ArticleBMC bioinformatics2018
BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Computational Analyses and Challenges of Single-cell ATAC-seq.Genomics, proteomics & bioinformatics · 2025Review
- IceQream: Quantitative chromosome accessibility analysis using physical TF models.Nature communications · 2025Article
- A hierarchical, count-based model highlights challenges in scATAC-seq data analysis and points to opportunities to extract finer-resolution information.Genome biology · 2025Article
- Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases.Briefings in functional genomics · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Going beyond cell clustering and feature aggregation: Is there single cell level information in single-cell ATAC-seq data?bioRxiv : the preprint server for biology · 2024Article
- Benchmarking computational methods for single-cell chromatin data analysis.Genome biology · 2024Article
- MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts.Nature communications · 2024Article
- A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder.International journal of molecular sciences · 2023Article
- Characterizing cis-regulatory elements using single-cell epigenomics.Nature reviews. Genetics · 2023Review
- Epigenomic priming of immune genes implicates oligodendroglia in multiple sclerosis susceptibility.Neuron · 2022Article
- Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research.Biomedicines · 2021Review
- EpiScanpy: integrated single-cell epigenomic analysis.Nature communications · 2021Article
- Profiling Chromatin Accessibility at Single-cell Resolution.Genomics, proteomics & bioinformatics · 2021Review
- Single Cell Omics for Musculoskeletal Research.Current osteoporosis reports · 2021Review
- simATAC: a single-cell ATAC-seq simulation framework.Genome biology · 2021Article
- Comprehensive analysis of single cell ATAC-seq data with SnapATAC.Nature communications · 2021Article
- Chromatin accessibility profiling methods.Nature reviews. Methods primers · 2021Article
- Predicting transcription factor binding in single cells through deep learning.Science advances · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundVariation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark.
resultsBROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification of the TFs that distinguish groups. Applied to single cell ATAC-seq, BROCKMAN readily distinguished cell types, treatments, batch effects, experimental artifacts, and cycling cells. We show that each variable component in the k-mer landscape reflects a set of co-varying TFs, which are often known to physically interact. For example, in K562 cells, AP-1 TFs were central determinant of variability in chromatin accessibility through their variable expression levels and diverse interactions with other TFs. We provide a theoretical basis for why cooperative TF binding - and any associated epigenomic mark - is inherently more variable than non-cooperative binding.
conclusionsBROCKMAN and related approaches will help gain a mechanistic understanding of the trans determinants of chromatin variability between cells, treatments, and individuals.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.