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      Genome-scale pan-cancer interrogation of lncRNA dependencies using CasRx

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          Abstract

          Although long noncoding RNAs (lncRNAs) dominate the transcriptome, their functions are largely unexplored. The extensive overlap of lncRNAs with coding and regulatory sequences restricts their systematic interrogation by DNA-directed perturbation. Here we developed genome-scale lncRNA transcriptome screening using Cas13d/CasRx. We show that RNA targeting overcomes limitations inherent to other screening methods, thereby considerably expanding the explorable space of the lncRNAome. By evolving the screening system toward pan-cancer applicability, it supports molecular and phenotypic data integration to contextualize screening hits or infer lncRNA function. We thereby addressed challenges posed by the enormous transcriptome size and tissue specificity through a size-reduced multiplexed gRNA library termed Albarossa, targeting 24,171 lncRNA genes. Its rational design incorporates target prioritization based on expression, evolutionary conservation and tissue specificity, thereby reconciling high discovery power and pan-cancer representation with scalable experimental throughput. Applied across entities, the screening platform identified numerous context-specific and common essential lncRNAs. Our work sets the stage for systematic exploration of lncRNA biology in health and disease.

          Abstract

          A CasRx-based screening platform for genome-scale long noncoding RNA transcriptome perturbation is described.

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          Most cited references105

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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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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              The Sequence Alignment/Map format and SAMtools

              Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                jj.montero@tum.de
                roland.rad@tum.de
                Journal
                Nat Methods
                Nat Methods
                Nature Methods
                Nature Publishing Group US (New York )
                1548-7091
                1548-7105
                26 February 2024
                26 February 2024
                2024
                : 21
                : 4
                : 584-596
                Affiliations
                [1 ]Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, ( https://ror.org/02kkvpp62) Munich, Germany
                [2 ]Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technische Universität München, ( https://ror.org/02kkvpp62) Munich, Germany
                [3 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, German Cancer Consortium (DKTK), , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [4 ]Institute of Experimental Hematology, School of Medicine, Technical University of Munich, ( https://ror.org/02kkvpp62) Munich, Germany
                [5 ]GRID grid.6936.a, ISNI 0000000123222966, Department of Medicine II, , Klinikum rechts der Isar, School of Medicine, Technische Universität München, ; Munich, Germany
                [6 ]Institute for Experimental Cancer Therapy, School of Medicine, Technische Universität München, ( https://ror.org/02kkvpp62) Munich, Germany
                Author information
                http://orcid.org/0000-0002-5131-3205
                http://orcid.org/0000-0002-5889-994X
                http://orcid.org/0009-0002-5968-4964
                http://orcid.org/0000-0002-2292-5982
                http://orcid.org/0000-0002-2868-5107
                http://orcid.org/0000-0001-5874-0210
                http://orcid.org/0000-0002-6849-9659
                Article
                2190
                10.1038/s41592-024-02190-0
                11009108
                38409225
                c9a60c4c-c659-4c3c-be7b-1aedf3f656ba
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 March 2023
                : 19 January 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: DFG RA1629/2-1
                Award ID: SFB1321
                Award ID: SFB1371
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100005972, Deutsche Krebshilfe (German Cancer Aid);
                Award ID: (70114314)
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: Consolidator grant PACA-MET (819642)
                Award ID: MSCA-ITNETN (861196
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100004410, European Molecular Biology Organization (EMBO);
                Award ID: Long-Term Fellowship (ALFT 655-2019).
                Award Recipient :
                Funded by: the German Federal Ministry of Education and Research (Cluster4Future: CNATM)
                Categories
                Article
                Custom metadata
                © Springer Nature America, Inc. 2024

                Life sciences
                functional genomics,genetics research,cancer genetics,genomics
                Life sciences
                functional genomics, genetics research, cancer genetics, genomics

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