CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data

Thibodeau, Asa; Khetan, Shubham; Eroğlu, Alper; Tewhey, R; Stitzel, Michael L.; Ucar, Duygu

doi:10.1371/journal.pcbi.1009670

Cited by 8 publications

(5 citation statements)

References 59 publications

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“…For this purpose, we utilized ATAC-seq data to identify the open and closed chromatin segments (Daugherty et al, 2017; Jänes et al, 2018; Thibodeau et al, 2021). The ATAC-seq peak data from C. elegans (Jänes et al, 2018), includes 42,245 peaks with an average width of 152 bp.…”

Section: Resultsmentioning

confidence: 99%

Inferring the selective history of CNVs using a maximum likelihood model

Malekpour,

Kalirad,

Majidian

2024

Preprint

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Copy number variations (CNVs), structural variations generated by deletion and/or duplication that result in change in DNA dosage, are prevalent in nature. CNVs can drastically affect the phenotype of an organism and have been shown to be both involved in genetic disorders and be used as raw material in adaptive evolution. Unlike single-nucleotide variations, the often large and varied effects of CNVs on phenotype hinders our ability to infer their selective advantage based on the population genetics data. Here, we present a likelihood-based approach, dubbed PoMoCNV, that estimates the evolutionary parameters of CNVs based on population genetics data. As a case study, we analyze the genomics data of 40 strains of Caenorhabditis elegans, representing four different populations. We take advantage of the data on chromatin accessibility to interpret the evolutionary parameters of CNVs inferred by PoMoCNV. We further test the reliability of PoMoCNV by estimating the evolutionary parameters of CNVs for mutation-accumulation experiments in C. elegans with varying levels of genetic drift.

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Section: Resultsmentioning

confidence: 99%

Inferring the selective history of CNVs using a maximum likelihood model

Malekpour,

Kalirad,

Majidian

2024

Preprint

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“…maxATAC is a unique resource, representing the largest collection of deep CNN TFBS prediction models for chromatin accessibility and a first-time benchmark of trans-cell type prediction on scATAC-seq. There are numerous deep CNN methods for ATAC-seq data [21,[66][67][68][69][70], but these methods have different modeling objectives, including: prediction of ATAC-seq PLOS COMPUTATIONAL BIOLOGY signal from DNA sequence (e.g., AI-TAC [68], chromBPNet [66], deepMEL [67]), denoising of ATAC-seq and scATAC-seq signal (AtacWorks [70]), classification of enhancers, promoters and insulators based on ATAC-derived inputs (CoRE-ATAC [69]) and clustering of scATACseq data (scFAN [21]).…”

Section: Discussionmentioning

confidence: 99%

maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

et al. 2023

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Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built “maxATAC”, a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC’s capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.

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“…The coupling of different techniques allows the visualization of different activating or repressive histone marks correlated with the chromatin opening state [97,98].…”

Section: Cut and Tagmentioning

confidence: 99%

Chromatin Structure and Dynamics: Focus on Neuronal Differentiation and Pathological Implication

Nothof

Magdinier

Van-Gils

2022

Genes

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Chromatin structure is an essential regulator of gene expression. Its state of compaction contributes to the regulation of genetic programs, in particular during differentiation. Epigenetic processes, which include post-translational modifications of histones, DNA methylation and implication of non-coding RNA, are powerful regulators of gene expression. Neurogenesis and neuronal differentiation are spatio-temporally regulated events that allow the formation of the central nervous system components. Here, we review the chromatin structure and post-translational histone modifications associated with neuronal differentiation. Studying the impact of histone modifications on neuronal differentiation improves our understanding of the pathophysiological mechanisms of chromatinopathies and opens up new therapeutic avenues. In addition, we will discuss techniques for the analysis of histone modifications on a genome-wide scale and the pathologies associated with the dysregulation of the epigenetic machinery.

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CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data

Cited by 8 publications

References 59 publications

Inferring the selective history of CNVs using a maximum likelihood model

Inferring the selective history of CNVs using a maximum likelihood model

maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

Chromatin Structure and Dynamics: Focus on Neuronal Differentiation and Pathological Implication

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