ArticleJournal of computational biology : a journal of computational molecular cell biology2014
Identifying splicing regulatory elements with de Bruijn graphs.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Article
- Computational Identification of Tissue-Specific Splicing Regulatory Elements in Human Genes from RNA-Seq Data.PloS one · 2016Article
- CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements.BMC bioinformatics · 2015Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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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.
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