PEPATAC: An optimized pipeline for ATAC-seq data analysis with serial alignments

Smith, Jason P.; Corces, M. Ryan; Xu, Jin; Reuter, Vincent; Chang, HY; Sheffield, Nathan C.

doi:10.1101/2020.10.21.347054

Cited by 16 publications

(13 citation statements)

References 47 publications

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“…After CD4 + T cell purification, the “Omni-ATAC-seq” protocol 78 was followed for nuclei isolation, transposase incubation, and library preparation. Libraries were sequenced using 75 bp paired-end reads on an Illumina NextSeq and data were processed using the PEPATAC pipeline 85 . Briefly, reads were trimmed using Skewer (version0.2.2) 86 and, after removing reads mapping to mitochondrial and human repeat regions, were mapped to GRCh38 using bowtie2 87 .…”

Section: Methodsmentioning

confidence: 99%

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

et al. 2021

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We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide significant ( P < 5 × 10 −8 ) regions, including 36 novel. We define credible sets of T1D-associated variants and show they are enriched in immune cell-accessible chromatin, particularly CD4 + effector T cells. Using chromatin accessibility profiling of CD4 + T cells from 115 individuals, we map chromatin accessibility quantitative trait loci (caQTLs) and identify five regions where T1D risk variants colocalize with caQTLs. We highlight rs72928038 in BACH2 as a candidate causal T1D variant leading to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritize potential drug targets by integrating genetic evidence, functional genomic maps, and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D.

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Section: Methodsmentioning

confidence: 99%

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

et al. 2021

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“…Libraries were sequenced as 2x50 on HiSeq2000 platform. ATAC-seq data processing and genome alignment was performed with PEPATAC (version 0.8.2), a pipeline developed to analyze ATAC-seq samples (Smith et al, 2021). PEPATAC begins by trimming adapters using skewer (version 0.2.2) with the parameters “-f sanger -t 8 -m pe”.…”

Section: Methodsmentioning

confidence: 99%

Single-cell Transcriptome and Accessible Chromatin Dynamics During Endocrine Pancreas Development

Duvall

Benitez

Tellez

et al. 2022

Preprint

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Delineating gene regulatory networks that orchestrate cell-type specification is an ongoing challenge for developmental biology studies. Single-cell analyses offer opportunities to address these challenges and accelerate discovery of rare cell lineage relationships and mechanisms underlying hierarchical lineage decisions. Here, we describe the molecular analysis of pancreatic endocrine cell differentiation using single-cell gene expression, chromatin accessibility assays coupled to genetic labeling and cell sorting. We uncover transcription factor networks that delineate beta-, alpha- and delta-cell lineages. Through genomic footprint analysis we identify transcription factor-regulatory DNA interactions governing pancreatic cell development at unprecedented resolution. Our analysis suggests that the transcription factor Neurog3 may act as a pioneer transcription factor to specify the pancreatic endocrine lineage. These findings could improve protocols to generate replacement endocrine cells from renewable sources, like stem cells, for diabetes therapy.

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“…We and others have successfully used the PEP infrastructure in dozens of projects with hundreds to thousands of individual samples. For example, PEP has been used as the sample metadata structure for ATAC-seq [ 21 ] and PRO-seq [ 22 ] workflows, which have been used on several downstream projects. We also rely on the PEP parsers for listing reference genome assets for the refgenie server [ 23 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects

et al. 2021

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Background Organizing and annotating biological sample data is critical in data-intensive bioinformatics. Unfortunately, metadata formats from a data provider are often incompatible with requirements of a processing tool. There is no broadly accepted standard to organize metadata across biological projects and bioinformatics tools, restricting the portability and reusability of both annotated datasets and analysis software. Results To address this, we present the Portable Encapsulated Project (PEP) specification, a formal specification for biological sample metadata structure. The PEP specification accommodates typical features of data-intensive bioinformatics projects with many biological samples. In addition to standardization, the PEP specification provides descriptors and modifiers for project-level and sample-level metadata, which improve portability across both computing environments and data processing tools. PEPs include a schema validator framework, allowing formal definition of required metadata attributes for data analysis broadly. We have implemented packages for reading PEPs in both Python and R to provide a language-agnostic interface for organizing project metadata. Conclusions The PEP specification is an important step toward unifying data annotation and processing tools in data-intensive biological research projects. Links to tools and documentation are available at http://pep.databio.org/.

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PEPATAC: An optimized pipeline for ATAC-seq data analysis with serial alignments

Cited by 16 publications

References 47 publications

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

Single-cell Transcriptome and Accessible Chromatin Dynamics During Endocrine Pancreas Development

Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects

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