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DC Field | Value | Language |
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dc.citation.endPage | 2961 | - |
dc.citation.number | 22 | - |
dc.citation.startPage | 2955 | - |
dc.citation.title | BIOINFORMATICS | - |
dc.citation.volume | 25 | - |
dc.contributor.author | Ramakrishnan, Smriti R. | - |
dc.contributor.author | Vogel, Christine | - |
dc.contributor.author | Kwon, Taejoon | - |
dc.contributor.author | Penalva, Luiz O. | - |
dc.contributor.author | Marcotte, Edward M. | - |
dc.contributor.author | Miranker, Daniel P. | - |
dc.date.accessioned | 2023-12-22T07:37:58Z | - |
dc.date.available | 2023-12-22T07:37:58Z | - |
dc.date.created | 2015-08-04 | - |
dc.date.issued | 2009-11 | - |
dc.description.abstract | Motivation: High-throughput protein identification experiments based on tandem mass spectrometry (MS/MS) often suffer from low sensitivity and low-confidence protein identifications. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other evidence to suggest that a protein is present and confidence in individual protein identification can be updated accordingly. Results: We develop a method that analyzes MS/MS experiments in the larger context of the biological processes active in a cell. Our method, MSNet, improves protein identification in shotgun proteomics experiments by considering information on functional associations from a gene functional network. MSNet substantially increases the number of proteins identified in the sample at a given error rate. We identify 8-29% more proteins than the original MS experiment when applied to yeast grown in different experimental conditions analyzed on different MS/MS instruments, and 37% more proteins in a human sample. We validate up to 94% of our identifications in yeast by presence in ground-truth reference sets | - |
dc.identifier.bibliographicCitation | BIOINFORMATICS, v.25, no.22, pp.2955 - 2961 | - |
dc.identifier.doi | 10.1093/bioinformatics/btp461 | - |
dc.identifier.issn | 1367-4803 | - |
dc.identifier.scopusid | 2-s2.0-70449361857 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/13337 | - |
dc.identifier.url | http://bioinformatics.oxfordjournals.org/content/25/22/2955 | - |
dc.identifier.wosid | 000271564300009 | - |
dc.language | 영어 | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.title.alternative | Mining gene functional networks to improve mass-spectrometry-based protein identification | - |
dc.title | Mining gene functional networks to improve mass-spectrometry-based protein identification | - |
dc.type | Article | - |
dc.description.journalRegisteredClass | scopus | - |
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