2019
DOI: 10.1101/607408
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Motto: Representing motifs in consensus sequences with minimum information loss

Abstract: Sequence analysis frequently requires intuitive understanding and convenient representation of motifs. Typically, motifs are represented as position weight matrices (PWMs) and visualized using sequence logos. However, in many scenarios, representing motifs by wildcard-style consensus sequences is compact and sufficient for interpreting the motif information and search for motif match. Based on mutual information theory and Jenson-Shannon Divergence, we propose a mathematical framework to minimize the informati… Show more

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“…UM_180.0_3.14_0.56_7_known-CTCF) matched by Tomtom described previously, or a consensus sequence (e.g. MM_10.2_2.16_0.54_1_ATKGCGSCA) determined by a minimal information loss method (20). The strongest 313 motifs were filtered by volcano test with combined P < 1e–10 and enrichment > 2 (Supplementary Figure S1B).…”
Section: Methodsmentioning
confidence: 99%
“…UM_180.0_3.14_0.56_7_known-CTCF) matched by Tomtom described previously, or a consensus sequence (e.g. MM_10.2_2.16_0.54_1_ATKGCGSCA) determined by a minimal information loss method (20). The strongest 313 motifs were filtered by volcano test with combined P < 1e–10 and enrichment > 2 (Supplementary Figure S1B).…”
Section: Methodsmentioning
confidence: 99%