Beyond tissueInfo: functional prediction using tissue expression profile similarity searches. Academic Article uri icon

Overview

abstract

  • We present and validate tissue expression profile similarity searches (TEPSS), a computational approach to identify transcripts that share similar tissue expression profiles to one or more transcripts in a group of interest. We evaluated TEPSS for its ability to discriminate between pairs of transcripts coding for interacting proteins and non-interacting pairs. We found that ordering protein-protein pairs by TEPSS score produces sets significantly enriched in reported pairs of interacting proteins [interacting versus non-interacting pairs, Odds-ratio (OR) = 157.57, 95% confidence interval (CI) (36.81-375.51) at 1% coverage, employing a large dataset of about 50 000 human protein interactions]. When used with multiple transcripts as input, we find that TEPSS can predict non-obvious members of the cytosolic ribosome. We used TEPSS to predict S-nitrosylation (SNO) protein targets from a set of brain proteins that undergo SNO upon exposure to physiological levels of S-nitrosoglutathione in vitro. While some of the top TEPSS predictions have been validated independently, several of the strongest SNO TEPSS predictions await experimental validation. Our data indicate that TEPSS is an effective and flexible approach to functional prediction. Since the approach does not use sequence similarity, we expect that TEPSS will be useful for various gene discovery applications. TEPSS programs and data are distributed at http://icb.med.cornell.edu/crt/tepss/index.xml.

publication date

  • May 15, 2008

Research

keywords

  • Computational Biology
  • Expressed Sequence Tags
  • Gene Expression Profiling
  • Proteins

Identity

PubMed Central ID

  • PMC2441795

Scopus Document Identifier

  • 46349098811

Digital Object Identifier (DOI)

  • 10.1093/nar/gkn233

PubMed ID

  • 18483083

Additional Document Info

volume

  • 36

issue

  • 11