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Zheng G, Rymuza J, Gharavi E, LeRoy N, Zhang A, Sheffield N. Methods for evaluating unsupervised vector representations of genomic regions. NAR Genom Bioinform 2024; 6:lqae086. [PMID: 39131817 PMCID: PMC11316252 DOI: 10.1093/nargab/lqae086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 06/14/2024] [Accepted: 07/29/2024] [Indexed: 08/13/2024] Open
Abstract
Representation learning models have become a mainstay of modern genomics. These models are trained to yield vector representations, or embeddings, of various biological entities, such as cells, genes, individuals, or genomic regions. Recent applications of unsupervised embedding approaches have been shown to learn relationships among genomic regions that define functional elements in a genome. Unsupervised representation learning of genomic regions is free of the supervision from curated metadata and can condense rich biological knowledge from publicly available data to region embeddings. However, there exists no method for evaluating the quality of these embeddings in the absence of metadata, making it difficult to assess the reliability of analyses based on the embeddings, and to tune model training to yield optimal results. To bridge this gap, we propose four evaluation metrics: the cluster tendency score (CTS), the reconstruction score (RCS), the genome distance scaling score (GDSS), and the neighborhood preserving score (NPS). The CTS and RCS statistically quantify how well region embeddings can be clustered and how well the embeddings preserve information in training data. The GDSS and NPS exploit the biological tendency of regions close in genomic space to have similar biological functions; they measure how much such information is captured by individual region embeddings in a set. We demonstrate the utility of these statistical and biological scores for evaluating unsupervised genomic region embeddings and provide guidelines for learning reliable embeddings.
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Affiliation(s)
- Guangtao Zheng
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Julia Rymuza
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Erfaneh Gharavi
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Nathan J LeRoy
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Aidong Zhang
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Nathan C Sheffield
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Child Health Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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2
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Rymuza J, Sun Y, Zheng G, LeRoy NJ, Murach M, Phan N, Zhang A, Sheffield NC. Methods for constructing and evaluating consensus genomic interval sets. Nucleic Acids Res 2024:gkae685. [PMID: 39180401 DOI: 10.1093/nar/gkae685] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 07/05/2024] [Accepted: 07/29/2024] [Indexed: 08/26/2024] Open
Abstract
The amount of genomic region data continues to increase. Integrating across diverse genomic region sets requires consensus regions, which enable comparing regions across experiments, but also by necessity lose precision in region definitions. We require methods to assess this loss of precision and build optimal consensus region sets. Here, we introduce the concept of flexible intervals and propose three novel methods for building consensus region sets, or universes: a coverage cutoff method, a likelihood method, and a Hidden Markov Model. We then propose three novel measures for evaluating how well a proposed universe fits a collection of region sets: a base-level overlap score, a region boundary distance score, and a likelihood score. We apply our methods and evaluation approaches to several collections of region sets and show how these methods can be used to evaluate fit of universes and build optimal universes. We describe scenarios where the common approach of merging regions to create consensus leads to undesirable outcomes and provide principled alternatives that provide interoperability of interval data while minimizing loss of resolution.
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Affiliation(s)
- Julia Rymuza
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Yuchen Sun
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Guangtao Zheng
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Nathan J LeRoy
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Maria Murach
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Neil Phan
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Aidong Zhang
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Nathan C Sheffield
- Department of Genome Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Child Health Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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Sunitha Kumary VUN, Venters BJ, Raman K, Sen S, Estève PO, Cowles MW, Keogh MC, Pradhan S. Emerging Approaches to Profile Accessible Chromatin from Formalin-Fixed Paraffin-Embedded Sections. EPIGENOMES 2024; 8:20. [PMID: 38804369 PMCID: PMC11130958 DOI: 10.3390/epigenomes8020020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Nucleosomes are non-uniformly distributed across eukaryotic genomes, with stretches of 'open' chromatin strongly associated with transcriptionally active promoters and enhancers. Understanding chromatin accessibility patterns in normal tissue and how they are altered in pathologies can provide critical insights to development and disease. With the advent of high-throughput sequencing, a variety of strategies have been devised to identify open regions across the genome, including DNase-seq, MNase-seq, FAIRE-seq, ATAC-seq, and NicE-seq. However, the broad application of such methods to FFPE (formalin-fixed paraffin-embedded) tissues has been curtailed by the major technical challenges imposed by highly fixed and often damaged genomic material. Here, we review the most common approaches for mapping open chromatin regions, recent optimizations to overcome the challenges of working with FFPE tissue, and a brief overview of a typical data pipeline with analysis considerations.
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Affiliation(s)
| | - Bryan J. Venters
- EpiCypher Inc., Durham, NC 27709, USA; (V.U.N.S.K.); (B.J.V.); (M.W.C.)
| | - Karthikeyan Raman
- Genome Biology Division, New England Biolabs, Ipswich, MA 01983, USA; (K.R.); (S.S.); (P.-O.E.)
| | - Sagnik Sen
- Genome Biology Division, New England Biolabs, Ipswich, MA 01983, USA; (K.R.); (S.S.); (P.-O.E.)
| | - Pierre-Olivier Estève
- Genome Biology Division, New England Biolabs, Ipswich, MA 01983, USA; (K.R.); (S.S.); (P.-O.E.)
| | - Martis W. Cowles
- EpiCypher Inc., Durham, NC 27709, USA; (V.U.N.S.K.); (B.J.V.); (M.W.C.)
| | | | - Sriharsa Pradhan
- Genome Biology Division, New England Biolabs, Ipswich, MA 01983, USA; (K.R.); (S.S.); (P.-O.E.)
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Sinha R, Dvorak M, Ganesan A, Kalesinskas L, Niemeyer CM, Flotho C, Sakamoto KM, Lacayo N, Patil RV, Perriman R, Cepika AM, Liu YL, Kuo A, Utz PJ, Khatri P, Bertaina A. Epigenetic Profiling of PTPN11 Mutant JMML Hematopoietic Stem and Progenitor Cells Reveals an Aberrant Histone Landscape. Cancers (Basel) 2023; 15:5204. [PMID: 37958378 PMCID: PMC10650722 DOI: 10.3390/cancers15215204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 10/18/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Juvenile myelomonocytic leukemia (JMML) is a deadly pediatric leukemia driven by RAS pathway mutations, of which >35% are gain-of-function in PTPN11. Although DNA hypermethylation portends severe clinical phenotypes, the landscape of histone modifications and chromatin profiles in JMML patient cells have not been explored. Using global mass cytometry, Epigenetic Time of Flight (EpiTOF), we analyzed hematopoietic stem and progenitor cells (HSPCs) from five JMML patients with PTPN11 mutations. These data revealed statistically significant changes in histone methylation, phosphorylation, and acetylation marks that were unique to JMML HSPCs when compared with healthy controls. Consistent with these data, assay for transposase-accessible chromatin with sequencing (ATAC-seq) analysis revealed significant alterations in chromatin profiles at loci encoding post-translational modification enzymes, strongly suggesting their mis-regulated expression. Collectively, this study reveals histone modification pathways as an additional epigenetic abnormality in JMML patient HSPCs, thereby uncovering a new family of potential druggable targets for the treatment of JMML.
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Affiliation(s)
- Roshani Sinha
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (R.S.); (R.V.P.); (R.P.); (A.-M.C.); (Y.L.L.)
| | - Mai Dvorak
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA; (M.D.); (A.G.); (L.K.); (A.K.); (P.J.U.); (P.K.)
| | - Ananthakrishnan Ganesan
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA; (M.D.); (A.G.); (L.K.); (A.K.); (P.J.U.); (P.K.)
| | - Larry Kalesinskas
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA; (M.D.); (A.G.); (L.K.); (A.K.); (P.J.U.); (P.K.)
| | - Charlotte M. Niemeyer
- Department of Pediatric Hematology and Oncology, University of Freiburg Medical Centre, 79098 Freiburg im Breisgau, Germany; (C.M.N.); (C.F.)
| | - Christian Flotho
- Department of Pediatric Hematology and Oncology, University of Freiburg Medical Centre, 79098 Freiburg im Breisgau, Germany; (C.M.N.); (C.F.)
| | - Kathleen M. Sakamoto
- Bass Center for Childhood Cancer and Blood Disorders at Lucile Packard Children’s Hospital, Palo Alto, CA 94304, USA; (K.M.S.); (N.L.)
| | - Norman Lacayo
- Bass Center for Childhood Cancer and Blood Disorders at Lucile Packard Children’s Hospital, Palo Alto, CA 94304, USA; (K.M.S.); (N.L.)
| | - Rachana Vinay Patil
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (R.S.); (R.V.P.); (R.P.); (A.-M.C.); (Y.L.L.)
| | - Rhonda Perriman
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (R.S.); (R.V.P.); (R.P.); (A.-M.C.); (Y.L.L.)
| | - Alma-Martina Cepika
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (R.S.); (R.V.P.); (R.P.); (A.-M.C.); (Y.L.L.)
| | - Yunying Lucy Liu
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (R.S.); (R.V.P.); (R.P.); (A.-M.C.); (Y.L.L.)
| | - Alex Kuo
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA; (M.D.); (A.G.); (L.K.); (A.K.); (P.J.U.); (P.K.)
| | - Paul J. Utz
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA; (M.D.); (A.G.); (L.K.); (A.K.); (P.J.U.); (P.K.)
| | - Purvesh Khatri
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA 94305, USA; (M.D.); (A.G.); (L.K.); (A.K.); (P.J.U.); (P.K.)
| | - Alice Bertaina
- Division of Hematology, Oncology, Stem Cell Transplantation and Regenerative Medicine, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94305, USA; (R.S.); (R.V.P.); (R.P.); (A.-M.C.); (Y.L.L.)
- Bass Center for Childhood Cancer and Blood Disorders at Lucile Packard Children’s Hospital, Palo Alto, CA 94304, USA; (K.M.S.); (N.L.)
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5
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Geraldes I, Fernandes M, Fraga AG, Osório NS. The impact of single-cell genomics on the field of mycobacterial infection. Front Microbiol 2022; 13:989464. [PMID: 36246265 PMCID: PMC9562642 DOI: 10.3389/fmicb.2022.989464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/14/2022] [Indexed: 11/13/2022] Open
Abstract
Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.
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Affiliation(s)
- Inês Geraldes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Mónica Fernandes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Alexandra G. Fraga
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Nuno S. Osório
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- *Correspondence: Nuno S. Osório
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6
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Epigenetic Application of ATAC-Seq Based on Tn5 Transposase Purification Technology. Genet Res (Camb) 2022; 2022:8429207. [PMID: 36062065 PMCID: PMC9388308 DOI: 10.1155/2022/8429207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 06/22/2022] [Indexed: 11/17/2022] Open
Abstract
Background Assays of transposase accessible chromatin sequencing (ATAC-seq) is an efficient assay to investigate chromatin accessibility, which depends on the activity of a robust Tn5 transposase to fragment the genome while cutting in the sequencing adapters. Methods We propose reliable approaches for purifying hyperactive Tn5 transposase by chitin magnetic bead sorting. Double-stranded DNA of J76 cells and 293T cells were digested and subjected to tagmentation as test samples with Tn5 transposase, and libraries were established and sequenced. Sequencing data was then analyzed for peak calling, GO enrichment, and motif analysis. Results We report a set of rapid, efficient, and low-cost methods for ATAC-seq library construction and data analysis, through large-scale and rapid sequencing. These methods can provide a reference for the study of epigenetic regulation of gene expression.
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7
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Grandi FC, Modi H, Kampman L, Corces MR. Chromatin accessibility profiling by ATAC-seq. Nat Protoc 2022; 17:1518-1552. [PMID: 35478247 PMCID: PMC9189070 DOI: 10.1038/s41596-022-00692-9] [Citation(s) in RCA: 112] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 02/22/2022] [Indexed: 12/13/2022]
Abstract
The assay for transposase-accessible chromatin using sequencing (ATAC-seq) provides a simple and scalable way to detect the unique chromatin landscape associated with a cell type and how it may be altered by perturbation or disease. ATAC-seq requires a relatively small number of input cells and does not require a priori knowledge of the epigenetic marks or transcription factors governing the dynamics of the system. Here we describe an updated and optimized protocol for ATAC-seq, called Omni-ATAC, that is applicable across a broad range of cell and tissue types. The ATAC-seq workflow has five main steps: sample preparation, transposition, library preparation, sequencing and data analysis. This protocol details the steps to generate and sequence ATAC-seq libraries, with recommendations for sample preparation and downstream bioinformatic analysis. ATAC-seq libraries for roughly 12 samples can be generated in 10 h by someone familiar with basic molecular biology, and downstream sequencing analysis can be implemented using benchmarked pipelines by someone with basic bioinformatics skills and with access to a high-performance computing environment.
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Affiliation(s)
- Fiorella C Grandi
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Hailey Modi
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Lucas Kampman
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA.
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA.
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
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Smith JP, Corces MR, Xu J, Reuter VP, Chang HY, Sheffield NC. PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments. NAR Genom Bioinform 2021; 3:lqab101. [PMID: 34859208 PMCID: PMC8632735 DOI: 10.1093/nargab/lqab101] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 09/30/2021] [Accepted: 11/15/2021] [Indexed: 12/18/2022] Open
Abstract
As chromatin accessibility data from ATAC-seq experiments continues to expand, there is continuing need for standardized analysis pipelines. Here, we present PEPATAC, an ATAC-seq pipeline that is easily applied to ATAC-seq projects of any size, from one-off experiments to large-scale sequencing projects. PEPATAC leverages unique features of ATAC-seq data to optimize for speed and accuracy, and it provides several unique analytical approaches. Output includes convenient quality control plots, summary statistics, and a variety of generally useful data formats to set the groundwork for subsequent project-specific data analysis. Downstream analysis is simplified by a standard definition format, modularity of components, and metadata APIs in R and Python. It is restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also demonstrate the advantage of aligning to the mitochondrial genome serially, which improves the accuracy of alignment statistics and quality control metrics. PEPATAC is a robust and portable first step for any ATAC-seq project. BSD2-licensed code and documentation are available at https://pepatac.databio.org.
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Affiliation(s)
- Jason P Smith
- Center for Public Health Genomics, University of Virginia, VA,22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, VA 22908 USA
| | - M Ryan Corces
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94304, USA
| | - Jin Xu
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94304, USA
| | - Vincent P Reuter
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, PA 19087, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94304, USA
| | - Nathan C Sheffield
- Center for Public Health Genomics, University of Virginia, VA,22908, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, VA 22908 USA
- Department of Public Health Sciences, University of Virginia, VA 22908, USA
- Department of Biomedical Engineering, University of Virginia, VA 22908, USA
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