Description:

For splice site recognition, one has to solve two classification problems: discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems typically rely on Markov Chains to solve these tasks. In this work we consider Support Vector Machines for splice site recognition. We employ the so-called weighted degree kernel which turns out well suited for this task, as we will illustrate in several experiments where we compare its prediction accuracy with that of recently proposed systems. We apply our method to the genome-wide recognition of splice sites in Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, and Homo sapiens. Our performance estimates indicate that splice sites can be recognized very accurately in these genomes and that our method outperforms many other methods including Markov Chains, GeneSplicer and SpliceMachine. We provide genome-wide predictions of splice sites and a stand-alone prediction tool ready to be used for incorporation in a gene finder.

Changes to previous version:

Asp now supports three formats:

-g fname for gff format

-s fname for spf format

-b dir for a binary format compatible with mGene.

And a new switch

-t which switches on a sigmoid-based transformation of the svm scores to get scores between 0 and 1.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Cygwin, Linux, Macosx, Posix

Data Formats: Fasta

Tags: Bioinformatics, Shogun, Large Scale Learning

Archive: download here

Other available revisons

Version	Changelog	Date
0.3	Asp now supports three formats: -g fname for gff format -s fname for spf format -b dir for a binary format compatible with mGene. And a new switch -t which switches on a sigmoid-based transformation of the svm scores to get scores between 0 and 1.	May 7, 2010, 10:25:39
0.2	Initial Announcement on mloss.org.	May 25, 2009, 10:06:19

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