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      Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases

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          Abstract

          Genome-wide association studies (GWAS) of neurological diseases have identified thousands of variants associated with disease phenotypes. However, the majority of these variants do not alter coding sequences, making it difficult to assign their function. Here, we present a multi-omic epigenetic atlas of the adult human brain through profiling of single-cell chromatin accessibility landscapes and three-dimensional (3D) chromatin interactions of diverse adult brain regions across a cohort of cognitively healthy individuals. We developed a machine-learning classifier to integrate this multi-omic framework and predict dozens of functional single-nucleotide polymorphisms (SNPs) for Alzheimer’s disease (AD) and Parkinson’s disease (PD), nominating target genes and cell types for previously orphaned GWAS loci. Moreover, we dissected the complex inverted haplotype of the MAPT (encoding tau) PD risk locus, identifying putative ectopic regulatory interactions in neurons that may mediate this disease association. This work expands our understanding of inherited variation and provides a roadmap for the epigenomic dissection of causal regulatory variation in disease.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            BEDTools: a flexible suite of utilities for comparing genomic features

            Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat Genet
                Nature genetics
                1061-4036
                1546-1718
                2 October 2020
                26 October 2020
                November 2020
                26 April 2021
                : 52
                : 11
                : 1158-1168
                Affiliations
                [1 ]Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
                [2 ]Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
                [3 ]Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
                [4 ]Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
                [5 ]Department of Computer Science, Stanford University, Stanford, CA, USA
                [6 ]Program in Biophysics, Stanford University, Stanford, CA, USA
                [7 ]Department of Biology, Stanford University, Stanford, CA, USA
                [8 ]Baidu Research, Sunnyvale, CA, USA
                [9 ]Department of Applied Physics, Stanford University, Stanford, CA, USA
                [10 ]Chan-Zuckerberg Biohub, San Francisco, CA, USA
                [11 ]Program in Epithelial Biology, Stanford University, Stanford, CA, USA
                [12 ]Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
                Author notes

                AUTHOR CONTRIBUTIONS

                M.R.C., H.Y.C., and T.J.M. conceived of and designed the project. M.R.C. and T.J.M. compiled the figures and wrote the manuscript with help and input from all authors. A.S. and M.R.C. performed bulk ATAC-seq data processing and analysis. M.R.C. performed all HiChIP data analysis with help from M.R.M. and J.M.G.. J.M.G., M.R.C., and A.S. performed all single-cell ATAC-seq data processing and analysis with supervision from W.J.G., A.K., S.B.M. and H.Y.C.. M.J.G. performed GWAS locus curation, colocalization analysis, and GTEx analysis and M.J.G., L.F., and B.L. performed all LD score regression analysis with supervision from S.B.M.. S.K. and A.S. performed all machine-learning analysis with supervision from A.K.. S.K. and T.E. performed allelic imbalance analyses with supervision from A.K. and S.B.M.. B.H.L., S.S., and M.R.C. performed all ATAC-seq, scATAC-seq, and HiChIP data generation with help from S.T.B. and M.R.M.. K.S.M. curated the frozen tissue specimens used in this work.

                [* ] Contact Information: Thomas J. Montine, MD, PhD, Stanford University School of Medicine, Lane L235, 300 Pasteur Dr., Stanford, CA, 94305-5324, tmontine@ 123456stanford.edu , Phone: 650-725-9352; Howard Y. Chang, MD, PhD, Stanford University School of Medicine, CCSR 2155c, 269 Campus Drive, Stanford, CA 94305-5168, howchang@ 123456stanford.edu , Phone: 650-736-0306
                Article
                NIHMS1630836
                10.1038/s41588-020-00721-x
                7606627
                33106633
                7da4946f-339d-4f10-96b1-17da01f73a75

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                Genetics
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