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      Systematic characterization of cancer transcriptome at transcript resolution

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          Abstract

          Transcribed RNAs undergo various regulation and modification to become functional transcripts. Notably, cancer transcriptome has not been fully characterized at transcript resolution. Herein, we carry out a reference-based transcript assembly across >1000 cancer cell lines. We identify 498,255 transcripts, approximately half of which are unannotated. Unannotated transcripts are closely associated with cancer-related hallmarks and show clinical significance. We build a high-confidence RNA binding protein (RBP)-transcript regulatory network, wherein most RBPs tend to regulate transcripts involved in cell proliferation. We identify numerous transcripts that are highly associated with anti-cancer drug sensitivity. Furthermore, we establish RBP-transcript-drug axes, wherein PTBP1 is experimentally validated to affect the sensitivity to decitabine by regulating KIAA1522-a6 transcript. Finally, we establish a user-friendly data portal to serve as a valuable resource for understanding cancer transcriptome diversity and its potential clinical utility at transcript level. Our study substantially extends cancer RNA repository and will facilitate anti-cancer drug discovery.

          Abstract

          Modification of transcribed mRNAs enables regulation of transcription but its extent in cancer cells is incompletely understood. Here, the authors analyse transcript assembly in over 1000 cancer cell lines and find unannotated transcripts are common, and are associated with drug sensitivity.

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          Most cited references70

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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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              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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                Author and article information

                Contributors
                xhhe@fudan.edu.cn
                liuteng@alumni.hust.edu.cn
                shengli.li@sjtu.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                10 November 2022
                10 November 2022
                2022
                : 13
                : 6803
                Affiliations
                [1 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, , Shanghai Jiao Tong University School of Medicine, ; Shanghai, 201620 China
                [2 ]GRID grid.452404.3, ISNI 0000 0004 1808 0942, Department of Gynecological Oncology, , Fudan University Shanghai Cancer Center, ; Shanghai, 200032 China
                [3 ]GRID grid.11841.3d, ISNI 0000 0004 0619 8943, Fudan University Shanghai Cancer Center and Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, ; Shanghai, 200032 China
                [4 ]GRID grid.440657.4, ISNI 0000 0004 1762 5832, Institute of Big Data and Artificial Intelligence in Medicine, School of Electronics and Information Engineering, , Taizhou University, ; Taizhou, 318000 China
                Author information
                http://orcid.org/0000-0001-8872-668X
                http://orcid.org/0000-0001-5430-303X
                Article
                34568
                10.1038/s41467-022-34568-z
                9649690
                36357395
                798546e0-fba0-49e5-ab3d-b34f0f92a210
                © The Author(s) 2022

                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
                : 23 February 2022
                : 31 October 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 32100517
                Award Recipient :
                Categories
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                © The Author(s) 2022

                Uncategorized
                cancer genomics,data mining,regulatory networks,cancer therapy,transcriptomics
                Uncategorized
                cancer genomics, data mining, regulatory networks, cancer therapy, transcriptomics

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