Mining Proteomics Data for Novel Biological Insights

Abstract

There is now a substantial body of proteomic mass spectrometry data available in public data repositories [1], providing valuable information about the expression of proteins in a wide range of species, in various tissue types, and under different conditions. However, whereas hundreds of research group reanalyze genomes every day, the public proteomic data has yet to be extensively mined [2]. Our hypothesis is that this data harbours valuable biological infromation, and this project seeks to mine some of this information from the data using existing and novel bioinformatics approaches.




References:
[1]

Perez-Riverol, Y., et al., Making proteomics data accessible and reusable: current state of proteomics databases and repositories. Proteomics, 2015. 15(5-6): p. 930-49.

[2]

Martens, L. Public proteomics data: How the field has evolved from sceptical inquiry to the promise of in silico proteomics. EuPA Open Proteomics, 2016. 11, 42-44.Martens, L. Public proteomics data: How the field has evolved from sceptical inquiry to the promise of in silico proteomics. EuPA Open Proteomics, 2016. 11, 42-44.

[3]

Evans, V.C., et al., De novo derivation of proteomes from transcriptomes for transcript and protein identification. Nat Methods, 2012. 9(12): p. 1207-11.

[4]

Fan, J., et al., Galaxy Integrated Omics: Web-based Standards-Compliant Workflows for Proteomics Informed by Transcriptomics. Mol Cell Proteomics, 2015. 14(11): p. 3087-93.


Biological Areas:

Genetics

BBSRC Area:

Genes, development and STEM approaches to biology