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      ZRSR1 co-operates with ZRSR2 in regulating splicing of U12-type introns in murine hematopoietic cells

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          Abstract

          Recurrent loss-of-function mutations of spliceosome gene, ZRSR2, occur in myelodysplastic syndromes (MDS). Mutation/loss of ZRSR2 in human myeloid cells primarily causes impaired splicing of the U12-type introns. In order to further investigate the role of this splice factor in RNA splicing and hematopoietic development, we generated mice lacking ZRSR2. Unexpectedly, Zrsr2-deficient mice developed normal hematopoiesis with no abnormalities in myeloid differentiation evident in either young or ≥1-year old knockout mice. Repopulation ability of Zrsr2-deficient hematopoietic stem cells was also unaffected in both competitive and non-competitive reconstitution assays. Myeloid progenitors lacking ZRSR2 exhibited mis-splicing of U12-type introns, however, this phenotype was moderate compared to the ZRSR2-deficient human cells. Our investigations revealed that a closely related homolog, Zrsr1, expressed in the murine hematopoietic cells, but not in human cells contributes to splicing of U12-type introns. Depletion of Zrsr1 in Zrsr2 KO myeloid cells exacerbated retention of the U12-type introns, thus highlighting a collective role of ZRSR1 and ZRSR2 in murine U12-spliceosome. We also demonstrate that aberrant retention of U12-type introns of MAPK9 and MAPK14 leads to their reduced protein expression. Overall, our findings highlight that both ZRSR1 and ZRSR2 are functional components of the murine U12-spliceosome, and depletion of both proteins is required to accurately model ZRSR2-mutant MDS in mice.

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          Most cited references49

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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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            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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              GENCODE reference annotation for the human and mouse genomes

              Abstract The accurate identification and description of the genes in the human and mouse genomes is a fundamental requirement for high quality analysis of data informing both genome biology and clinical genomics. Over the last 15 years, the GENCODE consortium has been producing reference quality gene annotations to provide this foundational resource. The GENCODE consortium includes both experimental and computational biology groups who work together to improve and extend the GENCODE gene annotation. Specifically, we generate primary data, create bioinformatics tools and provide analysis to support the work of expert manual gene annotators and automated gene annotation pipelines. In addition, manual and computational annotation workflows use any and all publicly available data and analysis, along with the research literature to identify and characterise gene loci to the highest standard. GENCODE gene annotations are accessible via the Ensembl and UCSC Genome Browsers, the Ensembl FTP site, Ensembl Biomart, Ensembl Perl and REST APIs as well as https://www.gencodegenes.org.
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                Author and article information

                Journal
                Haematologica
                Haematologica
                HAEMA
                Haematologica
                Fondazione Ferrata Storti
                0390-6078
                1592-8721
                11 March 2021
                01 March 2022
                : 107
                : 3
                : 680-689
                Affiliations
                [1 ]Cancer Science Institute of Singapore, National University of Singapore , Singapore
                [2 ]Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore , Singapore
                [3 ]Programme in Cancer and Stem Cell Biology, Duke–NUS Medical School , Singapore
                [4 ]School of Biological Sciences, Nanyang Technological University , Singapore
                [5 ]Hematology-Oncology, National University Cancer Institute , National University Hospital Singapore, Singapore
                [6 ]Cedars-Sinai Medical Center, Division of Hematology/Oncology, UCLA School of Medicine , Los Angeles, CA, USA
                [7 ]National University Cancer Institute, National University Hospital Singapore , Singapore
                Author notes
                *VM and ZC contributed equally as co-first authors.
                #WJC and HPK contributed equally as co-senior authors

                Disclosures

                No conflicts of interest to disclose.

                Contributions

                VM conceived the study, designed and performed research, analysed data and wrote the manuscript; ZC designed and performed research, analysed data and wrote the manuscript; WWT, LH, PS and MJ performed research and analysed data; PD performed bioinformatics and statistical analyses and wrote the manuscript; SZ, JL and HY performed and supervised bioinformatics and statistical analyses; SJ, YS and MZH performed blastocyst injections to generate chimeras from targeted ES cells; WJC supervised the study and wrote the manuscript; HPK conceived and supervised the study, interpreted the data and wrote the manuscript. All authors reviewed and approved the manuscript.

                Article
                10.3324/haematol.2020.260562
                8883539
                33691379
                61db1271-6a4f-4ccb-bf8b-364e9c596420
                Copyright© 2022 Ferrata Storti Foundation

                This article is distributed under the terms of the Creative Commons Attribution Noncommercial License ( by-nc 4.0) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

                History
                : 24 May 2020
                : 01 March 2021
                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 48, Pages: 10
                Funding
                Funding : This work was funded by the Leukemia and Lymphoma Society, the Singapore Ministry of Health’s National Medical Research Council (NMRC) under its Singapore Translational Research (STaR) Investigator Award to HPK (NMRC/STaR/0021/2014), the NMRC Center Grant awarded to the National University Cancer Institute of Singapore (NMRC/CG/012/2013) and the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centers of Excellence initiatives. This research is also supported by the RNA Biology Center at the Cancer Science Institute of Singapore, NUS, as part of funding under the Singapore Ministry of Education’s Tier 3 grants, grant number MOE2014-T3-1-006. We thank the Melamed Family for their generous support.
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