Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Proteome Res. 2016, 15, 1, 332-338

SFINX: Straightforward Filtering Index for Affinity Purification–Mass Spectrometry Data Analysis

Kevin Titeca^†^‡

Pieter Meysman^§^∥

Kris Gevaert^†^‡

Jan Tavernier^†^‡

Kris Laukens^§^∥

Lennart Martens^†^‡^⊥

, and

Sven Eyckerman^*^†^‡

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† VIB Medical Biotechnology Center, A. Baertsoenkaai 3, B-9000 Ghent, Belgium

‡ Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium

§ Advanced Database Research and Modelling (ADReM), Department of Mathematics and Computer Science, University of Antwerp, B-2020 Antwerp, Belgium

∥ Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital, B-2650 Edegem, Belgium

⊥ Bioinformatics Institute Ghent, Ghent University, B-9000 Ghent, Belgium

*Tel: +32-9-264.92.73. Fax: +32-9-264.94.90. E-mail: [email protected]

Cite this: J. Proteome Res. 2016, 15, 1, 332–338

Publication Date (Web):November 30, 2015

https://doi.org/10.1021/acs.jproteome.5b00666

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Abstract

Affinity purification–mass spectrometry is one of the most common techniques for the analysis of protein–protein interactions, but inferring bona fide interactions from the resulting data sets remains notoriously difficult. We introduce SFINX, a Straightforward Filtering INdeX that identifies true-positive protein interactions in a fast, user-friendly, and highly accurate way. SFINX outperforms alternative techniques on two benchmark data sets and is available via the Web interface at http://sfinx.ugent.be/.

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.5b00666.

SFINX uses peptide counts instead of spectral counts (Supplementary Figure 1A), SFINX with peptide counts versus spectral counts (Supplementary Figure 1B), overlap of the interactions detected by the different filter techniques (Supplementary Figure 2), benchmarking visualized in broader range (Supplementary Figure 3), benchmarking visualized with confidence areas (Supplementary Figure 4), benchmarking of the HGSCore in comparison with the other techniques on TIP49 data (Supplementary Figure 5), and numeric performance increases of SFINX over alternative techniques (Supplementary Table S1). (PDF)
GO terms of benchmarkings (Supplementary Table S2). (TXT)

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Cited By

This article is cited by 13 publications.

Surya Gupta, Kenneth Verheggen, Jan Tavernier, and Lennart Martens . Unbiased Protein Association Study on the Public Human Proteome Reveals Biological Connections between Co-Occurring Protein Pairs. Journal of Proteome Research 2017, 16 (6) , 2204-2212. https://doi.org/10.1021/acs.jproteome.6b01066
Stefano Sala, Marleen Van Troys, Sandrine Medves, Marie Catillon, Evy Timmerman, An Staes, Elisabeth Schaffner-Reckinger, Kris Gevaert, and Christophe Ampe . Expanding the Interactome of TES by Exploiting TES Modules with Different Subcellular Localizations. Journal of Proteome Research 2017, 16 (5) , 2054-2071. https://doi.org/10.1021/acs.jproteome.7b00034
Sven Eyckerman, Francis Impens, Emmy Van Quickelberghe, Noortje Samyn, Giel Vandemoortele, Delphine De Sutter, Jan Tavernier, and Kris Gevaert . Intelligent Mixing of Proteomes for Elimination of False Positives in Affinity Purification-Mass Spectrometry. Journal of Proteome Research 2016, 15 (10) , 3929-3937. https://doi.org/10.1021/acs.jproteome.6b00517
E. V. Poverennaya, O. I. Kiseleva, A. S. Ivanov, E. A. Ponomarenko. Methods of Computational Interactomics for Investigating Interactions of Human Proteoforms. Biochemistry (Moscow) 2020, 85 (1) , 68-79. https://doi.org/10.1134/S000629792001006X
Mihaela E. Sardiu, Joshua M. Gilmore, Brad D. Groppe, Arnob Dutta, Laurence Florens, Michael P. Washburn. Topological scoring of protein interaction networks. Nature Communications 2019, 10 (1) https://doi.org/10.1038/s41467-019-09123-y
Bo Tian, Qiong Duan, Can Zhao, Ben Teng, Zengyou He. Reinforce: An Ensemble Approach for Inferring PPI Network from AP-MS Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2019, 16 (2) , 365-376. https://doi.org/10.1109/TCBB.2017.2705060
Kevin Titeca, Irma Lemmens, Jan Tavernier, Sven Eyckerman. Discovering cellular protein‐protein interactions: Technological strategies and opportunities. Mass Spectrometry Reviews 2019, 38 (1) , 79-111. https://doi.org/10.1002/mas.21574
Shaojun Long, Kevin Brown, L. Sibley. CRISPR-mediated Tagging with BirA Allows Proximity Labeling in Toxoplasma gondii. BIO-PROTOCOL 2018, 8 (6) https://doi.org/10.21769/BioProtoc.2768
Shaojun Long, Bryan Anthony, Lisa L. Drewry, L. David Sibley. A conserved ankyrin repeat-containing protein regulates conoid stability, motility and cell invasion in Toxoplasma gondii. Nature Communications 2017, 8 (1) https://doi.org/10.1038/s41467-017-02341-2
Pieter Meysman, Kevin Titeca, Sven Eyckerman, Jan Tavernier, Bart Goethals, Lennart Martens, Dirk Valkenborg, Kris Laukens. Protein complex analysis: From raw protein lists to protein interaction networks. Mass Spectrometry Reviews 2017, 36 (5) , 600-614. https://doi.org/10.1002/mas.21485
Kevin Titeca, Pieter Meysman, Kris Laukens, Lennart Martens, Jan Tavernier, Sven Eyckerman, . sfinx: an R package for the elimination of false positives from affinity purification–mass spectrometry datasets. Bioinformatics 2017, 33 (12) , 1902-1904. https://doi.org/10.1093/bioinformatics/btx076
Kevin Titeca, Emmy Van Quickelberghe, Noortje Samyn, Delphine De Sutter, Annick Verhee, Kris Gevaert, Jan Tavernier, Sven Eyckerman. Analyzing trapped protein complexes by Virotrap and SFINX. Nature Protocols 2017, 12 (5) , 881-898. https://doi.org/10.1038/nprot.2017.014
Sven Eyckerman, Kevin Titeca, Emmy Van Quickelberghe, Eva Cloots, Annick Verhee, Noortje Samyn, Leentje De Ceuninck, Evy Timmerman, Delphine De Sutter, Sam Lievens, Serge Van Calenbergh, Kris Gevaert, Jan Tavernier. Trapping mammalian protein complexes in viral particles. Nature Communications 2016, 7 (1) https://doi.org/10.1038/ncomms11416

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SFINX: Straightforward Filtering Index for Affinity Purification–Mass Spectrometry Data Analysis

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