Journal article
Metaprotein expression modeling for label-free quantitative proteomics
JE Lucas, JW Thompson, LG Dubois, J McCarthy, H Tillmann, A Thompson, N Shire, R Hendrickson, F Dieguez, P Goldman, K Schwarz, K Patel, J McHutchison, MA Moseley
BMC Bioinformatics | BMC | Published : 2012
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 ..
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Awarded by National Institutes of Health
Funding Acknowledgements
Supported in part by Duke University's CTSA grant 1 UL1 RR024128-01 from NCRR/NIH. Supported in part by a gift from David H. Murdock. We gratefully acknowledge Waters Corporation and Rosetta Biosoftware, Inc for hardware and software support for the data presented in this manuscript. In addition, we would like to acknowledge the PEDS C Clinical Research Network, the NIDDK grant U01-DK-067767 and Roche Pharmaceuticals, Inc for the collection of the pediatric HCV samples.