MOTIVATION: Identifying regulatory elements in genomic sequences is a key component in understanding the control of gene expression. Computationally, this problem is often addressed by motif discovery, where the goal is to find a set of mutually similar subsequences within a collection of input sequences. Though motif discovery is widely studied and many approaches to it have been suggested, it remains a challenging and as yet unresolved problem. RESULTS: We introduce SAMF (Solution-Aggregating Motif Finder), a novel approach for motif discovery. SAMF is based on a Markov Random Field formulation, and its key idea is to uncover and aggregate multiple statistically significant solutions to the given motif finding problem. In contrast to many earlier methods, SAMF does not require prior estimates on the number of motif instances present in the data, is not limited by motif length, and allows motifs to overlap. Though SAMF is broadly applicable, these features make it particularly well suited for addressing the challenges of prokaryotic regulatory element detection. We test SAMF's ability to find transcription factor binding sites in an Escherichia coli dataset and show that it outperforms previous methods. Additionally, we uncover a number of previously unidentified binding sites in this data, and provide evidence that they correspond to actual regulatory elements. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Identifying regulatory elements in genomic sequences is a key component in understanding the control of gene expression. Computationally, this problem is often addressed by motif discovery, where the goal is to find a set of mutually similar subsequences within a collection of input sequences. Though motif discovery is widely studied and many approaches to it have been suggested, it remains a challenging and as yet unresolved problem. RESULTS: We introduce SAMF (Solution-Aggregating Motif Finder), a novel approach for motif discovery. SAMF is based on a Markov Random Field formulation, and its key idea is to uncover and aggregate multiple statistically significant solutions to the given motif finding problem. In contrast to many earlier methods, SAMF does not require prior estimates on the number of motif instances present in the data, is not limited by motif length, and allows motifs to overlap. Though SAMF is broadly applicable, these features make it particularly well suited for addressing the challenges of prokaryotic regulatory element detection. We test SAMF's ability to find transcription factor binding sites in an Escherichia coli dataset and show that it outperforms previous methods. Additionally, we uncover a number of previously unidentified binding sites in this data, and provide evidence that they correspond to actual regulatory elements. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Alexander Stark; Michael F Lin; Pouya Kheradpour; Jakob S Pedersen; Leopold Parts; Joseph W Carlson; Madeline A Crosby; Matthew D Rasmussen; Sushmita Roy; Ameya N Deoras; J Graham Ruby; Julius Brennecke; Emily Hodges; Angie S Hinrichs; Anat Caspi; Benedict Paten; Seung-Won Park; Mira V Han; Morgan L Maeder; Benjamin J Polansky; Bryanne E Robson; Stein Aerts; Jacques van Helden; Bassem Hassan; Donald G Gilbert; Deborah A Eastman; Michael Rice; Michael Weir; Matthew W Hahn; Yongkyu Park; Colin N Dewey; Lior Pachter; W James Kent; David Haussler; Eric C Lai; David P Bartel; Gregory J Hannon; Thomas C Kaufman; Michael B Eisen; Andrew G Clark; Douglas Smith; Susan E Celniker; William M Gelbart; Manolis Kellis Journal: Nature Date: 2007-11-08 Impact factor: 49.962
Authors: Gordon Robertson; Martin Hirst; Matthew Bainbridge; Misha Bilenky; Yongjun Zhao; Thomas Zeng; Ghia Euskirchen; Bridget Bernier; Richard Varhol; Allen Delaney; Nina Thiessen; Obi L Griffith; Ann He; Marco Marra; Michael Snyder; Steven Jones Journal: Nat Methods Date: 2007-06-11 Impact factor: 28.547
Authors: Jiangning Song; Hao Tan; Andrew J Perry; Tatsuya Akutsu; Geoffrey I Webb; James C Whisstock; Robert N Pike Journal: PLoS One Date: 2012-11-29 Impact factor: 3.240