Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv

Orchard, Peter; Kyono, Yasuhiro; Hensley, John; Kitzman, Jacob O.; Parker, Stephen C. J.

doi:10.1016/j.cels.2020.02.009

Cited by 62 publications

(48 citation statements)

References 57 publications

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“…First we examined the four snATAC-seq libraries, comparing the aggregate signal for each library to bulk ATAC-seq libraries from the same biological sample. We called peaks for the four libraries and ran the ataqv quality control software package (29) on the aggregated data to examine the overall transcription start site (TSS) enrichment and fragment length distributions. The fragment length distributions for each library resembled the expected stereotypical ATAC-seq fragment length distribution, showing an abundance of short fragments as well as mononucleosomal fragments (Fig.…”

Section: Results Fans Negatively Impacts 10x Snatac-seq Resultsmentioning

confidence: 99%

“…In addition to setting a threshold for minimum fragments (to filter out barcodes that only capture ambient DNA fragments), we set a threshold for maximum fragments, because barcodes with very high fragment counts may be enriched for doublets (41). We also set a threshold for minimum TSS enrichment (because ATAC-seq signal for healthy nuclei is expected to be enriched near TSS (41,85,86)), and we filtered out barcodes that showed an unexpectedly large fraction of reads coming from a single autosome (see (29)). Processing of snRNA-seq data snRNA-seq data was processed using starSOLO (STAR v. 2.7.3a), which outputs the count matrices needed for most of the analyses (87).…”

Section: Reproducibility Of Computational Analysesmentioning

confidence: 99%

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Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

Orchard

Manickam

Varshney

et al. 2020

Preprint

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AbstractBackgroundSkeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases, mobility, and quality of life. It is composed of several different cell and muscle fiber types.ResultsHere, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 30,531 nuclei, representing 11 libraries, profiled in this study, and identify seven distinct cell types ranging in abundance from 63% (type II fibers) to 0.9% (muscle satellite cells) of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site-distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells (∼3% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, and transcription factor motifs for creatinine levels and type 2 diabetes signals.ConclusionsThese chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for investigating specific cell types and nominating causal GWAS SNPs and cell types.

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Section: Results Fans Negatively Impacts 10x Snatac-seq Resultsmentioning

confidence: 99%

Section: Reproducibility Of Computational Analysesmentioning

confidence: 99%

Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

Orchard

Manickam

Varshney

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To assess the reproducibility among biological samples within the same cell type and brain region, we performed pairwise correlation between the raw read counts over consecutive bins of 10,000 bp genomic regions using bamCorrelate 51 . We also calculated transcription start site (TSS) enrichment in housekeeping genes as implemented in ataqv 52 for the current dataset as well as three other postmortem brain studies 5 , 31 , 53 . Ataqv calculates coverage around the TSS using ATAC-seq fragments up to 1 kb from the TSS in both directions.…”

Section: Methodsmentioning

confidence: 99%

Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons

et al. 2020

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The chromatin landscape of human brain cells encompasses key information to understanding brain function. Here we use ATAC-seq to profile the chromatin structure in four distinct populations of cells (glutamatergic neurons, GABAergic neurons, oligodendrocytes, and microglia/astrocytes) from three different brain regions (anterior cingulate cortex, dorsolateral prefrontal cortex, and primary visual cortex) in human postmortem brain samples. We find that chromatin accessibility varies greatly by cell type and, more moderately, by brain region, with glutamatergic neurons showing the largest regional variability. Transcription factor footprinting implicates cell-specific transcriptional regulators and infers cell-specific regulation of protein-coding genes, long intergenic noncoding RNAs and microRNAs. In vivo transgenic mouse experiments validate the cell type specificity of several of these human-derived regulatory sequences. We find that open chromatin regions in glutamatergic neurons are enriched for neuropsychiatric risk variants, particularly those associated with schizophrenia. Integration of cell-specific chromatin data with a bulk tissue study of schizophrenia brains increases statistical power and confirms that glutamatergic neurons are most affected. These findings illustrate the utility of studying the cell-type-specific epigenome in complex tissues like the human brain, and the potential of such approaches to better understand the genetic basis of human brain function.

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“…Duplicate reads were marked and removed using “MarkDuplicates” from Picard-tools version 1.95 (http://broadinstitute.github.io/picard/). The quality of aligned reads was examined using Ataqv v.1.0.0 51 . After preprocessing and quality filtering, peaks were called on alignments with MACS version 2.1.0.20151222 52 using the parameters “-p 0.0001 -g mm -f BAMPE -- nomodel --nolambda -B --keep-dup all --call-summits”.…”

Section: Methodsmentioning

confidence: 99%

Tet2 Controls β cells Responses to Inflammation in Type 1 Diabetes

Rui¹,

Deng²,

Ponath

et al. 2020

Preprint

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Abstractβ cells may participate and contribute to their own demise during Type 1 diabetes (T1D). We identified a novel role of Tet2 in regulating immune killing of β cells. Tet2 is induced in murine and human β cells with inflammation but its expression is reduced in surviving β cells. Tet2-KO mice that receive WT bone marrow transplants develop insulitis but not diabetes and islet infiltrates do not eliminate β cells even though immune cells from the mice can transfer diabetes to NOD/scid recipients. Tet2-KO β cells show reduced expression of inflammatory genes, associated with closed transcription factor binding sites. Tet2-KO recipients are protected from transfer of disease by diabetogenic immune cells. We conclude that Tet2 regulates pathologic interactions between β cells and immune cells and controls intrinsic protective pathways. Modulating TET2 may enable survival of β cells or their replacements in the setting of pathologic immune cells.

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Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv

Cited by 62 publications

References 57 publications

Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

Common schizophrenia risk variants are enriched in open chromatin regions of human glutamatergic neurons

Tet2 Controls β cells Responses to Inflammation in Type 1 Diabetes

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