JSM 2004 - Toronto

Abstract #300117

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Activity Number: 363
Type: Invited
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #300117
Title: Statistical Methods for Detecting RNA Splice Sites
Author(s): Mark Segal*+
Companies: University of California, San Francisco
Address: , , ,
Keywords: mRNA splicing ; random forests ; classification ; penalization
Abstract:

Pre-messenger RNA splicing, whereby introns are precisely removed from primary transcripts, is an essential step in the expression of most eukaryotic genes, especially in humans. While a great deal of knowledge has been accumulated on splicing factors, much less is known about the nature of pre-mRNA sequences that are recognized by the nuclear splicing machinery for selection of splice sites. We pursue such characterization by utilizing a large dataset of real and decoy 5' and 3' splice sites, partitioned into training (189422) and test (94727) sequences. We apply classification procedures, including random forests and penalized techniques, and introduce some context-specific refinements. Comparisons with previously used maximum entropy modeling are drawn. Methods are contrasted with respect to predictive performance and interpretative yield.

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Revised March 2004