Sequence motif finder using memetic algorithm

• DOI: 10.1186/s12859-017-2005-1
• Publication Date: 2018-01-03T05:35:16Z
• Authors: Jader M. Caldonaz...; André Y. Kashiwabara; Danilo S. Sanches
• Keywords: Heuristics; Motif (music); DNA binding site; Sequence motif; Memetic algorithm

De novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it an important problem in Bioinformatics. The correct recognition TFBS plays role understanding the mechanisms gene regulation helps to develop new drugs.We here present Memetic Framework for Motif Discovery (MFMD), algorithm that uses semi-greedy constructive heuristics as local optimizer. In addition, we used hybridization classic genetic global optimizer refine solutions initially found. MFMD can find classify overrepresented patterns DNA sequences predict their respective initial positions. performance was assessed ChIP-seq data retrieved from JASPAR site, promoter extracted ABS artificially generated synthetic data. evaluated compared with well-known approaches literature, called MEME Gibbs Sampler, achieving higher f-score most datasets this work.We have developed approach detecting motifs biopolymers sequences. freely available software be promising alternative development tools de motif discovery. Its open-source downloaded at https://github.com/jadermcg/mfmd .