PGCA: An algorithm to link protein groups created from MS/MS data Article Swipe
David Kepplinger
,
Mandeep Takhar
,
Mayu Sasaki
,
Zsuzsanna Hollander
,
Derek Smith
,
Bruce M. McManus
,
W. Robert McMaster
,
Raymond T. Ng
,
Gabriela V. Cohen Freue
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0177569
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0177569
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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- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0177569
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0177569&type=printable
- OA Status
- gold
- Cited By
- 1
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2619393833
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- OpenAlex ID
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https://openalex.org/W2619393833Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0177569Digital Object Identifier
- Title
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PGCA: An algorithm to link protein groups created from MS/MS dataWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-05-31Full publication date if available
- Authors
-
David Kepplinger, Mandeep Takhar, Mayu Sasaki, Zsuzsanna Hollander, Derek Smith, Bruce M. McManus, W. Robert McMaster, Raymond T. Ng, Gabriela V. Cohen FreueList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0177569Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0177569&type=printableDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0177569&type=printableDirect OA link when available
- Concepts
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Computer science, Identifier, Proteomics, Shotgun proteomics, Algorithm, Tandem mass spectrometry, Set (abstract data type), Workflow, Computational biology, Data mining, Bioinformatics, Mass spectrometry, Biology, Chemistry, Database, Chromatography, Genetics, Gene, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2018: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.However, | 123 |
| abstract_inverted_index.analysis | 245 |
| abstract_inverted_index.decades, | 13 |
| abstract_inverted_index.evidence | 169, 187 |
| abstract_inverted_index.factors: | 142 |
| abstract_inverted_index.handling | 16 |
| abstract_inverted_index.identity | 64 |
| abstract_inverted_index.matching | 113 |
| abstract_inverted_index.multiple | 132, 223 |
| abstract_inverted_index.peptides | 91, 118 |
| abstract_inverted_index.problem, | 101 |
| abstract_inverted_index.process, | 79 |
| abstract_inverted_index.proteins | 3, 67, 171, 192 |
| abstract_inverted_index.readout. | 29 |
| abstract_inverted_index.relevant | 288 |
| abstract_inverted_index.removing | 18 |
| abstract_inverted_index.software | 106 |
| abstract_inverted_index.solution | 125 |
| abstract_inverted_index.spectra. | 96 |
| abstract_inverted_index.studies. | 256 |
| abstract_inverted_index.Algorithm | 217 |
| abstract_inverted_index.achieving | 24 |
| abstract_inverted_index.algorithm | 236 |
| abstract_inverted_index.ambiguous | 78 |
| abstract_inverted_index.analyses. | 208 |
| abstract_inverted_index.biomarker | 278 |
| abstract_inverted_index.candidate | 289 |
| abstract_inverted_index.connected | 232 |
| abstract_inverted_index.different | 203, 299 |
| abstract_inverted_index.difficult | 76 |
| abstract_inverted_index.digestion | 35 |
| abstract_inverted_index.discovery | 286 |
| abstract_inverted_index.extensive | 19 |
| abstract_inverted_index.peptides, | 41 |
| abstract_inverted_index.plausible | 110 |
| abstract_inverted_index.separated | 45 |
| abstract_inverted_index.sequenced | 50 |
| abstract_inverted_index.stability | 264 |
| abstract_inverted_index.workflow, | 61 |
| abstract_inverted_index.algorithm, | 212 |
| abstract_inverted_index.biomarkers | 255 |
| abstract_inverted_index.challenges | 128 |
| abstract_inverted_index.conclusive | 186 |
| abstract_inverted_index.connection | 243 |
| abstract_inverted_index.identified | 92 |
| abstract_inverted_index.identifier | 85 |
| abstract_inverted_index.illustrate | 258 |
| abstract_inverted_index.introduces | 126 |
| abstract_inverted_index.originally | 68 |
| abstract_inverted_index.popularity | 9 |
| abstract_inverted_index.processing | 105 |
| abstract_inverted_index.proteomics | 6 |
| abstract_inverted_index.recovering | 62 |
| abstract_inverted_index.relatively | 26 |
| abstract_inverted_index.separation | 21 |
| abstract_inverted_index.subsequent | 206 |
| abstract_inverted_index.supporting | 168 |
| abstract_inverted_index.throughput | 28 |
| abstract_inverted_index.composition | 157 |
| abstract_inverted_index.identifiers | 112, 146 |
| abstract_inverted_index.inexpensive | 239 |
| abstract_inverted_index.procedures, | 17 |
| abstract_inverted_index.simplifying | 14 |
| abstract_inverted_index.statistical | 207 |
| abstract_inverted_index.compositions | 297 |
| abstract_inverted_index.experimental | 133, 224, 272 |
| abstract_inverted_index.quantitation | 1 |
| abstract_inverted_index.spectrometry | 54 |
| abstract_inverted_index.characterized | 138 |
| abstract_inverted_index.non-identical | 296 |
| abstract_inverted_index.chromatography | 48 |
| abstract_inverted_index.identification | 100, 260 |
| abstract_inverted_index.computationally | 238 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5010334833 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I141945490 |
| citation_normalized_percentile.value | 0.47647783 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |