Nucleic Acids Research Advance Access originally published online on May 15, 2008
Nucleic Acids Research 2008 36(11):3728-3737; doi:10.1093/nar/gkn233
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Nucleic Acids Research, 2008, Vol. 36, No. 11 3728-3737
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Beyond tissueInfo: functional prediction using tissue expression profile similarity searches
1HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, 2Department of Physiology and Biophysics, Weill Medical College of Cornell University, 1305 York Ave, New York, NY 10021, USA, 3Structural Bioinformatics Group (GRIB-IMIM), Universitat Pompeu Fabra, C/Doctor Aiguader, 88. Barcelona-08003, Spain and 4Department of Pharmacology, Weill Medical College of Cornell University, 1300 York Ave, New York, NY 10021, USA
*To whom correspondence should be addressed. Tel: 646 962 5613; Fax: 646 962 0383; Email: fac2003{at}med.cornell.edu
Received January 22, 2008. Revised March 31, 2008. Accepted April 11, 2008.
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.