Metamotifs - a generative model for building families of nucleotide position weight matrices

Background: Development of high-throughput methods for measuring DNA interactions of transcription factors together with computational advances in short motif inference algorithms is expanding our understanding of transcription factor binding site motifs. The consequential growth of sequence motif data sets makes it important to systematically group and categorise regulatory motifs. It has been shown that there are familial tendencies in DNA sequence motifs that are predictive of the family of factors that binds them. Further development of methods that detect and describe familial motif trends has the potential to help in measuring the similarity of novel computational motif predictions to previously known data and sensitively detecting regulatory motifs similar to previously known ones from novel sequence. Results: We propose a probabilistic model for position weight matrix (PWM) sequence motif families. The model, which we call the `metamotif' describes recurring familial patt...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Matias Piipari, Thomas A. Down, Tim J. P. Hubbard
• Keywords: BMCBI 2010, Motif Data, Motif Inference, Sequence Motif