Research

Studying alternative splicing regulatory networks through partial correlation analysis

Liang Chen¹ and Sika Zheng²

¹  Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, California 90089, USA

²  Howard Hughes Medical Institute, University of California, Los Angeles, MRL 6-619, Los Angeles, California 90095, USA

author email corresponding author email

Genome Biology 2009, 10:R3doi:10.1186/gb-2009-10-1-r3

Published:	9 January 2009

Subject areas: Bioinformatics, Genome studies

Abstract

Background

Alternative pre-mRNA splicing is an important gene regulation mechanism for expanding proteomic diversity in higher eukaryotes. Each splicing regulator can potentially influence a large group of alternative exons. Meanwhile, each alternative exon is controlled by multiple splicing regulators. The rapid accumulation of high-throughput data provides us with a unique opportunity to study the complicated alternative splicing regulatory network.

Results

We propose the use of partial correlation analysis to identify association links between exons and their upstream regulators or their downstream target genes (exon-gene links) and links between co-spliced exons (exon-exon links). The partial correlation analysis avoids taking the ratio of two noisy random variables, exon expression level and gene expression level, so that it achieves a higher statistical power. We named this analysis procedure pCastNet (partial Correlation analysis of splicing transcriptome Network). Through studies of known alternative exons, conservation patterns, relative positions, functional annotations, and RT-PCR experiments, we concluded that pCastNet can effectively identify exon-gene or exon-exon links. We further found that gene pairs with exon-gene or exon-exon links tend to have similar functions or are present in the same pathways. More interestingly, gene pairs with exon-gene or exon-exon links tend to share cis-elements in promoter regions and microRNA binding elements in 3' untranslated regions, which suggests the coupling of co-alternative-splicing, co-transcription-factor-binding, and co-microRNA-binding.

Conclusions

Alternative splicing regulatory networks reconstructed by pCastNet can help us better understand the coordinate and combinatorial nature of alternative splicing regulation. The proposed tool can be readily applied to other high-throughput data such as transcriptome sequencing data.