The evolutionary computation approach to motif discovery in biological sequences
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
TFBS identification by position- and consensus-led genetic algorithm with local filtering
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Towards Interactive Visualization for Exploring Conserved Motifs in Noncoding DNA Sequence
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
Motif discovery using multi-objective genetic algorithm in biosequences
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
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The human genome is approximately 3 billion basepairs long. An estimated 2--3% of DNA codes for genes; the remaining 97--98% is noncoding DNA [11]. Although the noncoding regions in DNA were once called "junk DNA" (with the assumption that these regions were not serving a purpose) it is now understood that within noncoding DNA are functional regions that affect the expression of genes [34]. However, we are still far from understanding the breadth of function in the noncoding regions, and identification of functional elements is a complex problem, difficult to study in vitro because of the enormous number of possibilities. In this project, we are searching in silico for candidate functional elements in noncoding DNA. These candidates will then be studied at the bench to assess function. Our guiding principle is that regions of noncoding DNA that have been conserved across evolutionary time are good candidates as functional elements; we use a genetic algorithms approach to search for these candidate elements, called motifs. Our system is thus called GAMI, Genetic Algorithms for Motif Inference. GAMI has been demonstrated to be a successful approach to this task, as will be described below.