Network-based inference from complex proteomic mixtures using SNIPE

• DOI: 10.1093/bioinformatics/bts594
• Publication Date: 2012-10-12T00:24:35Z
• Authors: David P. Nusinow; Adam Kiezun; Daniel J. O’Connell; Joel M. Chick; Yingzi Yue; Richard L. Maas; Steven P. Gygi; Shamil R. Sunyaev
• Keywords: Proteomics; 0301 basic medicine; Mice; 03 medical and health sciences; Proteome; Animals; Computational Biology; Tooth; Algorithms; Software

Proteomics presents the opportunity to provide novel insights about global biochemical state of a tissue. However, significant problem with current methods is that shotgun proteomics has limited success at detecting many low abundance proteins, such as transcription factors from complex mixtures cells and tissues. The ability assay for these proteins in context entire proteome would be useful areas experimental biology.We used network-based inference an approach named SNIPE (Software Network Inference Experiments) selectively highlights are more likely active but otherwise undetectable proteomic sample. integrates spectral counts paired case-control samples over network neighbourhood assesses statistical likelihood enrichment by permutation test. As initial application, was able select several required early murine tooth development. Multiple lines additional evidence confirm can uncover previously unreported this system. We conclude enhance utility data facilitate study poorly detected mixtures.An implementation R computing environment snipeR been made freely available http://genetics.bwh.harvard.edu/snipe/[e-mail] Bioinformatics online.