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PGCA: An algorithm to link protein groups created from MS/MS data
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 p...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451011/ https://www.ncbi.nlm.nih.gov/pubmed/28562641 http://dx.doi.org/10.1371/journal.pone.0177569 |
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author | Kepplinger, David Takhar, Mandeep Sasaki, Mayu Hollander, Zsuzsanna Smith, Derek McManus, Bruce McMaster, W. Robert Ng, Raymond T. Cohen Freue, Gabriela V. |
author_facet | Kepplinger, David Takhar, Mandeep Sasaki, Mayu Hollander, Zsuzsanna Smith, Derek McManus, Bruce McMaster, W. Robert Ng, Raymond T. Cohen Freue, Gabriela V. |
author_sort | Kepplinger, David |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5451011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54510112017-06-12 PGCA: An algorithm to link protein groups created from MS/MS data Kepplinger, David Takhar, Mandeep Sasaki, Mayu Hollander, Zsuzsanna Smith, Derek McManus, Bruce McMaster, W. Robert Ng, Raymond T. Cohen Freue, Gabriela V. PLoS One Research Article 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. Public Library of Science 2017-05-31 /pmc/articles/PMC5451011/ /pubmed/28562641 http://dx.doi.org/10.1371/journal.pone.0177569 Text en © 2017 Kepplinger et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kepplinger, David Takhar, Mandeep Sasaki, Mayu Hollander, Zsuzsanna Smith, Derek McManus, Bruce McMaster, W. Robert Ng, Raymond T. Cohen Freue, Gabriela V. PGCA: An algorithm to link protein groups created from MS/MS data |
title | PGCA: An algorithm to link protein groups created from MS/MS data |
title_full | PGCA: An algorithm to link protein groups created from MS/MS data |
title_fullStr | PGCA: An algorithm to link protein groups created from MS/MS data |
title_full_unstemmed | PGCA: An algorithm to link protein groups created from MS/MS data |
title_short | PGCA: An algorithm to link protein groups created from MS/MS data |
title_sort | pgca: an algorithm to link protein groups created from ms/ms data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451011/ https://www.ncbi.nlm.nih.gov/pubmed/28562641 http://dx.doi.org/10.1371/journal.pone.0177569 |
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