Literature DB >> 25393830

Identifying splicing regulatory elements with de Bruijn graphs.

Eman Badr1, Lenwood S Heath.   

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.

Entities:  

Keywords:  algorithms; combinatorics; computational molecular biology; graphs and networks; literature data mining; machine learning; probability; sequences

Mesh:

Year:  2014        PMID: 25393830      PMCID: PMC4253301          DOI: 10.1089/cmb.2014.0183

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  48 in total

1.  Comparison of intron-containing and intron-lacking human genes elucidates putative exonic splicing enhancers.

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Journal:  Nucleic Acids Res       Date:  2001-04-01       Impact factor: 16.971

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3.  Detection and evaluation of intron retention events in the human transcriptome.

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4.  Global detection and identification of developmental stage specific transcripts in mouse brain using subtractive cross-screening algorithm.

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5.  Sequence information for the splicing of human pre-mRNA identified by support vector machine classification.

Authors:  Xiang H-F Zhang; Katherine A Heller; Ilana Hefter; Christina S Leslie; Lawrence A Chasin
Journal:  Genome Res       Date:  2003-12       Impact factor: 9.043

6.  Computational definition of sequence motifs governing constitutive exon splicing.

Authors:  Xiang H-F Zhang; Lawrence A Chasin
Journal:  Genes Dev       Date:  2004-05-14       Impact factor: 11.361

7.  Alternative splicing in disease and therapy.

Authors:  Mariano A Garcia-Blanco; Andrew P Baraniak; Erika L Lasda
Journal:  Nat Biotechnol       Date:  2004-05       Impact factor: 54.908

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Authors:  Olivier Baris; Cécile Delettre; Patrizia Amati-Bonneau; Marie-Odile Surget; Jean-François Charlin; Antoine Catier; Laurence Derieux; Jean-Laurent Guyomard; Hélène Dollfus; Philippe Jonveaux; Carmen Ayuso; Irene Maumenee; Birgit Lorenz; Shehla Mohammed; Yves Tourmen; Dominique Bonneau; Yves Malthièry; Christian Hamel; Pascal Reynier
Journal:  Hum Mutat       Date:  2003-06       Impact factor: 4.878

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Journal:  Science       Date:  2003-11-14       Impact factor: 47.728

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Authors:  R Tacke; J L Manley
Journal:  EMBO J       Date:  1995-07-17       Impact factor: 11.598

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  2 in total

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

Authors:  Eman Badr; Mahmoud ElHefnawi; Lenwood S Heath
Journal:  PLoS One       Date:  2016-11-18       Impact factor: 3.240

2.  CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements.

Authors:  Eman Badr; Lenwood S Heath
Journal:  BMC Bioinformatics       Date:  2015-09-04       Impact factor: 3.169

  2 in total

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