ArticleGenome biology2024
Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- BayesPI-FLY: a Bayesian neural network approach for inferring feature weighted TF-DNA interaction.Briefings in bioinformatics · 2026Article
- inMOTIFin: a lightweight end-to-end simulation software for regulatory sequences.Bioinformatics (Oxford, England) · 2026Article
- SEGUID v2: Extending SEGUID checksums for circular, linear, single- and double-stranded biological sequences.PloS one · 2026Article
- 5mC and 5hmC methylation sequencing: the power of 6-base sequencing in a multiomic era.Epigenomics · 2026Review
- An integrative analysis of transcriptome, methylome and single-cell RNA sequencing data identifies UBE2H as a marker of oxaliplatin resistance in colorectal cancer.Cancer cell international · 2025Article
- Epigenetic networks coordinate DNA methylation across the genome.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Prevalence of malnutrition and associated factors in Chinese children and adolescents aged 3-14 years using machine learning algorithms.Journal of global health · 2025Article
- Targeting Gene Transcription Prevents Antibiotic Resistance.Antibiotics (Basel, Switzerland) · 2025Review
- Genomic and transcriptomic features of androgen receptor signaling inhibitor resistance in metastatic castration-resistant prostate cancer.The Journal of clinical investigation · 2024Article
- Epigenomic insights into common human disease pathology.Cellular and molecular life sciences : CMLS · 2024Review
- Genome-Wide Identification and Expression Pattern Analysis of BAHD Acyltransferase Family inInternational journal of molecular sciences · 2024Article
- Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet.Genome biology · 2024Article
- JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles.Nucleic acids research · 2024Article
- Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models.Nucleic acids research · 2023Article
- Noncanonical binding of transcription factors: time to revisitMolecular biology of the cell · 2023Review
- Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding.Genome biology · 2022Article
- Effects of DNA Methylation on TFs in Human Embryonic Stem Cells.Frontiers in genetics · 2021Article
- Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
- LogoJS: a Javascript package for creating sequence logos and embedding them in web applications.Bioinformatics (Oxford, England) · 2020Article
- DNA methylation disruption reshapes the hematopoietic differentiation landscape.Nature genetics · 2020Article
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14 authors.
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Abstract
backgroundTranscription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not to take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult.
resultsHere, we develop methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We develop Cytomod to create modified genomic sequences and we also enhance the MEME Suite, adding the capacity to handle custom alphabets. We adapt the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identify modification-sensitive transcription factor binding motifs. We confirm established binding preferences, such as the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated E-box motifs.
conclusionsUsing known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.
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