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dc.contributor.authorSun, Jingen_US
dc.contributor.authorZhang, Guang Lanen_US
dc.contributor.authorLi, Siyangen_US
dc.contributor.authorIvanov, Alexander R.en_US
dc.contributor.authorFenyo, Daviden_US
dc.contributor.authorLisacek, Frederiqueen_US
dc.contributor.authorMurthy, Shashi K.en_US
dc.contributor.authorKarger, Barry L.en_US
dc.contributor.authorBrusic, Vladimiren_US
dc.date.accessioned2019-05-13T13:49:25Z
dc.date.available2019-05-13T13:49:25Z
dc.date.issued2014-12-08
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000346166300002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e74115fe3da270499c3d65c9b17d654
dc.identifier.citationJing Sun, Guang Lan Zhang, Siyang Li, Alexander R Ivanov, David Fenyo, Frederique Lisacek, Shashi K Murthy, Barry L Karger, Vladimir Brusic. 2014. "Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells - a benchmarking study." BMC GENOMICS, Volume 15, pp. ? - ? (10). https://doi.org/10.1186/1471-2164-15-S9-S1
dc.identifier.issn1471-2164
dc.identifier.urihttps://hdl.handle.net/2144/35593
dc.description.abstractBACKGROUND: Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples. RESULTS: Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs. CONCLUSIONS: Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.en_US
dc.description.sponsorshipThe cost of this publication was funded by Vladimir Brusic. (Vladimir Brusic)en_US
dc.format.extent10 p.en_US
dc.languageEnglish
dc.language.isoen_US
dc.publisherBMCen_US
dc.relation.ispartofBMC GENOMICS
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectLife sciences & biomedicineen_US
dc.subjectBiotechnology & applied microbiologyen_US
dc.subjectGenetics & heredityen_US
dc.subjectCirculating tumor-cellsen_US
dc.subjectMass-spectrometryen_US
dc.subjectCancer cellsen_US
dc.subjectResource UniProten_US
dc.subjectSingle-ceIlen_US
dc.subjectDatabaseen_US
dc.subjectSpectraen_US
dc.subjectLineen_US
dc.subjectBenchmarkingen_US
dc.subjectGene expression profilingen_US
dc.subjectHumansen_US
dc.subjectMCF-7 cellsen_US
dc.subjectProteinsen_US
dc.subjectProteomicsen_US
dc.subjectSample sizeen_US
dc.subjectBiological sciencesen_US
dc.subjectMedical and health sciencesen_US
dc.subjectInformation and computing sciencesen_US
dc.subjectBioinformaticsen_US
dc.subjectTranscriptomics dataen_US
dc.subjectGene symbolen_US
dc.subjectCanonical pathwayen_US
dc.subjectProteomics dataen_US
dc.subjectTranscript expression levelen_US
dc.titlePathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells - a benchmarking studyen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1186/1471-2164-15-S9-S1
pubs.elements-sourceweb-of-scienceen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, Metropolitan Collegeen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0001-6010-490X (Zhang, Guang Lan)
dc.identifier.mycv197123


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Except where otherwise noted, this item's license is described as Attribution 4.0 International