Integrating shotgun proteomics and mRNA expression data to improve protein identification

Smriti R. Ramakrishnan, Christine Vogel, John T. Prince, Zhihua Li, Luiz O. Penalva, Margaret Myers, Edward M. Marcotte, Daniel P. Miranker, Rong Wang

Producción científica: Articlerevisión exhaustiva

56 Citas (Scopus)

Resumen

Motivation: Tandem mass spectrometry (MS/MS) offers fast and reliable characterization of complex protein mixtures, but suffers from low sensitivity in protein identification. In a typical shotgun proteomics experiment, it is assumed that all proteins are equally likely to be present. However, there is often other information available, e.g. the probability of a protein's presence is likely to correlate with its mRNA concentration. Results: We develop a Bayesian score that estimates the posterior probability of a protein's presence in the sample given its identification in an MS/MS experiment and its mRNA concentration measured under similar experimental conditions. Our method, MSpresso, substantially increases the number of proteins identified in an MS/MS experiment at the same error rate, e.g. in yeast, MSpresso increases the number of proteins identified by ∼40%. We apply MSpresso to data from different MS/MS instruments, experimental conditions and organisms (Escherichia coli, human), and predict 19-63% more proteins across the different datasets. MSpresso demonstrates that incorporating prior knowledge of protein presence into shotgun proteomics experiments can substantially improve protein identification scores.

Idioma originalEnglish (US)
Páginas (desde-hasta)1397-1403
Número de páginas7
PublicaciónBioinformatics
Volumen25
N.º11
DOI
EstadoPublished - jun 2009

ASJC Scopus subject areas

  • Computational Mathematics
  • Molecular Biology
  • Biochemistry
  • Statistics and Probability
  • Computer Science Applications
  • Computational Theory and Mathematics

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