Literature DB >> 28562641

PGCA: An algorithm to link protein groups created from MS/MS data.

David Kepplinger1, Mandeep Takhar2, Mayu Sasaki2, Zsuzsanna Hollander2, Derek Smith3, Bruce McManus2, W Robert McMaster4, Raymond T Ng2,5, Gabriela V Cohen Freue1.   

Abstract

The quantitation of proteins using shotgun proteomics has gained popularity in the last decades, simplifying sample handling procedures, removing extensive protein separation steps and achieving a relatively high throughput readout. The process starts with the digestion of the protein mixture into peptides, which are then separated by liquid chromatography and sequenced by tandem mass spectrometry (MS/MS). At the end of the workflow, recovering the identity of the proteins originally present in the sample is often a difficult and ambiguous process, because more than one protein identifier may match a set of peptides identified from the MS/MS spectra. To address this identification problem, many MS/MS data processing software tools combine all plausible protein identifiers matching a common set of peptides into a protein group. However, this solution introduces new challenges in studies with multiple experimental runs, which can be characterized by three main factors: i) protein groups' identifiers are local, i.e., they vary run to run, ii) the composition of each group may change across runs, and iii) the supporting evidence of proteins within each group may also change across runs. Since in general there is no conclusive evidence about the absence of proteins in the groups, protein groups need to be linked across different runs in subsequent statistical analyses. We propose an algorithm, called Protein Group Code Algorithm (PGCA), to link groups from multiple experimental runs by forming global protein groups from connected local groups. The algorithm is computationally inexpensive and enables the connection and analysis of lists of protein groups across runs needed in biomarkers studies. We illustrate the identification problem and the stability of the PGCA mapping using 65 iTRAQ experimental runs. Further, we use two biomarker studies to show how PGCA enables the discovery of relevant candidate protein group markers with similar but non-identical compositions in different runs.

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Year:  2017        PMID: 28562641      PMCID: PMC5451011          DOI: 10.1371/journal.pone.0177569

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  22 in total

Review 1.  Protein identification by mass spectrometry: issues to be considered.

Authors:  Michael A Baldwin
Journal:  Mol Cell Proteomics       Date:  2003-11-06       Impact factor: 5.911

2.  The International Protein Index: an integrated database for proteomics experiments.

Authors:  Paul J Kersey; Jorge Duarte; Allyson Williams; Youla Karavidopoulou; Ewan Birney; Rolf Apweiler
Journal:  Proteomics       Date:  2004-07       Impact factor: 3.984

3.  TANDEM: matching proteins with tandem mass spectra.

Authors:  Robertson Craig; Ronald C Beavis
Journal:  Bioinformatics       Date:  2004-02-19       Impact factor: 6.937

4.  DBParser: web-based software for shotgun proteomic data analyses.

Authors:  Xiaoyu Yang; Vijay Dondeti; Rebecca Dezube; Dawn M Maynard; Lewis Y Geer; Jonathan Epstein; Xiongfong Chen; Sanford P Markey; Jeffrey A Kowalak
Journal:  J Proteome Res       Date:  2004 Sep-Oct       Impact factor: 4.466

5.  Identification and characterization of the Sulfolobus solfataricus P2 proteome.

Authors:  Poh Kuan Chong; Phillip C Wright
Journal:  J Proteome Res       Date:  2005 Sep-Oct       Impact factor: 4.466

6.  Reporting protein identification data: the next generation of guidelines.

Authors:  Ralph A Bradshaw; Alma L Burlingame; Steven Carr; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2006-05       Impact factor: 5.911

7.  The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra.

Authors:  Ignat V Shilov; Sean L Seymour; Alpesh A Patel; Alex Loboda; Wilfred H Tang; Sean P Keating; Christie L Hunter; Lydia M Nuwaysir; Daniel A Schaeffer
Journal:  Mol Cell Proteomics       Date:  2007-05-27       Impact factor: 5.911

8.  ProteinLasso: A Lasso regression approach to protein inference problem in shotgun proteomics.

Authors:  Ting Huang; Haipeng Gong; Can Yang; Zengyou He
Journal:  Comput Biol Chem       Date:  2013-01-12       Impact factor: 2.877

Review 9.  Shotgun proteomics of bacterial pathogens: advances, challenges and clinical implications.

Authors:  Maja Semanjski; Boris Macek
Journal:  Expert Rev Proteomics       Date:  2016-01-11       Impact factor: 3.940

10.  X!TandemPipeline: A Tool to Manage Sequence Redundancy for Protein Inference and Phosphosite Identification.

Authors:  Olivier Langella; Benoît Valot; Thierry Balliau; Mélisande Blein-Nicolas; Ludovic Bonhomme; Michel Zivy
Journal:  J Proteome Res       Date:  2016-12-19       Impact factor: 4.466

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