Stanfill, Ernesto S. Nakayasu, Lisa M. Bramer, Allison M. Thompson, Charles K. Ansong, Therese R. Clauss, Marina A. Gritsenko, Matthew E. Monroe, Ronald J. Moore, Daniel J. Orton, Paul D. Piehowski, Athena A. Schepmoes, Richard D. Smith, Bobbie-Jo M. Webb-Robertson, Thomas O. Metz, . Quality Control Analysis in Real-time (QC-ART): A Tool for Real-time Quality Control Assessment of Mass Spectrometry-based Proteomics Data. Molecular & Cellular Proteomics 2018, 17 (9) , 1824-1836. https://doi.org/10.1074/mcp.RA118.000648
Biswapriya B. Misra. Updates on resources, software tools, and databases for plant proteomics in 2016-2017. ELECTROPHORESIS 2018, 39 (13) , 1543-1557. https://doi.org/10.1002/elps.201700401
Cristina Chiva, Roger Olivella, Eva Borràs, Guadalupe Espadas, Olga Pastor, Amanda Solé, Eduard Sabidó, . QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories. PLOS ONE 2018, 13 (1) , e0189209. https://doi.org/10.1371/journal.pone.0189209
Abdo Alnabulsi, Graeme I. Murray. Proteomics for early detection of colorectal cancer: recent updates. Expert Review of Proteomics 2018, 15 (1) , 55-63. https://doi.org/10.1080/14789450.2018.1396893
Rebecca Wangen, Elise Aasebø, Andrea Trentani, Stein-Ove Døskeland, Øystein Bruserud, Frode Selheim, Maria Hernandez-Valladares. Preservation Method and Phosphate Buffered Saline Washing Affect the Acute Myeloid Leukemia Proteome. International Journal of Molecular Sciences 2018, 19 (1) , 296. https://doi.org/10.3390/ijms19010296
Marica Grossegesse, Joerg Doellinger, Alona Tyshaieva, Lars Schaade, Andreas Nitsche. Combined Proteomics/Genomics Approach Reveals Proteomic Changes of Mature Virions as a Novel Poxvirus Adaptation Mechanism. Viruses 2017, 9 (11) , 337. https://doi.org/10.3390/v9110337
I. Benoit-Gelber, T. Gruntjes, A. Vinck, J.G. van Veluw, H.A.B. Wösten, S. Boeren, J.J.M. Vervoort, R.P. de Vries. Mixed colonies of Aspergillus niger and Aspergillus oryzae cooperatively degrading wheat bran. Fungal Genetics and Biology 2017, 102 , 31-37. https://doi.org/10.1016/j.fgb.2017.02.006
Wout Bittremieux, Dirk Valkenborg, Lennart Martens, Kris Laukens. Computational quality control tools for mass spectrometry proteomics. PROTEOMICS 2017, 17 (3-4) , 1600159. https://doi.org/10.1002/pmic.201600159
Abdo Alnabulsi, Graeme I Murray. Integrative analysis of the colorectal cancer proteome: potential clinical impact. Expert Review of Proteomics 2016, 13 (10) , 917-927. https://doi.org/10.1080/14789450.2016.1233062

Abstract
High Resolution Image
Download MS PowerPoint Slide
Figure 1
Figure 1. Experimental and software workflow for bottom-up shotgun proteomics experiments. First, the protein sample is digested, typically using trypsin, to yield peptides. Subsequently, the sample is subjected to HPLC, separating the peptides by their physicochemical properties. The eluent is then ionizied using electrospray ionization, and the mass/charge ratio of the peptides is measured. The quality of the resulting spectra is influenced by all preceding steps. Spectra are then submitted to MaxQuant for analysis. The resulting output is assessed by PTXQC, and upon passing the quality criteria, it is cleared for downstream analysis. If quality is not satisfactory, then either remeasurement is required or (preferably) MaxQuant parameters are adapted to remove the bias detected by PTXQC.
High Resolution Image
Download MS PowerPoint Slide
Figure 2
Figure 2. Heatmap overview of a TMT-labeled data set. Columns denote the metric; rows correspond to Raw files. The color gradient for each cell ranges from best (green), to underperforming (black), and, finally, fail (red). Column names are sorted and color-coded (gray or black, alternating) by the four main steps in the analytical workflow.
High Resolution Image
Download MS PowerPoint Slide
Figure 3
Figure 3. A custom database containing proteins from Mycoplasma hyorhinis was included during the MaxQuant analysis of an in-house human QC data set. PTXQC was configured to search for mycoplasma proteins. (A) Summary of the relative abundance (red) and spectral counts (blue) of proteins (or protein descriptions) containing the string “MYCOPLASMA”. The first two Raw files (file 1, file 2) serve as negative controls, in addition to two Raw files with known contamination (file 3, file 4), as confirmed by both intensity and spectral counting. The default threshold of 1% is plotted by PTXQC as a horizontal dashed line for visual guidance. Exceeding the threshold will report the respective Raw file as failed in the overview heatmap. (B) Corresponding heatmap summarizing the whole study. The second column shows the scores for the mycoplasma query. This column is present only if a custom contaminant query is requested via the PTXQC configuration file.
High Resolution Image
Download MS PowerPoint Slide
Figure 4
Figure 4. Retention time correction using Match-between-runs. Alignment performance is judged using the residual RT difference (ΔRT) of identical genuine 3D peak pairs after alignment with respect to a reference file (file 1). Each ID-pair is represented by a dot: green dots indicate that the underlying 3D peaks are successfully aligned, with a residual RT difference of less than 0.7 min. Red dots indicate that the alignment was unable to bring the 3D peaks close enough in RT (>0.7 min). The RT correction function of MaxQuant is shown in blue. The fraction of good pairs is given in the panel title, e.g., 99% of the pairs between the reference (file 1) and file 2 are successfully aligned. (A) Four Raw files of human QC samples with varying degrees of alignment success (decreasing). MaxQuant’s RT alignment tolerance window was set to the default of 20 min. The horizontal yellow arrow indicates the required RT alignment tolerance (∼85 min). (B) The same files as in (A) but with a larger RT alignment tolerance of 100 min. Note the increased fraction of good ID-pairs for file 4 (11%) due to a small region between 200 and 250 min that was now successfully aligned. (C) Side-by-side representation of the MBR alignment scores for the analyses in A (left column) and B (right column) as shown in the heatmap. The actual heatmap has many more columns; we show only the column of interest, “EVD: MBR Align”. File 3 shows a trend toward being colored red (due to the score decreasing from 58 to 40%); file 4 shows a slight improvement (from 0 to 11%).
High Resolution Image
Download MS PowerPoint Slide
Figure 5
Figure 5. ID-transfer performance of Match-between-runs. Per Raw file (rows), three different aspects of evidence are shown (columns): “genuine” uses only 3D peaks that have genuine MS² identifications, “transferred” ignores 3D peak groups that are purely genuine, and “all” considers all evidence (genuine + transferred). Each stacked bar contains three peak classes, together summing to 100% of peaks: single, group (in width), and group (out width). (A) Four Raw files of human QC samples. Files 1 and 2 were measured on the same day, file 3, the following day, and file 4, under different column conditions (aging) a few months earlier. MaxQuant’s RT alignment tolerance was set to the default of 20 min. Most IDs transferred to file 4 are false positives (large red bar in the “transferred” column). The overall effect is not drastic (“all” column) since most IDs in file 4 are genuine and only few IDs were transferred to file 4. (B) The same files as in (A) but with a larger RT alignment tolerance of 100 min. Note the decreased contribution of the “group (out-width)” for file 4, indicating fewer false positive matches. (C) Side-by-side representation of the MBR ID-transfer scores for the analyses in A (left column) and B (right column) as shown in the heatmap. The actual heatmap has many more columns; we show only the column of interest, “EVD: MBR ID-Transfer”. The first three files show almost no change, whereas file 4 shows an improvement (dark red to black).
High Resolution Image
Download MS PowerPoint Slide
References
ARTICLE SECTIONS
Jump To
This article references 27 other publications.
1. 1
  Cox, J.; Mann, M. MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification Nat. Biotechnol. 2008, 26, 1367– 1372 DOI: 10.1038/nbt.1511
  [Crossref], [PubMed], [CAS], Google Scholar
  1
  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification
  Cox, Juergen; Mann, Matthias
  Nature Biotechnology (2008), 26 (12), 1367-1372CODEN: NABIF9; ISSN:1087-0156. (Nature Publishing Group)
  Efficient anal. of very large amts. of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resoln., quant. MS data. Using correlation anal. and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over std. techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome expt. and allows statistically robust identification and quantification of >4000 proteins in mammalian cell lysates.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhsVWjtLzJ&md5=675d31ca84e3a7e4fb9bdd601d8075ea
2. 2
  Tabb, D. L. Quality assessment for clinical proteomics Clin. Biochem. 2013, 46, 411– 420 DOI: 10.1016/j.clinbiochem.2012.12.003
  [Crossref], [PubMed], [CAS], Google Scholar
  2
  Quality assessment for clinical proteomics
  Tabb, David L.
  Clinical Biochemistry (2013), 46 (6), 411-420CODEN: CLBIAS; ISSN:0009-9120. (Elsevier B.V.)
  A review. Proteomics has emerged from the labs of technologists to enter widespread application in clin. contexts. This transition, however, has been hindered by overstated early claims of accuracy, concerns about reproducibility, and the challenges of handling batch effects properly. New efforts have produced sets of performance metrics and measurements of variability that establish sound expectations for expts. in clin. proteomics. As researchers begin incorporating these metrics in a quality by design paradigm, the variability of individual steps in exptl. pipelines will be reduced, regularizing overall outcomes. This review discusses the evolution of quality assessment in 2D gel electrophoresis, mass spectrometry-based proteomic profiling, tandem mass spectrometry-based protein inventories, and proteomic quantitation. Taken together, the advances in each of these technologies are establishing databases that will be increasingly useful for decision-making in clin. experimentation.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFyquw%253D%253D&md5=413a5ec5acd42ed37327dd0813a64175
3. 3
  Petricoin, E. F., III; Ardekani, A. M.; Hitt, B. A.; Levine, P. J.; Fusaro, V. A.; Steinberg, S. M.; Mills, G. B.; Simone, C.; Fishman, D. A.; Kohn, E. C.; Liotta, L. A. Use of proteomic patterns in serum to identify ovarian cancer Lancet 2002, 359, 572– 577 DOI: 10.1016/S0140-6736(02)07746-2
  [Crossref], [PubMed], [CAS], Google Scholar
  3
  Use of proteomic patterns in serum to identify ovarian cancer
  Petricoin, Emanuel F., III; Ardekani, Ali M.; Hitt, Ben A.; Levine, Peter J.; Fusaro, Vincent A.; Steinberg, Seth M.; Mills, Gordon B.; Simone, Charles; Fishman, David A.; Kohn, Elise C.; Liotta, Lance A.
  Lancet (2002), 359 (9306), 572-577CODEN: LANCAO; ISSN:0140-6736. (Lancet Publishing Group)
  New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathol. changes within an organ might be reflected in proteomic patterns in serum. The authors developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionization). A preliminary "training" set of spectra derived from anal. of serum from 50 unaffected women and 50 patients with ovarian cancer were analyzed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders. The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognized as not cancer. This result yielded a sensitivity of 100% (95% CI 93-100), specificity of 95% (87-99), and pos. predictive value of 94% (84-99). These findings justify a prospective population-based assessment of proteomic pattern technol. as a screening tool for all stages of ovarian cancer in high-risk and general populations.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XhsFansbw%253D&md5=7b23ec6a32f27dd67e874d30fdfc6220
4. 4
  Baggerly, K. A.; Morris, J. S.; Coombes, K. R. Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments Bioinformatics 2004, 20, 777– 785 DOI: 10.1093/bioinformatics/btg484
  [Crossref], [PubMed], [CAS], Google Scholar
  4
  Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments
  Baggerly, Keith A.; Morris, Jeffrey S.; Coombes, Kevin R.
  Bioinformatics (2004), 20 (5), 777-785CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
  Motivation: There has been much interest in using patterns derived from surface-enhanced laser desorption and ionization (SELDI) protein mass spectra from serum to differentiate samples from patients both with and without disease. Such patterns have been used without identification of the underlying proteins responsible. However, there are questions as to the stability of this procedure over multiple expts. Results: We compared SELDI proteomic spectra from serum from three expts. by the same group on sepg. ovarian cancer from normal tissue. These spectra are available on the web at. In general, the results were not reproducible across expts. Baseline correction prevents reprodn. of the results for two of the expts. In one expt., there is evidence of a major shift in protocol mid-expt. which could bias the results. In another, structure in the noise regions of the spectra allows us to distinguish normal from cancer, suggesting that the normals and cancers were processed differently. Sets of features found to discriminate well in one expt. do not generalize to other expts. Finally, the mass calibration in all three expts. appears suspect. Taken together, these and other concerns suggest that much of the structure uncovered in these expts. could be due to artifacts of sample processing, not to the underlying biol. of cancer. We provide some guidelines for design and anal. in expts. like these to ensure better reproducible, biol. meaningfully results.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXitlyhs7Y%253D&md5=6d13a31b22d983b72b31efa9d4246d77
5. 5
  Rodriguez, H.; Snyder, M.; Uhlén, M.; Andrews, P.; Beavis, R.; Borchers, C.; Chalkley, R. J.; Cho, S. Y.; Cottingham, K.; Dunn, M. Recommendations from the 2008 international summit on proteomics data release and sharing policy: The Amsterdam principles J. Proteome Res. 2009, 8, 3689– 3692 DOI: 10.1021/pr900023z
  [ACS Full Text ], [CAS], Google Scholar
  5
  Recommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy: The Amsterdam Principles
  Rodriguez, Henry; Snyder, Mike; Uhlen, Mathias; Andrews, Phil; Beavis, Ronald; Borchers, Christoph; Chalkley, Robert J.; Cho, Sang Yun; Cottingham, Katie; Dunn, Michael; Dylag, Tomasz; Edgar, Ron; Hare, Peter; Heck, Albert J. R.; Hirsch, Roland F.; Kennedy, Karen; Kolar, Patrik; Kraus, Hans-Joachim; Mallick, Parag; Nesvizhskii, Alexey; Ping, Peipei; Ponten, Fredrik; Yang, Liming; Yates, John R.; Stein, Stephen E.; Hermjakob, Henning; Kinsinger, Christopher R.; Apweiler, Rolf
  Journal of Proteome Research (2009), 8 (7), 3689-3692CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  Policies supporting the rapid and open sharing of genomic data have directly fueled the accelerated pace of discovery in large-scale genomics research. The proteomics community is starting to implement analogous policies and infrastructure for making large-scale proteomics data widely available on a precompetitive basis. On August 14, 2008, the National Cancer Institute (NCI) convened the "International Summit on Proteomics Data Release and Sharing Policy" in Amsterdam, The Netherlands, to identify and address potential roadblocks to rapid and open access to data. The six principles agreed upon by key stakeholders at the summit addressed issues surrounding (1) timing, (2) comprehensiveness, (3) format, (4) deposition to repositories, (5) quality metrics, and (6) responsibility for proteomics data release. This summit report explores various approaches to develop a framework of data release and sharing principles that will most effectively fulfill the needs of the funding agencies and the research community.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXmtVGrsLw%253D&md5=9f55d19f947d886bb522677024eda199
6. 6
  Kinsinger, C. R.; Apffel, J.; Baker, M.; Bian, X.; Borchers, C. H.; Bradshaw, R.; Brusniak, M.-Y.; Chan, D. W.; Deutsch, E. W.; Domon, B. Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles) J. Proteome Res. 2012, 11, 1412– 1419 DOI: 10.1021/pr201071t
  [ACS Full Text ], [CAS], Google Scholar
  6
  Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles)
  Kinsinger, Christopher R.; Apffel, James; Baker, Mark; Bian, Xiaopeng; Borchers, Christoph H.; Bradshaw, Ralph; Brusniak, Mi-Youn; Chan, Daniel W.; Deutsch, Eric W.; Domon, Bruno; Gorman, Jeff; Grimm, Rudolf; Hancock, William; Hermjakob, Henning; Horn, David; Hunter, Christie; Kolar, Patrik; Kraus, Hans-Joachim; Langen, Hanno; Linding, Rune; Moritz, Robert L.; Omenn, Gilbert S.; Orlando, Ron; Pandey, Akhilesh; Ping, Peipei; Rahbar, Amir; Rivers, Robert; Seymour, Sean L.; Simpson, Richard J.; Slotta, Douglas; Smith, Richard D.; Stein, Stephen E.; Tabb, David L.; Tagle, Danilo; Yates, John R., III; Rodriguez, Henry
  Journal of Proteome Research (2012), 11 (2), 1412-1419CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On Sept. 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: (1) an evolving list of comprehensive quality metrics and (2) stds. accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data.By agreement, this article is published simultaneously in the Journal of Proteome Research, Mol. and Cellular Proteomics, Proteomics, and Proteomics Clin. Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXhsVWmsrvM&md5=0cb2de0296ec2af55330b60f61df84a9
7. 7
  Rudnick, P. A.; Clauser, K. R.; Kilpatrick, L. E.; Tchekhovskoi, D. V.; Neta, P.; Blonder, N.; Billheimer, D. D.; Blackman, R. K.; Bunk, D. M.; Cardasis, H. L. Performance Metrics for Liquid Chromatography-Tandem Mass Spectrometry Systems in Proteomics Analyses Mol. Cell. Proteomics 2010, 9, 225– 241 DOI: 10.1074/mcp.M900223-MCP200
  [Crossref], [PubMed], [CAS], Google Scholar
  7
  Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses
  Rudnick, Paul A.; Clauser, Karl R.; Kilpatrick, Lisa E.; Tchekhovskoi, Dmitrii V.; Neta, Pedatsur; Blonder, Niksa; Billheimer, Dean D.; Blackman, Ronald K.; Bunk, David M.; Cardasis, Helene L.; Ham, Amy-Joan L.; Jaffe, Jacob D.; Kinsinger, Christopher R.; Mesri, Mehdi; Neubert, Thomas A.; Schilling, Birgit; Tabb, David L.; Tegeler, Tony J.; Vega-Montoto, Lorenzo; Variyath, Asokan Mulayath; Wang, Mu; Wang, Pei; Whiteaker, Jeffrey R.; Zimmerman, Lisa J.; Carr, Steven A.; Fisher, Susan J.; Gibson, Bradford W.; Paulovich, Amanda G.; Regnier, Fred E.; Rodriguez, Henry; Spiegelman, Cliff; Tempst, Paul; Liebler, Daniel C.; Stein, Stephen E.
  Molecular and Cellular Proteomics (2010), 9 (2), 225-241CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
  A major unmet need in LC-MS/MS-based proteomics analyses is a set of tools for quant. assessment of system performance and evaluation of tech. variability. Here we describe 46 system performance metrics for monitoring chromatog. performance, electrospray source stability, MS1 and MS2 signals, dynamic sampling of ions for MS/MS, and peptide identification. Applied to data sets from replicate LC-MS/MS analyses, these metrics displayed consistent, reasonable responses to controlled perturbations. The metrics typically displayed variations less than 10% and thus can reveal even subtle differences in performance of system components. Analyses of data from interlab. studies conducted under a common std. operating procedure identified outlier data and provided clues to specific causes. Moreover, interlab. variation reflected by the metrics indicates which system components vary the most between labs. Application of these metrics enables rational, quant. quality assessment for proteomics and other LC-MS/MS anal. applications.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjtFWhsb8%253D&md5=7c79551182bac7134467c423509f72ce
8. 8
  Paulovich, A. G. Interlaboratory Study Characterizing a Yeast Performance Standard for Benchmarking LC-MS Platform Performance Mol. Cell. Proteomics 2010, 9, 242– 254 DOI: 10.1074/mcp.M900222-MCP200
  [Crossref], [PubMed], [CAS], Google Scholar
  8
  Interlaboratory study characterizing a yeast performance standard for benchmarking LC-MS platform performance
  Paulovich, Amanda G.; Billheimer, Dean; Ham, Amy-Joan L.; Vega-Montoto, Lorenzo; Rudnick, Paul A.; Tabb, David L.; Wang, Pei; Blackman, Ronald K.; Bunk, David M.; Cardasis, Helene L.; Clauser, Karl R.; Kinsinger, Christopher R.; Schilling, Birgit; Tegeler, Tony J.; Variyath, Asokan Mulayath; Wang, Mu; Whiteaker, Jeffrey R.; Zimmerman, Lisa J.; Fenyo, David; Carr, Steven A.; Fisher, Susan J.; Gibson, Bradford W.; Mesri, Mehdi; Neubert, Thomas A.; Regnier, Fred E.; Rodriguez, Henry; Spiegelman, Cliff; Stein, Stephen E.; Tempst, Paul; Liebler, Daniel C.
  Molecular and Cellular Proteomics (2010), 9 (2), 242-254CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
  Optimal performance of LC-MS/MS platforms is crit. to generating high quality proteomics data. Although individual labs. have developed quality control samples, there is no widely available performance std. of biol. complexity (and assocd. ref. data sets) for benchmarking of platform performance for anal. of complex biol. proteomes across different labs. in the community. Individual prepns. of the yeast Saccharomyces cerevisiae proteome have been used extensively by labs. in the proteomics community to characterize LC-MS platform performance. The yeast proteome is uniquely attractive as a performance std. because it is the most extensively characterized complex biol. proteome and the only one assocd. with several large scale studies estg. the abundance of all detectable proteins. In this study, we describe a std. operating protocol for large scale prodn. of the yeast performance std. and offer aliquots to the community through the National Institute of Stds. and Technol. where the yeast proteome is under development as a certified ref. material to meet the long term needs of the community. Using a series of metrics that characterize LC-MS performance, we provide a ref. data set demonstrating typical performance of commonly used ion trap instrument platforms in expert labs.; the results provide a basis for labs. to benchmark their own performance, to improve upon current methods, and to evaluate new technologies. Addnl., we demonstrate how the yeast ref., spiked with human proteins, can be used to benchmark the power of proteomics platforms for detection of differentially expressed proteins at different levels of concn. in a complex matrix, thereby providing a metric to evaluate and minimize preanal. and anal. variation in comparative proteomics expts.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3cXjtFWhsbw%253D&md5=2057d48a1a5edd1c7ba8c21e29dd6065
9. 9
  Geer, L. Y.; Markey, S. P.; Kowalak, J. A.; Wagner, L.; Xu, M.; Maynard, D. M.; Yang, X.; Shi, W.; Bryant, S. H. Open Mass Spectrometry Search Algorithm J. Proteome Res. 2004, 3, 958– 964 DOI: 10.1021/pr0499491
  [ACS Full Text ], [CAS], Google Scholar
  9
  Open Mass Spectrometry Search Algorithm
  Geer, Lewis Y.; Markey, Sanford P.; Kowalak, Jeffrey A.; Wagner, Lukas; Xu, Ming; Maynard, Dawn M.; Yang, Xiaoyu; Shi, Wenyao; Bryant, Stephen H.
  Journal of Proteome Research (2004), 3 (5), 958-964CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  Large nos. of MS/MS peptide spectra generated in proteomics expts. require efficient, sensitive and specific algorithms for peptide identification. In the Open Mass Spectrometry Search Algorithm (OMSSA), specificity is calcd. by a classic probability score using an explicit model for matching exptl. spectra to sequences. At default thresholds, OMSSA matches more spectra from a std. protein cocktail than a comparable algorithm. OMSSA is designed to be faster than published algorithms in searching large MS/MS datasets.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXltl2lur8%253D&md5=811b62e2caae44f6e4f83cbbff48b7b6
10. 10
  Lam, H.; Deutsch, E.; Eddes, J.; Eng, J.; King, N.; Yang, S.; Roth, J.; Kilpatrick, L.; Neta, P.; Stein, S. SpectraST: An open-source MS/MS spectramatching library search tool for targeted proteomics, 54th ASMS Conference on Mass Spectrometry, Seattle, Washington, May 28–June 1, 2006.
  Google Scholar
  There is no corresponding record for this reference.
11. 11
  Ma, Z.-Q.; Polzin, K. O.; Dasari, S.; Chambers, M. C.; Schilling, B.; Gibson, B. W.; Tran, B. Q.; Vega-Montoto, L.; Liebler, D. C.; Tabb, D. L. QuaMeter: multivendor performance metrics for LC–MS/MS proteomics instrumentation Anal. Chem. 2012, 84, 5845– 5850 DOI: 10.1021/ac300629p
  [ACS Full Text ], [CAS], Google Scholar
  11
  QuaMeter: Multivendor Performance Metrics for LC-MS/MS Proteomics Instrumentation
  Ma, Ze-Qiang; Polzin, Kenneth O.; Dasari, Surendra; Chambers, Matthew C.; Schilling, Birgit; Gibson, Bradford W.; Tran, Bao Q.; Vega-Montoto, Lorenzo; Liebler, Daniel C.; Tabb, David L.
  Analytical Chemistry (Washington, DC, United States) (2012), 84 (14), 5845-5850CODEN: ANCHAM; ISSN:0003-2700. (American Chemical Society)
  LC-MS/MS-based proteomics studies rely on stable anal. system performance that can be evaluated by objective criteria. The National Institute of Stds. and Technol. (NIST) introduced the MSQC software to compute diverse metrics from exptl. LC-MS/MS data, enabling quality anal. and quality control (QA/QC) of proteomics instrumentation. In practice, however, several attributes of the MSQC software prevent its use for routine instrument monitoring. Here, we present QuaMeter, an open-source tool that improves MSQC in several aspects. QuaMeter can directly read raw data from instruments manufd. by different vendors. The software can work with a wide variety of peptide identification software for improved reliability and flexibility. Finally, QC metrics implemented in QuaMeter are rigorously defined and tested. The source code and binary versions of QuaMeter are available under Apache 2.0 License at http://fenchurch.mc.vanderbilt.edu.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XosFelsr0%253D&md5=99c35883d058b2e2a892ce9f06a4a370
12. 12
  Taylor, R. M.; Dance, J.; Taylor, R. J.; Prince, J. T. Metriculator: quality assessment for mass spectrometry-based proteomics Bioinformatics 2013, 29, 2948– 2949 DOI: 10.1093/bioinformatics/btt510
  [Crossref], [PubMed], [CAS], Google Scholar
  12
  Metriculator: quality assessment for mass spectrometry-based proteomics
  Taylor, Ryan M.; Dance, Jamison; Taylor, Russ J.; Prince, John T.
  Bioinformatics (2013), 29 (22), 2948-2949CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
  Quality control in mass spectrometry-based proteomics remains subjective, labor-intensive and inconsistent between labs. We introduce Metriculator, a software designed to facilitate long-term storage of extensive performance metrics as introduced by NIST in 2010. Metriculator features a web interface that generates interactive comparison plots for contextual understanding of metric values and an automated metric generation toolkit. The comparison plots are designed for at-a-glance detn. of outliers and trends in the datasets, together with relevant statistical comparisons. Easy-to-use quant. comparisons and a framework for integration plugins will encourage a culture of quality assurance within the proteomics community.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhslWnsbzO&md5=0bce88fda1411349f7a3df756a9a7d9b
13. 13
  Walzer, M. qcML: An Exchange Format for Quality Control Metrics from Mass Spectrometry Experiments Mol. Cell. Proteomics 2014, 13, 1905– 1913 DOI: 10.1074/mcp.M113.035907
  [Crossref], [PubMed], [CAS], Google Scholar
  13
  qcML: An Exchange Format for Quality Control Metrics from Mass Spectrometry Experiments
  Walzer, Mathias; Pernas, Lucia Espona; Nasso, Sara; Bittremieux, Wout; Nahnsen, Sven; Kelchtermans, Pieter; Pichler, Peter; van den Toorn, Henk W. P.; Staes, An; Vandenbussche, Jonathan; Mazanek, Michael; Taus, Thomas; Scheltema, Richard A.; Kelstrup, Christian D.; Gatto, Laurent; van Breukelen, Bas; Aiche, Stephan; Valkenborg, Dirk; Laukens, Kris; Lilley, Kathryn S.; Olsen, Jesper V.; Heck, Albert J. R.; Mechtler, Karl; Aebersold, Ruedi; Gevaert, Kris; Vizcaino, Juan Antonio; Hermjakob, Henning; Kohlbacher, Oliver; Martens, Lennart
  Molecular & Cellular Proteomics (2014), 13 (8), 1905-1913CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
  Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to ext. these from the instrumental raw data. What has been missing, however, is a std. data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based std. that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML stds. from the HUPO-PSI (Proteomics Stds. Initiative). In addn. to the XML format, we also provide tools for the calcn. of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent anal. possibilities. All information about qcML is available at http://code.google.com/p/qcml.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXht1CksL%252FF&md5=b87d256f986786be3f1b6c343410ddfa
14. 14
  Pichler, P.; Mazanek, M.; Dusberger, F.; Weilnböck, L.; Huber, C. G.; Stingl, C.; Luider, T. M.; Straube, W. L.; Köcher, T.; Mechtler, K. SIMPATIQCO: a server-based software suite which facilitates monitoring the time course of LC–MS performance metrics on Orbitrap instruments J. Proteome Res. 2012, 11, 5540– 5547 DOI: 10.1021/pr300163u
  [ACS Full Text ], [CAS], Google Scholar
  14
  SIMPATIQCO: A Server-Based Software Suite Which Facilitates Monitoring the Time Course of LC-MS Performance Metrics on Orbitrap Instruments
  Pichler, Peter; Mazanek, Michael; Dusberger, Frederico; Weilnboeck, Lisa; Huber, Christian G.; Stingl, Christoph; Luider, Theo M.; Straube, Werner L.; Koecher, Thomas; Mechtler, Karl
  Journal of Proteome Research (2012), 11 (11), 5540-5547CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  While the performance of liq. chromatog. (LC) and mass spectrometry (MS) instrumentation continues to increase, applications such as analyses of complete or near-complete proteomes and quant. studies require const. and optimal system performance. For this reason, research labs. and core facilities alike are recommended to implement quality control (QC) measures as part of their routine workflows. Many labs. perform sporadic quality control checks. However, successive and systematic longitudinal monitoring of system performance would be facilitated by dedicated automatic or semiautomatic software solns. that aid an effortless anal. and display of QC metrics over time. We present the software package SIMPATIQCO (SIMPle AuTomatIc Quality COntrol) designed for evaluation of data from LTQ Orbitrap, Q-Exactive, LTQ FT, and LTQ instruments. A centralized SIMPATIQCO server can process QC data from multiple instruments. The software calcs. QC metrics supervising every step of data acquisition from LC and electrospray to MS. For each QC metric the software learns the range indicating adequate system performance from the uploaded data using robust statistics. Results are stored in a database and can be displayed in a comfortable manner from any computer in the lab. via a web browser. QC data can be monitored for individual LC runs as well as plotted over time. SIMPATIQCO thus assists the longitudinal monitoring of important QC metrics such as peptide elution times, peak widths, intensities, total ion current (TIC) as well as sensitivity, and overall LC-MS system performance; in this way the software also helps identify potential problems. The SIMPATIQCO software package is available free of charge.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhsFequrvJ&md5=f3979ab704ec9ad64f2f7ec7c6afb7f0
15. 15
  Dorfer, V.; Pichler, P.; Stranzl, T.; Stadlmann, J.; Taus, T.; Winkler, S.; Mechtler, K. MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra J. Proteome Res. 2014, 13, 3679– 3684 DOI: 10.1021/pr500202e
  [ACS Full Text ], [CAS], Google Scholar
  15
  MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra
  Dorfer, Viktoria; Pichler, Peter; Stranzl, Thomas; Stadlmann, Johannes; Taus, Thomas; Winkler, Stephan; Mechtler, Karl
  Journal of Proteome Research (2014), 13 (8), 3679-3684CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  Today's highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the anal. of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide identification algorithm. While most widely used search engines were developed when high-resoln. mass spectrometry data were not readily available for fragment ion masses, the authors have designed a scoring algorithm particularly suitable for high mass accuracy. The authors' algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examd. high mass accuracy data sets, with excellent overlap and identical peptide sequence identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at http://ms.imp.ac.at/goto=msamanda, is provided free of charge both as standalone version for integration into custom workflows and as a plugin for the Proteome Discoverer platform.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXptl2nsL4%253D&md5=3d3100ed4c0d6c667f1b5fa2c6be18eb
16. 16
  Amidan, B. G.; Orton, D. J.; LaMarche, B. L.; Monroe, M. E.; Moore, R. J.; Venzin, A. M.; Smith, R. D.; Sego, L. H.; Tardiff, M. F.; Payne, S. H. Signatures for Mass Spectrometry Data Quality J. Proteome Res. 2014, 13, 2215– 2222 DOI: 10.1021/pr401143e
  [ACS Full Text ], [CAS], Google Scholar
  16
  Signatures for Mass Spectrometry Data Quality
  Amidan, Brett G.; Orton, Daniel J.; LaMarche, Brian L.; Monroe, Matthew E.; Moore, Ronald J.; Venzin, Alexander M.; Smith, Richard D.; Sego, Landon H.; Tardiff, Mark F.; Payne, Samuel H.
  Journal of Proteome Research (2014), 13 (4), 2215-2222CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  Ensuring data quality and proper instrument functionality is a prerequisite for scientific investigation. Manual quality assurance is time-consuming and subjective. Metrics for describing liq. chromatog. mass spectrometry (LC-MS) data have been developed; however, the wide variety of LC-MS instruments and configurations precludes applying a simple cutoff. Using 1150 manually classified quality control (QC) data sets, we trained logistic regression classification models to predict whether a data set is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the trade-off between false pos. and false neg. errors. The classifier models detected bad data sets with high sensitivity while maintaining high specificity. Moreover, the composite classifier was dramatically more specific than single metrics. Finally, we evaluated the performance of the classifier on a sep. validation set where it performed comparably to the results for the testing/training data sets. By presenting the methods and software used to create the classifier, other groups can create a classifier for their specific QC regimen, which is highly variable lab-to-lab. In total, this manuscript presents 3400 LC-MS data sets for the same QC sample (whole cell lysate of Shewanella oneidensis), deposited to the ProteomeXchange with identifiers PXD000320-PXD000324.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXjslCqsr0%253D&md5=4d540fad07265b1361aef31ed612510f
17. 17
  R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014.
  Google Scholar
  There is no corresponding record for this reference.
18. 18
  Gatto, L.; Breckels, L. M.; Naake, T.; Gibb, S. Visualization of proteomics data using R and Bioconductor Proteomics 2015, 15, 1375– 1389 DOI: 10.1002/pmic.201400392
  [Crossref], [PubMed], [CAS], Google Scholar
  18
  Visualization of proteomics data using R and Bioconductor
  Gatto, Laurent; Breckels, Lisa M.; Naake, Thomas; Gibb, Sebastian
  Proteomics (2015), 15 (8), 1375-1389CODEN: PROTC7; ISSN:1615-9853. (Wiley-VCH Verlag GmbH & Co. KGaA)
  Data visualization plays a key role in high-throughput biol. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXmtVarurw%253D&md5=684af78aac1287607d5506be64106558
19. 19
  Cox, J.; Neuhauser, N.; Michalski, A.; Scheltema, R. A.; Olsen, J. V.; Mann, M. Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment J. Proteome Res. 2011, 10, 1794– 1805 DOI: 10.1021/pr101065j
  [ACS Full Text ], [CAS], Google Scholar
  19
  Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment
  Cox, Juergen; Neuhauser, Nadin; Michalski, Annette; Scheltema, Richard A.; Olsen, Jesper V.; Mann, Matthias
  Journal of Proteome Research (2011), 10 (4), 1794-1805CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  A key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Here the authors describe Andromeda, a novel peptide search engine using a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used com. search engine, as judged by sensitivity and specificity anal. based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform and both are freely available at www.maxquant.org. The combination enables anal. of large data sets in a simple anal. workflow on a desktop computer. For searching individual spectra Andromeda is also accessible via a web server. The authors demonstrate the flexibility of the system by implementing the capability to identify cofragmented peptides, significantly improving the total no. of identified peptides.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXit1Gis74%253D&md5=7587da6364fe0ff020e3dbf1d80bb22f
20. 20
  Cox, J.; Hein, M. Y.; Luber, C. A.; Paron, I.; Nagaraj, N.; Mann, M. Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ Mol. Cell. Proteomics 2014, 13, 2513– 2526 DOI: 10.1074/mcp.M113.031591
  [Crossref], [PubMed], [CAS], Google Scholar
  20
  Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ
  Cox, Juergen; Hein, Marco Y.; Luber, Christian A.; Paron, Igor; Nagaraj, Nagarjuna; Mann, Matthias
  Molecular & Cellular Proteomics (2014), 13 (9), 2513-2526CODEN: MCPOBS; ISSN:1535-9484. (American Society for Biochemistry and Molecular Biology)
  Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity detn. and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein sepn. prior to LC-MS anal. Protein abundance profiles are assembled using the max. possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technol. that is readily applicable to many biol. questions; it is compatible with std. statistical anal. workflows, and it has been validated in many and diverse biol. projects. Our algorithms can handle very large expts. of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVynurrI&md5=f3f1c7dc8fbf729c568446968b89f37c
21. 21
  Geiger, T.; Wehner, A.; Schaab, C.; Cox, J.; Mann, M. Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins Mol. Cell. Proteomics 2012, 11, M111.014050 DOI: 10.1074/mcp.M111.014050
  [Crossref], [PubMed], Google Scholar
  There is no corresponding record for this reference.
22. 22
  Chiva, C.; Ortega, M.; Sabidó, E. Influence of the Digestion Technique, Protease, and Missed Cleavage Peptides in Protein Quantitation J. Proteome Res. 2014, 13, 3979– 86 DOI: 10.1021/pr500294d
  [ACS Full Text ], [CAS], Google Scholar
  22
  Influence of the Digestion Technique, Protease, and Missed Cleavage Peptides in Protein Quantitation
  Chiva, Cristina; Ortega, Mireia; Sabido, Eduard
  Journal of Proteome Research (2014), 13 (9), 3979-3986CODEN: JPROBS; ISSN:1535-3893. (American Chemical Society)
  Quant. detn. of abs. and relative protein amts. is an essential requirement for most current bottom-up proteomics applications, but protein quantitation ests. are affected by several sources of variability such as sample prepn., mass spectrometric acquisition, and data anal. Among them, sample digestion has attracted much attention from the proteomics community, as protein quantitation by bottom-up proteomics relies on the efficiency and reproducibility of protein enzymic digestion, with the presence of missed cleavages, nonspecific cleavages, or even the use of different proteases having been postulated as important sources of variation in protein quantitation. Here the authors evaluated both in-soln. and filter-aided digestion protocols and assessed their influence in the estn. of protein abundances using five E. coli mixts. with known amts. of spiked proteins. Replicates of trypsin specificity digestion protocols are highly reproducible in terms of peptide quantitation, with digestion technique and the chosen proteolytic enzyme being the major sources of variability in peptide quantitation. Finally, the authors also evaluated the result of including peptides with missed cleavages in protein quantitation and obsd. no significant differences in precision, accuracy, specificity, and sensitivity compared using fully tryptic peptides.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtVyit7rF&md5=4203c7755a3d98f5f0d905647cc78341
23. 23
  Licker, V.; Turck, N.; Kövari, E.; Burkhardt, K.; Côte, M.; Surini-Demiri, M.; Lobrinus, J. A.; Sanchez, J.-C.; Burkhard, P. R. Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson’s disease pathogenesis Proteomics 2014, 14, 784– 794 DOI: 10.1002/pmic.201300342
  [Crossref], [PubMed], [CAS], Google Scholar
  23
  Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson's disease pathogenesis
  Licker, Virginie; Turck, Natacha; Koevari, Enikoe; Burkhardt, Karim; Cote, Melanie; Surini-Demiri, Maria; Lobrinus, Johannes A.; Sanchez, Jean-Charles; Burkhard, Pierre R.
  Proteomics (2014), 14 (6), 784-794CODEN: PROTC7; ISSN:1615-9853. (Wiley-VCH Verlag GmbH & Co. KGaA)
  Parkinson's disease (PD) pathol. spreads throughout the brain following a region-specific process predominantly affecting the substantia nigra (SN) pars compacta. SN exhibits a progressive loss of dopaminergic neurons responsible for the major cardinal motor symptoms, along with the occurrence of Lewy bodies in the surviving neurons. To gain new insights into the underlying pathogenic mechanisms in PD, we studied postmortem nigral tissues dissected from pathol. confirmed PD cases (n = 5) and neurol. intact controls (n = 8). Using a high-throughput shotgun proteomic strategy, we simultaneously identified 1795 proteins with concomitant quant. data. To date, this represents the most extensive catalog of nigral proteins. Of them, 204 proteins displayed significant expression level changes in PD patients vs. controls. These were involved in novel or known pathogenic processes including mitochondrial dysfunction, oxidative stress, or cytoskeleton impairment. We further characterized four candidates that might be relevant to PD pathogenesis. We confirmed the differential expression of ferritin-L and seipin by Western blot and demonstrated the neuronal localization of gamma glutamyl hydrolase and nebulette by immunohistochem. Our preliminary findings suggest a role for nebulette overexpression in PD neurodegeneration, through mechanisms that may involve cytoskeleton dynamics disruption. All MS data have been deposited in the ProteomeXchange with identifier PXD000427. (http://proteomecentral.proteomexchange.org/dataset/PXD000427).
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXisFOrtbc%253D&md5=9631925921afa8b7e50351093bd21cb0
24. 24
  Vizcaíno, J. A. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013 Nucleic Acids Res. 2013, 41, D1063– 9 DOI: 10.1093/nar/gks1262
  [Crossref], [PubMed], [CAS], Google Scholar
  24
  The Proteomics Identifications (PRIDE) database and associated tools: status in 2013
  Vizcaino, Juan Antonio; Cote, Richard G.; Csordas, Attila; Dianes, Jose A.; Fabregat, Antonio; Foster, Joseph M.; Griss, Johannes; Alpi, Emanuele; Birim, Melih; Contell, Javier; O'Kelly, Gavin; Schoenegger, Andreas; Ovelleiro, David; Perez-Riverol, Yasset; Reisinger, Florian; Rios, Daniel; Wang, Rui; Hermjakob, Henning
  Nucleic Acids Research (2013), 41 (D1), D1063-D1069CODEN: NARHAD; ISSN:0305-1048. (Oxford University Press)
  The PRoteomics IDEntifications (PRIDE, http://www.ebi.ac.uk/pride) database at the European Bioinformatics Institute is one of the most prominent data repositories of mass spectrometry (MS)-based proteomics data. Here, we summarize recent developments in the PRIDE database and related tools. First, we provide up-to-date statistics in data content, splitting the figures by groups of organisms and species, including peptide and protein identifications, and post-translational modifications. We then describe the tools that are part of the PRIDE submission pipeline, esp. the recently developed PRIDE Converter 2 (new submission tool) and PRIDE Inspector (visualization and anal. tool). We also give an update about the integration of PRIDE with other MS proteomics resources in the context of the ProteomeXchange consortium. Finally, we briefly review the quality control efforts that are ongoing at present and outline our future plans.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XhvV2ksb3P&md5=e49aba656ba6d88418202d9e54f67db0
25. 25
  Drexler, H. G.; Uphoff, C. C. Mycoplasma contamination of cell cultures: Incidence, sources, effects, detection, elimination, prevention Cytotechnology 2002, 39, 75– 90 DOI: 10.1023/A:1022913015916
  [Crossref], [PubMed], [CAS], Google Scholar
  25
  Mycoplasma contamination of cell cultures: Incidence, sources, effects, detection, elimination, prevention
  Drexler Hans G; Uphoff Cord C
  Cytotechnology (2002), 39 (2), 75-90 ISSN:0920-9069.
  The contamination of cell cultures by mycoplasmas remains a major problem in cell culture. Mycoplasmas can produce a virtually unlimited variety of effects in the cultures they infect. These organisms are resistant to most antibiotics commonly employed in cell cultures. Here we provide a concise overview of the current knowledge on: (1) the incidence and sources of mycoplasma contamination in cell cultures, the mycoplasma species most commonly detected in cell cultures, and the effects of mycoplasmas on the function and activities of infected cell cultures; (2) the various techniques available for the detection of mycoplasmas with particular emphasis on the most reliable detection methods; (3) the various methods available for the elimination of mycoplasmas highlighting antibiotic treatment; and (4) the recommended procedures and working protocols for the detection, elimination and prevention of mycoplasma contamination. The availability of accurate, sensitive and reliable detection methods and the application of robust and successful elimination methods provide powerful means for overcoming the problem of mycoplasma contamination in cell cultures.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cjjtlKhtQ%253D%253D&md5=5b2d4bfa3f147408814d53d41b35c2ce
26. 26
  Noble, W. S. Mass spectrometrists should search only for peptides they care about Nat. Methods 2015, 12, 605– 608 DOI: 10.1038/nmeth.3450
  [Crossref], [PubMed], [CAS], Google Scholar
  26
  Mass spectrometrists should search only for peptides they care about
  Noble, William Stafford
  Nature Methods (2015), 12 (7), 605-608CODEN: NMAEA3; ISSN:1548-7091. (Nature Publishing Group)
  Anal. pipelines that assign peptides to shotgun proteomics mass spectra often discard identified spectra deemed irrelevant to the scientific hypothesis being tested. To improve statistical power, I propose that researchers remove irrelevant peptides from the database prior to searching rather than assigning these peptides to spectra and then discarding the matches.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXht1ygsb7M&md5=f0a7d9fba72f4d26f79fd4ef0cf58038
27. 27
  Suzek, B. E.; Wang, Y.; Huang, H.; McGarvey, P. B.; Wu, C. H. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches Bioinformatics 2015, 31, 926– 932 DOI: 10.1093/bioinformatics/btu739
  [Crossref], [PubMed], [CAS], Google Scholar
  27
  UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches
  Suzek Baris E; Wu Cathy H; Wang Yuqi; Huang Hongzhan; McGarvey Peter B
  Bioinformatics (Oxford, England) (2015), 31 (6), 926-32 ISSN:.
  MOTIVATION: UniRef databases provide full-scale clustering of UniProtKB sequences and are utilized for a broad range of applications, particularly similarity-based functional annotation. Non-redundancy and intra-cluster homogeneity in UniRef were recently improved by adding a sequence length overlap threshold. Our hypothesis is that these improvements would enhance the speed and sensitivity of similarity searches and improve the consistency of annotation within clusters. RESULTS: Intra-cluster molecular function consistency was examined by analysis of Gene Ontology terms. Results show that UniRef clusters bring together proteins of identical molecular function in more than 97% of the clusters, implying that clusters are useful for annotation and can also be used to detect annotation inconsistencies. To examine coverage in similarity results, BLASTP searches against UniRef50 followed by expansion of the hit lists with cluster members demonstrated advantages compared with searches against UniProtKB sequences; the searches are concise (∼7 times shorter hit list before expansion), faster (∼6 times) and more sensitive in detection of remote similarities (>96% recall at e-value <0.0001). Our results support the use of UniRef clusters as a comprehensive and scalable alternative to native sequence databases for similarity searches and reinforces its reliability for use in functional annotation.
  >> More from SciFinder ^®
  https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2M3otlehtQ%253D%253D&md5=bdc2c0e47870945ae712f3932089f4e5
Supporting Information
Supporting Information
ARTICLE SECTIONS
Jump To
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.5b00780.
- Complete reports for the data sets generated by PTXQC (ZIP)
- Summary of data sets, including PRIDE archive identifiers, Figure S1, and a detailed description of all metrics and scoring functions (PDF)
- pr5b00780_si_001.zip (751.66 kb)
- pr5b00780_si_002.pdf (1.25 MB)
Terms & Conditions

Electronic Supporting Information files are available without a subscription to ACS Web Editions. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society. For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system. Information about how to use the RightsLink permission system can be found at http://pubs.acs.org/page/copyright/permissions.html.

PDF [ 3MB]

Proteomics Quality Control: Quality Control Software for MaxQuant Results

Article Views

Altmetric

Citations

Abstract

SPECIAL ISSUE

Introduction

Methods

Figure 1

QC Metrics

Customizable Contaminant Search

Retention Time Alignment and ID-Transfer

Retention Time Alignment

ID Transfer

QC Scores

Results

Overview Heatmap

Figure 2

Custom Contaminant Detection (Mycoplasma)

Figure 3

Retention Time Alignment

Figure 4

ID Transfer

Figure 5

Report Configuration

Discussion

Software Information

Runtime

System Requirements

MaxQuant Support

Target Audience

Software Availability and Documentation

Sample Data

Supporting Information

Terms & Conditions

Acknowledgment

Abbreviations

References

Cited By

Abstract

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

References

Supporting Information

Supporting Information

Terms & Conditions

STEP 1:

STEP 2:

OOPS

This website uses cookies to improve your user experience. By continuing to use the site, you are accepting our use of cookies. Read the ACS privacy policy.