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      BACH1 recruits NANOG and histone H3 lysine 4 methyltransferase MLL/SET1 complexes to regulate enhancer–promoter activity and maintains pluripotency

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          Abstract

          Maintenance of stem-cell identity requires proper regulation of enhancer activity. Both transcription factors OCT4/SOX2/NANOG and histone methyltransferase complexes MLL/SET1 were shown to regulate enhancer activity, but how they are regulated in embryonic stem cells (ESCs) remains further studies. Here, we report a transcription factor BACH1, which directly interacts with OCT4/SOX2/NANOG (OSN) and MLL/SET1 methyltransferase complexes and maintains pluripotency in mouse ESCs (mESCs). BTB domain and bZIP domain of BACH1 are required for these interactions and pluripotency maintenance. Loss of BACH1 reduced the interaction between NANOG and MLL1/SET1 complexes, and decreased their occupancy on chromatin, and further decreased H3 lysine 4 trimethylation (H3K4me3) level on gene promoters and (super-) enhancers, leading to decreased enhancer activity and transcription activity, especially on stemness-related genes. Moreover, BACH1 recruited NANOG through chromatin looping and regulated remote NANOG binding, fine-tuning enhancer–promoter activity and gene expression. Collectively, these observations suggest that BACH1 maintains pluripotency in ESCs by recruiting NANOG and MLL/SET1 complexes to chromatin and maintaining the trimethylated state of H3K4 and enhancer–promoter activity, especially on stemness-related genes.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              HISAT: a fast spliced aligner with low memory requirements.

              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                26 February 2021
                27 January 2021
                27 January 2021
                : 49
                : 4
                : 1972-1986
                Affiliations
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
                Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University , Beijing 100871, China
                CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Perinatal Institute, Division of Pulmonary Biology, Cincinnati Children's Hospital Medical Center , Cincinnati, OH 45229, USA
                Department of Biomedical Engineering, University of Alabama at Birmingham , Birmingham, AL 35294, USA
                CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences , Shanghai 200031, China
                Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University , Beijing 100871, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules; Institutes of Biomedical Sciences, Fudan University , Shanghai 200032, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 21 54237392; Fax: +86 21 54237392; Email: dmeng@ 123456fudan.edu.cn
                Correspondence may also be addressed to Fei Lan. Tel: +86 21 54237874; Fax: +86 21 54237874; Email: fei_lan@ 123456fudan.edu.cn

                The authors wish it to be known that, in their opinion, the first five authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0001-8588-0235
                Article
                gkab034
                10.1093/nar/gkab034
                7913776
                33503260
                85e9b230-7809-4f99-a82c-95c10ed7bace
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 12 January 2021
                : 08 January 2021
                : 03 December 2020
                Page count
                Pages: 15
                Funding
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 92068202
                Award ID: 81873469
                Funded by: Shanghai Science and Technology Commission of China;
                Award ID: 19JC1411300
                Funded by: Program of Shanghai Academic/Technology Research Leader, DOI 10.13039/501100012247;
                Award ID: 20XD1400600
                Funded by: National Key Research and Development Program of China, DOI 10.13039/501100012166;
                Award ID: 2018YFC2000202
                Award ID: 2018YFA0108700
                Funded by: Education Commission of Shanghai Municipality, DOI 10.13039/501100003395;
                Award ID: ZDSYS14005
                Categories
                AcademicSubjects/SCI00010
                Gene regulation, Chromatin and Epigenetics

                Genetics
                Genetics

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