A non-independent energy-based multiple sequence alignment improves prediction of transcription factor binding sites
Author(s) -
Rafik Salama,
Dov J. Stekel
Publication year - 2013
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btt463
Subject(s) - transcription factor , multiple sequence alignment , sequence (biology) , computational biology , computer science , sequence alignment , dna binding site , factor (programming language) , energy (signal processing) , algorithm , artificial intelligence , biology , genetics , peptide sequence , promoter , mathematics , gene , statistics , gene expression , programming language
Multiple sequence alignments (MSAs) are usually scored under the assumption that the sequences being aligned have evolved by common descent. Consequently, the differences between sequences reflect the impact of insertions, deletions and mutations. However, non-coding DNA binding sequences, such as transcription factor binding sites (TFBSs), are frequently not related by common descent, and so the existing alignment scoring methods are not well suited for aligning such sequences.
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