Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization
Machine Learning - Special issue on applications in molecular biology
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Spelling Approximate Repeated or Common Motifs Using a Suffix Tree
LATIN '98 Proceedings of the Third Latin American Symposium on Theoretical Informatics
FMGA: Finding Motifs by Genetic Algorithm
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Regulatory Motif Discovery Using a Population Clustering Evolutionary Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Motif discoveries in unaligned molecular sequences using self-organizing neural networks
IEEE Transactions on Neural Networks
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DNA motif discovery is a much explored problem in functional genomics. This paper describes a table driven greedy algorithm for discovering regulatory motifs in the promoter sequences of co-expressed genes. The proposed algorithm searches both DNA strands for the common patterns or motifs. The inputs to the algorithm are set of promoter sequences, the motif length and minimum Information Content. The algorithm generates subsequences of given length from the shortest input promoter sequence. It stores these subsequences and their reverse complements in a table. Then it searches the remaining sequences for good matches of these subsequences. The Information Content score is used to measure the goodness of the motifs. The algorithm has been tested with synthetic data and real data. The results are found promising. The algorithm could discover meaningful motifs from the muscle specific regulatory sequences.