Metaprotein expression modeling for label-free quantitative proteomics.

ABSTRACT:

Background

Label-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.

Results

We describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a s

SUBMITTER: Lucas JE 

PROVIDER: S-EPMC3436780 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature