Model-based detection of alternative splicing signals.

ABSTRACT:

Motivation

Transcripts from approximately 95% of human multi-exon genes are subject to alternative splicing (AS). The growing interest in AS is propelled by its prominent contribution to transcriptome and proteome complexity and the role of aberrant AS in numerous diseases. Recent technological advances enable thousands of exons to be simultaneously profiled across diverse cell types and cellular conditions, but require accurate identification of condition-specific splicing changes. It is necessary to accurately identify such splicing changes to elucidate the underlying regulatory programs or link the splicing changes to specific diseases.

Results

We present a probabilistic model tailored for high-throughput AS data, where observed isoform levels are explained as combination

SUBMITTER: Barash Y 

PROVIDER: S-EPMC2881385 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature