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      The genome-wide impact of trisomy 21 on DNA methylation and its implications for hematopoiesis

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          Abstract

          Down syndrome is associated with genome-wide perturbation of gene expression, which may be mediated by epigenetic changes. We perform an epigenome-wide association study on neonatal bloodspots comparing 196 newborns with Down syndrome and 439 newborns without Down syndrome, adjusting for cell-type heterogeneity, which identifies 652 epigenome-wide significant CpGs ( P < 7.67 × 10 −8) and 1,052 differentially methylated regions. Differential methylation at promoter/enhancer regions correlates with gene expression changes in Down syndrome versus non-Down syndrome fetal liver hematopoietic stem/progenitor cells ( P < 0.0001). The top two differentially methylated regions overlap RUNX1 and FLI1, both important regulators of megakaryopoiesis and hematopoietic development, with significant hypermethylation at promoter regions of these two genes. Excluding Down syndrome newborns harboring preleukemic GATA1 mutations ( N = 30), identified by targeted sequencing, has minimal impact on the epigenome-wide association study results. Down syndrome has profound, genome-wide effects on DNA methylation in hematopoietic cells in early life, which may contribute to the high frequency of hematological problems, including leukemia, in children with Down syndrome.

          Abstract

          Down syndrome has a high co-morbidity with immune and hematopoietic disorders. Here, the authors perform an epigenome-wide association study in newborns with and without Down syndrome to find differential methylation across the genome, including in hematopoietic regulators RUNX1 and FLI1.

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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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            Complex heatmaps reveal patterns and correlations in multidimensional genomic data.

            Parallel heatmaps with carefully designed annotation graphics are powerful for efficient visualization of patterns and relationships among high dimensional genomic data. Here we present the ComplexHeatmap package that provides rich functionalities for customizing heatmaps, arranging multiple parallel heatmaps and including user-defined annotation graphics. We demonstrate the power of ComplexHeatmap to easily reveal patterns and correlations among multiple sources of information with four real-world datasets.
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              The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019

              Abstract The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.
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                Author and article information

                Contributors
                adam.desmith@med.usc.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                5 February 2021
                5 February 2021
                2021
                : 12
                : 821
                Affiliations
                [1 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, Center for Genetic Epidemiology, Department of Preventive Medicine, , Keck School of Medicine of the University of Southern California, ; Los Angeles, CA USA
                [2 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, Norris Comprehensive Cancer Center, , University of Southern California, ; Los Angeles, USA
                [3 ]GRID grid.421962.a, ISNI 0000 0004 0641 4431, Department of Paediatrics and MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, Oxford University and BRC Blood Theme, , NIHR Oxford Biomedical Centre, ; Oxford, UK
                [4 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Neurological Surgery, , University of California San Francisco, ; San Francisco, CA USA
                [5 ]GRID grid.39382.33, ISNI 0000 0001 2160 926X, Department of Pediatrics, Section of Hematology-Oncology, , Baylor College of Medicine, ; Houston, TX USA
                [6 ]GRID grid.416975.8, ISNI 0000 0001 2200 2638, Texas Children’s Cancer and Hematology Centers, , Texas Children’s Hospital, ; Houston, TX USA
                [7 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Computational Biology and Informatics, , University of California San Francisco, ; San Francisco, CA USA
                [8 ]GRID grid.42505.36, ISNI 0000 0001 2156 6853, Department of Preventive Medicine, , Keck School of Medicine of the University of Southern California, ; Los Angeles, CA USA
                [9 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, , University of Oxford, ; Oxford, UK
                [10 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Neurosurgery, , Duke University, ; Durham, NC USA
                [11 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Pediatrics, , Duke University, ; Durham, NC USA
                [12 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Chronic Disease Epidemiology, , Yale School of Public Health, ; New Haven, CT USA
                Author information
                http://orcid.org/0000-0002-0544-5338
                http://orcid.org/0000-0001-7599-9726
                http://orcid.org/0000-0002-4934-4125
                http://orcid.org/0000-0002-5879-9981
                http://orcid.org/0000-0003-3931-0914
                http://orcid.org/0000-0001-8607-5748
                http://orcid.org/0000-0003-4880-7543
                Article
                21064
                10.1038/s41467-021-21064-z
                7865055
                33547282
                ad388282-8547-4cc4-aaae-2644c238ef12
                © 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
                : 20 August 2020
                : 5 January 2021
                Funding
                Funded by: Blood Cancer UK Specialist Programme Grant 13001 NIHR Oxford Biomedical Centre Research Fund
                Funded by: FundRef https://doi.org/10.13039/100000054, U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI);
                Award ID: R01CA175737
                Award ID: 3R01CA175737-05S1
                Award ID: R01CA175737
                Award ID: 3R01CA175737-05S1
                Award ID: 3R01CA175737-05S1
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
                Funded by: FundRef https://doi.org/10.13039/100012117, Lady Tata Memorial Trust;
                Award ID: Lady Tata Memorial International Fellowship
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: Wellcome Clinical Research Career Development Fellowship (216632/Z/19/Z)
                Award Recipient :
                Funded by: Blood Cancer UK Clinician Scientist Fellowship (17001)
                Funded by: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
                Funded by: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
                Funded by: FundRef https://doi.org/10.13039/100001445, Alex’s Lemonade Stand Foundation for Childhood Cancer (Alex’s Lemonade Stand Foundation);
                Award ID: ‘A’ Award
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                methylation analysis,haematopoiesis,dna methylation,epidemiology
                Uncategorized
                methylation analysis, haematopoiesis, dna methylation, epidemiology

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