Abstract
Understanding of transcriptional regulation through the discovery of transcription factor binding sites (TFBS) is a fundamental problem in molecular biology research. Here we propose a new computational method for motif discovery by mixing a genetic algorithm structure with several statistical coefficients. The algorithm was tested with 56 data sets from four different species. The motifs obtained were compared to the known motifs for each one of the data sets, and the accuracy in this prediction compared to 14 other methods both at nucleotide and site level. The results, though did not stand out in detection of false positives, showed a remarkable performance in most of the cases in sensitivity and in overall performance at site level, generally outperforming the other methods in these statistics, and suggesting that the algorithm can be a useful tool to successfully predict motifs in different kinds of sets of DNA sequences.
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Basha Gutierrez, J., Frith, M., Nakai, K. (2015). A Genetic Algorithm for Motif Finding Based on Statistical Significance. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2015. Lecture Notes in Computer Science(), vol 9043. Springer, Cham. https://doi.org/10.1007/978-3-319-16483-0_43
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DOI: https://doi.org/10.1007/978-3-319-16483-0_43
Publisher Name: Springer, Cham
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