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      Transcription readthrough is prevalent in healthy human tissues and associated with inherent genomic features

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          Abstract

          Transcription termination is a crucial step in the production of conforming mRNAs and functional proteins. Under cellular stress conditions, the transcription machinery fails to identify the termination site and continues transcribing beyond gene boundaries, a phenomenon designated as transcription readthrough. However, the prevalence and impact of this phenomenon in healthy human tissues remain unexplored. Here, we assessed transcription readthrough in almost 3000 transcriptome profiles representing 23 human tissues and found that 34% of the expressed protein-coding genes produced readthrough transcripts. The production of readthrough transcripts was restricted in genomic regions with high transcriptional activity and was associated with inefficient splicing and increased chromatin accessibility in terminal regions. In addition, we showed that these transcripts contained several binding sites for the same miRNA, unravelling a potential role as miRNA sponges. Overall, this work provides evidence that transcription readthrough is pervasive and non-stochastic, not only in abnormal conditions but also in healthy tissues. This suggests a potential role for such transcripts in modulating normal cellular functions.

          Abstract

          A comprehensive computational analysis of 3000 transcriptome profiles shows that transcription readthrough is pervasive and non-stochastic in both abnormal conditions and in healthy tissues.

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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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              Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.

              Genome-scale studies have revealed extensive, cell type-specific colocalization of transcription factors, but the mechanisms underlying this phenomenon remain poorly understood. Here, we demonstrate in macrophages and B cells that collaborative interactions of the common factor PU.1 with small sets of macrophage- or B cell lineage-determining transcription factors establish cell-specific binding sites that are associated with the majority of promoter-distal H3K4me1-marked genomic regions. PU.1 binding initiates nucleosome remodeling, followed by H3K4 monomethylation at large numbers of genomic regions associated with both broadly and specifically expressed genes. These locations serve as beacons for additional factors, exemplified by liver X receptors, which drive both cell-specific gene expression and signal-dependent responses. Together with analyses of transcription factor binding and H3K4me1 patterns in other cell types, these studies suggest that simple combinations of lineage-determining transcription factors can specify the genomic sites ultimately responsible for both cell identity and cell type-specific responses to diverse signaling inputs. Copyright 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                p.caldas@fct.unl.pt
                argrosso@fct.unl.pt
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                15 January 2024
                15 January 2024
                2024
                : 7
                : 100
                Affiliations
                [1 ]Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, Universidade NOVA de Lisboa, ( https://ror.org/02xankh89) 2829-516 Caparica, Portugal
                [2 ]UCIBIO – Applied Molecular Biosciences Unit, Department of Life Sciences, NOVA School of Science and Technology, Universidade NOVA de Lisboa, ( https://ror.org/02xankh89) 2829-516 Caparica, Portugal
                [3 ]Present Address: Genomics and Bioinformatics Unit, Department of Infectious Diseases, National Institute of Health Doutor Ricardo Jorge (INSA), ( https://ror.org/03mx8d427) Lisbon, Portugal
                Author information
                http://orcid.org/0000-0001-6730-4461
                http://orcid.org/0000-0003-0165-3807
                http://orcid.org/0000-0003-4006-8205
                http://orcid.org/0000-0003-3955-0117
                http://orcid.org/0000-0001-6974-4209
                Article
                5779
                10.1038/s42003-024-05779-5
                10789751
                38225287
                ee8a9fef-5c6c-4d83-af34-fcafe2c2e8ee
                © The Author(s) 2024

                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
                : 27 March 2023
                : 4 January 2024
                Funding
                Funded by: Fundação para a Ciência e Tecnologia (FCT) in the scope of the projects UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences (UCIBIO) and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy - i4HB.
                Funded by: Marie Skłodowska-Curie Postdoctoral Fellowship FOX-MTN-HORIZON-MSCA - 2021-PF-01-01
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                genome informatics,gene regulation
                genome informatics, gene regulation

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