Beyond TissueInfo: Functional Prediction Using Tissue Expression Profile Similarity Searches

1. Beyond TissueInfo: Functional Prediction Using Tissue Expression Profile Similarity Searches Fabien Campagne, Ph.D. Institute for Computational Biomedicine and Dept of Physiology and Biophysics, Weill Medical College of Cornell University New York, USA Rocky Mountains Bioinformatics 2007

2. Tissue Expression Profile Similarity Searches TEPSS assumes that functionally related transcripts are co-expressed in subsets of tissues Expression measured with expressed sequence tags (dbEST and TissueInfo) The TEPSS approach formalizes similarity searches in tissue expression space and offers a computationally efficient solution See http://icb.med.cornell.edu/crt/tepss/ Hint: Google for ‘TEPSS’

3. 50,000 human protein-protein interactions High-quality interactions only (~1,000) TEPSS scores discriminate interacting protein pairs from non-interacting pairs

4. Combining signals from multiple transcripts Validation with leave-one-out lift curves TEPSS used with multiple transcripts is a flexible and effective candidate prediction approach TEPSS prioritizes S-nitrosylation protein targets TEPSS prioritizes members of the cytosolic ribosome Beyond TissueInfo: Functional Prediction using Tissue Expression Profile Similarity SearchesDaniel Aguilar, Lucy Skrabanek, Steven S. Gross, BaldomeroOliva, Fabien Campagne. Submitted for publication

5. Ribosome screen: top predictions

6. Predicting targets of S-nitrosylation Protein post-translational modification by nitric oxyde Training set from: Hao G, Derakhshan B, Shi L, Campagne F, Gross SS. SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures. PNAS 2006

7. Summary and conclusions Tissue Expression Profile Similarity Searches (TEPSS) • TEPSS scores can discriminate pairs of proteins reported to interact from pairs of proteins not reported to interact. • TEPSS effectively prioritizes members of the ribosome and S-nitrosylation (SNO) protein targets, in whole genome screens. • Approach predicts non-trivial members of cytosolic ribosome • Application to S-nitrosylation protein targets suggests candidates for experimental validation • TEPSS does not use sequence similarity and thus can be used in complement to methods that do. • Open-source, seehttp://icb.med.cornell.edu/crt/tepss/ for programs, source code, and data.

8. Acknowledgments Weill Medical College of Cornell University Dept. of Pharmacology Gang Hao Steven S. Gross Institute for Computational Biomedicine Lucy Skrabanek (EST data curation) Universitat Pompeu Fabra, Barcelona, Spain Instituto Municipal de Investigación Medica Daniel AguilarBaldo Oliva