Skip to main content
Journal cover image

Metaprotein expression modeling for label-free quantitative proteomics.

Publication ,  Journal Article
Lucas, JE; Thompson, JW; Dubois, LG; McCarthy, J; Tillmann, H; Thompson, A; Shire, N; Hendrickson, R; Dieguez, F; Goldman, P; Schwarz, K ...
Published in: BMC Bioinformatics
May 4, 2012

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 strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C. CONCLUSIONS: Mass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Duke Scholars

Published In

BMC Bioinformatics

DOI

EISSN

1471-2105

Publication Date

May 4, 2012

Volume

13

Start / End Page

74

Location

England

Related Subject Headings

  • Tandem Mass Spectrometry
  • Reproducibility of Results
  • Proteomics
  • Proteome
  • Proteins
  • Peptides
  • Models, Statistical
  • Male
  • Humans
  • Hepatitis C, Chronic
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Lucas, J. E., Thompson, J. W., Dubois, L. G., McCarthy, J., Tillmann, H., Thompson, A., … Moseley, M. A. (2012). Metaprotein expression modeling for label-free quantitative proteomics. BMC Bioinformatics, 13, 74. https://doi.org/10.1186/1471-2105-13-74
Lucas, Joseph E., J Will Thompson, Laura G. Dubois, Jeanette McCarthy, Hans Tillmann, Alexander Thompson, Norah Shire, et al. “Metaprotein expression modeling for label-free quantitative proteomics.BMC Bioinformatics 13 (May 4, 2012): 74. https://doi.org/10.1186/1471-2105-13-74.
Lucas JE, Thompson JW, Dubois LG, McCarthy J, Tillmann H, Thompson A, et al. Metaprotein expression modeling for label-free quantitative proteomics. BMC Bioinformatics. 2012 May 4;13:74.
Lucas, Joseph E., et al. “Metaprotein expression modeling for label-free quantitative proteomics.BMC Bioinformatics, vol. 13, May 2012, p. 74. Pubmed, doi:10.1186/1471-2105-13-74.
Lucas JE, Thompson JW, Dubois LG, McCarthy J, Tillmann H, Thompson A, Shire N, Hendrickson R, Dieguez F, Goldman P, Schwarz K, Patel K, McHutchison J, Moseley MA. Metaprotein expression modeling for label-free quantitative proteomics. BMC Bioinformatics. 2012 May 4;13:74.
Journal cover image

Published In

BMC Bioinformatics

DOI

EISSN

1471-2105

Publication Date

May 4, 2012

Volume

13

Start / End Page

74

Location

England

Related Subject Headings

  • Tandem Mass Spectrometry
  • Reproducibility of Results
  • Proteomics
  • Proteome
  • Proteins
  • Peptides
  • Models, Statistical
  • Male
  • Humans
  • Hepatitis C, Chronic