ArticleBioinformatics (Oxford, England)2019
Finding de novo methylated DNA motifs.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Loss of Pathogenicity and Evidence of Horizontal Gene Transfer in Colletotrichum gloeosporioides From a Medicinal Plant.Molecular plant pathology · 2025Article
- Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet.Genome biology · 2024Article
- Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.Nucleic acids research · 2023Article
- Genome-Wide Analysis of theAnimals : an open access journal from MDPI · 2023Article
- CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction.Computational and structural biotechnology journal · 2023Article
- Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding.Genome biology · 2022Article
- Deciphering the genetic code of DNA methylation.Briefings in bioinformatics · 2021Review
- Evolution of DNA methylome from precancerous lesions to invasive lung adenocarcinomas.Nature communications · 2021Article
- Article
- LogoJS: a Javascript package for creating sequence logos and embedding them in web applications.Bioinformatics (Oxford, England) · 2020Article
- Finding de novo methylated DNA motifs.Bioinformatics (Oxford, England) · 2019Article
- Identification of DNA motifs that regulate DNA methylation.Nucleic acids research · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
motivationIncreasing 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.
resultsmEpigram 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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