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      Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney

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          Abstract

          The integration of single cell transcriptome and chromatin accessibility datasets enables a deeper understanding of cell heterogeneity. We performed single nucleus ATAC (snATAC-seq) and RNA (snRNA-seq) sequencing to generate paired, cell-type-specific chromatin accessibility and transcriptional profiles of the adult human kidney. We demonstrate that snATAC-seq is comparable to snRNA-seq in the assignment of cell identity and can further refine our understanding of functional heterogeneity in the nephron. The majority of differentially accessible chromatin regions are localized to promoters and a significant proportion are closely associated with differentially expressed genes. Cell-type-specific enrichment of transcription factor binding motifs implicates the activation of NF-κB that promotes VCAM1 expression and drives transition between a subpopulation of proximal tubule epithelial cells. Our multi-omics approach improves the ability to detect unique cell states within the kidney and redefines cellular heterogeneity in the proximal tubule and thick ascending limb.

          Abstract

          Single cell transcriptomic and epigenomic sequencing of human kidney highlight diverse cell types and states. These findings help characterize a novel population of injured proximal tubule cells and illustrate the power of multi-omic approaches to characterizing human tissue.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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                Author and article information

                Contributors
                humphreysbd@wustl.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 April 2021
                13 April 2021
                2021
                : 12
                : 2190
                Affiliations
                [1 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Division of Nephrology, Department of Medicine, , Washington University in St. Louis, ; St. Louis, MO USA
                [2 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Pathology and Immunology, , Washington University in St. Louis, ; St. Louis, MO USA
                [3 ]GRID grid.10825.3e, ISNI 0000 0001 0728 0170, Department of Cardiovascular and Renal Research, , Institute of Molecular Medicine, University of Southern Denmark, ; Odense, Denmark
                [4 ]GRID grid.7143.1, ISNI 0000 0004 0512 5013, Department of Nephrology, , Odense University Hospital, ; Odense, Denmark
                [5 ]GRID grid.189504.1, ISNI 0000 0004 1936 7558, Section of Nephrology, Department of Medicine, , Boston University School of Medicine and Boston Medical Center, ; Boston, MA USA
                [6 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Developmental Biology, , Washington University in St. Louis, ; St. Louis, MO USA
                Author information
                http://orcid.org/0000-0002-0358-9442
                http://orcid.org/0000-0001-8647-9662
                http://orcid.org/0000-0002-9237-3634
                http://orcid.org/0000-0002-9170-2168
                http://orcid.org/0000-0003-4004-326X
                http://orcid.org/0000-0002-6420-8703
                Article
                22368
                10.1038/s41467-021-22368-w
                8044133
                33850129
                558c1e67-f1e1-4afa-b548-765d4ed1ef97
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 June 2020
                : 11 March 2021
                Funding
                Funded by: Chan Zuckerberg Initiative CZF2019-002430
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                gene regulatory networks,genome informatics,epigenetics,kidney
                Uncategorized
                gene regulatory networks, genome informatics, epigenetics, kidney

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