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

• Autor(a) principal: Carvalho, Ana
• Data de Publicação: 2015
• Outros Autores: Matthiesen, Rune, Penque, Deborah
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.18/3458
• topic: Bioinformatics; Computational MS; Data Visualization; Proteoforms; Peptide Quantitation; Proteogenomics; Proteómica; Genómica Funcional; Genómica Funcional e Estrutural

The 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.