Computational Identification of Tissue-Specific Splicing Regulatory Elements in Human Genes from RNA-Seq Data

Abstract

Alternative splicing is a vital process for regulating gene expression and promoting proteomic diversity. It plays a key role in tissue-specific expressed genes. This specificity is mainly regulated by splicing factors that bind to specific sequences called splicing regulatory elements (SREs). Here, we report a genome-wide analysis to study alternative splicing on multiple tissues, including brain, heart, liver, and muscle. We propose a pipeline to identify differential exons across tissues and hence tissue-specific SREs. In our pipeline, we utilize the DEXSeq package along with our previously reported algorithms. Utilizing the publicly available RNA-Seq data set from the Human BodyMap project, we identified 28,100 differentially used exons across the four tissues. We identified tissue-specific exonic splicing enhancers that overlap with various previously published experimental and computational databases. A complicated exonic enhancer regulatory network was revealed, where multiple exonic enhancers were found across multiple tissues while some were found only in specific tissues. Putative combinatorial exonic enhancers and silencers were discovered as well, which may be responsible for exon inclusion or exclusion across tissues. Some of the exonic enhancers are found to be co-occurring with multiple exonic silencers and vice versa, which demonstrates a complicated relationship between tissue-specific exonic enhancers and silencers.

Fig 1. An example of the proposed pipeline applied on the brain tissue.

Fig 2. Brain-unique exons.

Each circle represents the number of brain-specific exons that resulted from brain pairwise comparisons with other tissues (heart, liver, and muscle). The intersection represents brain-unique exons against all other tissues.

Fig 3. Tissue-specific ESE regulatory network.

The circular nodes represent ESEs, and the rectangular ones represent tissues. An edge indicates an ESE contained in a tissue. The node size indicates the node degree.

Fig 4. Enhancer regulatory network that focuses on enhancers that are involved in multiple tissues.

The node size and color are proportional to its degree.

Fig 5. A regulatory network for combinatorial SREs identified in the brain tissue.

The red nodes represent ESEs, and the blue ones represent ESSs. The node size is proportional to node degree.

Fig 6. Relative expression levels for several splicing factors across the tissues from the RNA-Seq data.

Relative expression level for a splicing factor in a specific tissue is calculated by dividing its expression level value over the sum of all expression level values for this splicing factor across the tissues. This shows the different abundances of splicing factors across tissues.