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BROCKMAN: Deciphering variance in epigenomic regulators by k -mer factorization

https://doi.org/10.1101/129247
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Using Expression Data to Discover RNA and DNA Regulatory Sequence Motifs
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Dynamic Exchange at Regulatory Elements during Chromatin Remodeling Underlies Assisted Loading Mechanism
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Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
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A Proteome-Scale Map of the Human Interactome Network
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Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells
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Histone Acetylome-wide Association Study of Autism Spectrum Disorder
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Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens
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Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities
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Transcription Factor AP1 Potentiates Chromatin Accessibility and Glucocorticoid Receptor Binding
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Pioneer factors: directing transcriptional regulators within the chromatin environment
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AP-1 function and regulation
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An integrated encyclopedia of DNA elements in the human genome
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Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples
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Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state
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AP-1 as a regulator of cell life and death
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Chromatin accessibility maps of chronic lymphocytic leukaemia identify subtype-specific epigenome signatures and transcription regulatory networks
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Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution
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chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data
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Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR
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Nucleosome-mediated cooperativity between transcription factors
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Cross-family dimerization of transcription factors Fos/Jun and ATF/CREB alters DNA binding specificity.
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The Bcr-Abl leukemia oncogene activates Jun kinase and requires Jun for transformation.
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Smads bind directly to the Jun family of AP-1 transcription factors
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Physical Interaction of the Activator Protein-1 Factors c-Fos and c-Jun with Cbfa1 for Collagenase-3 Promoter Activation
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Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation
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Statistical significance of variables driving systematic variation in high-dimensional data
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The UCSC Table Browser data retrieval tool
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SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps
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Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions
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Discovery of Regulatory Elements by a Computational Method for Phylogenetic Footprinting
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Identification of Human Gene Core Promoters in Silico
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Genome‐wide analysis of histone H 3 lysine9 trimethylation by ChIP ‐seq in peripheral blood mononuclear cells of uremia patients
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Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells
inferredcites →https://doi.org/10.1126/science.aab1601openalex-snapshot
Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
inferredcites →https://doi.org/10.1128/mcb.16.12.6957openalex-snapshot
Interactions between STAT and Non-STAT Proteins in the Interferon-Stimulated Gene Factor 3 Transcription Complex
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The molecular biology of chronic myeloid leukemia
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Explicit equilibrium modeling of transcription-factor binding and gene regulation
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Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity
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Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features
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CTRL+INSERT: retrotransposons and their contribution to regulation and innovation of the transcriptome
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ChIP-seq analysis of histone H3K9 trimethylation in peripheral blood mononuclear cells of membranous nephropathy patients

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1038/s41467-018-04629-3openalex-snapshot
Unsupervised clustering and epigenetic classification of single cells
inferred← citeshttps://doi.org/10.1038/s41592-022-01562-8openalex-snapshot
scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks
inferred← citeshttps://doi.org/10.1101/370346openalex-snapshot
Cis-topic modelling of single-cell epigenomes
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