Identifying Splicing Regulatory Elements with de Bruijn Graphs

Abstract

Splicing regulatory elements (SREs) are short, degenerate sequences on pre-mRNA molecules that enhance or inhibit the splicing process via the binding of splicing factors, proteins that regulate the functioning of the spliceosome. Existing methods for identifying SREs in a genome are either experimental or computational. Here, we propose a formalism based on de Bruijn graphs that combines genomic structure, word count enrichment analysis, and experimental evidence to identify SREs found in exons. In our approach, SREs are not restricted to a fixed length (i.e., k-mers, for a fixed k). As a result, we identify 2001 putative exonic enhancers and 3080 putative exonic silencers for human genes, with lengths varying from 6 to 15 nucleotides. Many of the predicted SREs overlap with experimentally verified binding sites. Our model provides a novel method to predict variable length putative regulatory elements computationally for further experimental investigation.

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

“…The output of this stage is three sets of differentially used exons in the tissue of interest but not in the remaining tissues. We then apply GenSRE [21] separately on the exon sets to identify exonic enhancers. We identify tissue-specific exonic enhancers by determining the common ESEs across the sets.…”

“…We utilized a formalism based on de Bruijn graphs that we previously reported [21]. It combines genomic structure, word count enrichment analysis, and experimental evidence to identify enhancers found in exons [21].…”

“…However, the focus was only on individual regulatory elements as well. There are other studies that identify individual SREs in both exonic and intronic regions [21–28], which we use for comparing our results. However, the focus in these studies was not on tissue-specific SREs.…”

“…However, the focus in these studies was not on tissue-specific SREs. For more detailed review about these approaches, please refer to our previous paper [21]. …”

“…We used DEXSeq [30] to identify differential exons across tissues. Then, we applied our algorithms, GenSRE [21] and CoSREM [31], to identify both individual and combinatorial exonic regulatory elements responsible for exons that exist in one tissue but not in other tissues. Putative tissue-specific exonic enhancers were discovered and a complicated exonic enhancer regulatory network was revealed.…”