Finding de novo methylated DNA motifs

AbstractIncreasing evidence has shown that posttranslational modifications (PTMs) such as methylation and hydroxymethylation on cytosine would greatly impact the binding of transcription factors (TFs). However, there is a lack of motif finding algorithms with the function to search for motifs with PTMs. In this study, we expend on our previous motif finding pipeline Epigram to provide systematic de novo motif discovery and performance evaluation on methylated DNA motifs. Using the tool, we were able to identified methylated motifs in Arabidopsis DAP-seq data that were previously demonstrated to contain such motifs1. When applied to TF ChIP-seq and DNA methylome data in H1 and GM12878, our method successfully identified novel methylated motifs that can be recognized by the TFs or their co-factors. We also observed spacing constraint between the canonical motif of the TF and the newly discovered methylated motifs, which suggests operative recognition of these cis-elements by collaborative proteins.

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Finding de novo methylated DNA motifs

Bioinformatics ◽

10.1093/bioinformatics/btz079 ◽

2019 ◽

Vol 35 (18) ◽

pp. 3287-3293 ◽

Cited By ~ 4

Author(s):

Vu Ngo ◽

Mengchi Wang ◽

Wei Wang

Keyword(s):

Motif Discovery ◽

De Novo ◽

Affinity Purification ◽

Simulated Data ◽

Motif Finding ◽

Supplementary Information ◽

Dna Motifs ◽

Methylated Dna ◽

De Novo Motif Discovery ◽

And Performance

Abstract Motivation Increasing evidence has shown that nucleotide modifications such as methylation and hydroxymethylation on cytosine would greatly impact the binding of transcription factors (TFs). However, there is a lack of motif finding algorithms with the function to search for motifs with modified bases. In this study, we expand on our previous motif finding pipeline Epigram to provide systematic de novo motif discovery and performance evaluation on methylated DNA motifs. Results mEpigram outperforms both MEME and DREME on finding modified motifs in simulated data that mimics various motif enrichment scenarios. Furthermore we were able to identify methylated motifs in Arabidopsis DNA affinity purification sequencing (DAP-seq) data that were previously demonstrated to contain such motifs. When applied to TF ChIP-seq and DNA methylome data in H1 and GM12878, our method successfully identified novel methylated motifs that can be recognized by the TFs or their co-factors. We also observed spacing constraint between the canonical motif of the TF and the newly discovered methylated motifs, which suggests operative recognition of these cis-elements by collaborative proteins. Availability and implementation The mEpigram program is available at http://wanglab.ucsd.edu/star/mEpigram. Supplementary information Supplementary data are available at Bioinformatics online.

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SEAM: A STOCHASTIC EM-TYPE ALGORITHM FOR MOTIF-FINDING IN BIOPOLYMER SEQUENCES

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720007002527 ◽

2007 ◽

Vol 05 (01) ◽

pp. 47-77 ◽

Cited By ~ 15

Author(s):

CHENGPENG BI

Keyword(s):

Motif Discovery ◽

De Novo ◽

Cyclic Adenosine Monophosphate ◽

Adenosine Monophosphate ◽

Motif Finding ◽

Receptor Protein ◽

Type Algorithm ◽

De Novo Motif Discovery ◽

Stochastic Em

Position weight matrix-based statistical modeling for the identification and characterization of motif sites in a set of unaligned biopolymer sequences is presented. This paper describes and implements a new algorithm, the Stochastic EM-type Algorithm for Motif-finding (SEAM), and redesigns and implements the EM-based motif-finding algorithm called deterministic EM (DEM) for comparison with SEAM, its stochastic counterpart. The gold standard example, cyclic adenosine monophosphate receptor protein (CRP) binding sequences, together with other biological sequences, is used to illustrate the performance of the new algorithm and compare it with other popular motif-finding programs. The convergence of the new algorithm is shown by simulation. The in silico experiments using simulated and biological examples illustrate the power and robustness of the new algorithm SEAM in de novo motif discovery.

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