ArticlePloS one2015
msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Gaining insights into Alzheimer's disease by predicting chromatin spatial organization.Bioinformatics advances · 2025Article
- Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling.NAR genomics and bioinformatics · 2023Article
- Benefiting from the intrinsic role of epigenetics to predict patterns of CTCF binding.Computational and structural biotechnology journal · 2023Article
- TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile.PLoS computational biology · 2022Article
- Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.Genome research · 2022Article
- Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.Nucleic acids research · 2021Article
- From reads to insight: a hitchhiker's guide to ATAC-seq data analysis.Genome biology · 2020Review
- Dissecting the binding mechanisms of transcription factors to DNA using a statistical thermodynamics framework.Computational and structural biotechnology journal · 2020Article
- FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.Methods (San Diego, Calif.) · 2019Article
- Integrative analysis of vascular endothelial cell genomic features identifies AIDA as a coronary artery disease candidate gene.Genome biology · 2019Article
- Identification of transcription factor binding sites using ATAC-seq.Genome biology · 2019Article
- Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modeling.Genome biology · 2019Article
- Accurate prediction of cell type-specific transcription factor binding.Genome biology · 2019Article
- Applications of ENCODE data to Systematic Analyses via Data Integration.Current opinion in systems biology · 2018Article
- Genome-scale identification of transcription factors that mediate an inflammatory network during breast cellular transformation.Nature communications · 2018Article
- Modeling gene regulation from paired expression and chromatin accessibility data.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Regulatory elements in molecular networks.Wiley interdisciplinary reviews. Systems biology and medicine · 2017Review
- DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter.Bioinformatics (Oxford, England) · 2017Article
- Which Genetics Variants in DNase-Seq Footprints Are More Likely to Alter Binding?PLoS genetics · 2016Article
- On Accounting for Sequence-Specific Bias in Genome-Wide Chromatin Accessibility Experiments: Recent Advances and Contradictions.Frontiers in bioengineering and biotechnology · 2015Article
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5 authors.
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Abstract
Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede.
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