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Bottom up proteomics data analysis strategies to explore protein modifications and genomic variant

2015-06Journal article Restricted access
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dc.contributor.authorCarvalho, Ana
dc.contributor.authorMatthiesen, Rune
dc.contributor.authorPenque, Deborah
dc.date.accessioned2016-02-22T17:27:29Z
dc.date.available2018-01-01T01:30:10Z
dc.date.issued2015-06
dc.description.abstractThe quest to understand biological systems requires further attention of the scientific community to the challenges faced in proteomics. In fact the complexity of the proteome reaches uncountable orders of magnitudes. This means that significant technical and data-analytic innovations will be needed for the full understanding of biology. Current state of art mass spectrometry (MS) is probably our best choice for studying protein complexity and exploring new ways to use MS and MS derived data should be given higher priority. We present here a brief overview of visualization and statistical analyzes strategies for quantitative peptide values on an individual protein basis. These analysis strategies can help pinpoint protein modifications, splice and genomic variants of biological relevance. We demonstrated the application of these data analysis strategies using a bottom-up proteomics data set obtained in a drug profiling experiment. Furthermore, we have also observed that the presented methods are useful for studying peptide distributions from clinical proteomics samples from a large number of individuals. We expect that the presented data analysis strategy will be useful in the future to define functional protein variants in biological model systems and disease studies. Therefore robust software implementing these strategies is urgently needed.pt_PT
dc.description.sponsorshipR.M. is supported by EXPL/DTP-PIC/0616/2013 Fundação para a Ciência e a Tecnologia (FCT) and FCT investigator program 2012. A.S.C. is supported by post-doctoral grant reference SFRH / BPD / 85569 / 2012 funded by FCT.pt_PT
dc.identifier.citationProteomics. 2015 Jun;15(11):1789-92. doi: 10.1002/pmic.201400186. Epub 2015 Mar 30.pt_PT
dc.identifier.doi10.1002/pmic.201400186pt_PT
dc.identifier.issn1615-9853
dc.identifier.urihttp://hdl.handle.net/10400.18/3458
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherWiley-VCH Verlagpt_PT
dc.relation.publisherversionhttp://onlinelibrary.wiley.com/doi/10.1002/pmic.201400186/fullpt_PT
dc.subjectBioinformaticspt_PT
dc.subjectComputational MSpt_PT
dc.subjectData Visualizationpt_PT
dc.subjectProteoformspt_PT
dc.subjectPeptide Quantitationpt_PT
dc.subjectProteogenomicspt_PT
dc.subjectProteómicapt_PT
dc.subjectGenómica Funcionalpt_PT
dc.subjectGenómica Funcional e Estruturalpt_PT
dc.titleBottom up proteomics data analysis strategies to explore protein modifications and genomic variantpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage1792pt_PT
oaire.citation.startPage1789pt_PT
oaire.citation.titleProteomicspt_PT
oaire.citation.volume15(11)pt_PT
rcaap.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT

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