A Monte Carlo-based framework enhances the discovery and interpretation of regulatory sequence motifs
1 Department of Biomedical Engineering, One Shields Ave, University of California, Davis, CA 95616, USA
2 Genome Center, One Shields Ave, University of California, Davis, CA 95616, USA
3 Microbiology Graduate Group, One Shields Ave, University of California, Davis, CA, 95616, USA
BMC Bioinformatics 2012, 13:317 doi:10.1186/1471-2105-13-317Published: 27 November 2012
Additional file 1:
Figure S1. Gcn4 Retrieval as a function of dataset corruption. S2. Alternative extended description of the MotifCatcher algorithm. Monte Carlo framework. Motif tree construction. Organization and evaluation. Software platform. S3.Figure S2. Screenshots of MotifCatcher software platform. S4: Figure S3. Motif Finder performance on LexA data set as a function of FP frequency threshold. S5. Supplementary Excel Spread Sheet. Table of Contents. Recovery of LexA motif with variable FP Threshold. Ability for various motif finders to discover TFB motif. Comparison of MotifCatcher-discovered motifs with TSS. S6.Figure S4. Novel motif discovered in Type III LexA binding sites. Supplementary References.
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Additional file 2:
Recovery of LexA motif with variable FP threshold, Comparison of the ability of various motif finders to discover the TFB motif, and comparison of MotifCatcher-discovered motifs with location of experimentally determined Transcript Start Sites.
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