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      SURF: integrative analysis of a compendium of RNA-seq and CLIP-seq datasets highlights complex governing of alternative transcriptional regulation by RNA-binding proteins

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          Abstract

          Advances in high-throughput profiling of RNA-binding proteins (RBPs) have resulted inCLIP-seq datasets coupled with transcriptome profiling by RNA-seq. However, analysis methods that integrate both types of data are lacking. We describe SURF, Statistical Utility for RBP Functions, for integrative analysis of large collections of CLIP-seq and RNA-seq data. We demonstrate SURF’s ability to accurately detect differential alternative transcriptional regulation events and associate them to local protein-RNA interactions. We apply SURF to ENCODE RBP compendium and carry out downstream analysis with additional reference datasets. The results of this application are browsable at http://www.statlab.wisc.edu/shiny/surf/.

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          Most cited references26

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

            Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ∼1 h of hands-on time.
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              HITS-CLIP yields genome-wide insights into brain alternative RNA processing

              Summary Protein-RNA interactions play critical roles in all aspects of gene expression. Here we develop a genome-wide means of mapping protein-RNA binding sites in vivo, by high throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP). HITS-CLIP analysis of the neuron-specific splicing factor Nova2 revealed extremely reproducible RNA binding maps in multiple mouse brains. These maps provide genome-wide in vivo biochemical footprints confirming the previous prediction that the position of Nova binding determines the outcome of alternative splicing; moreover, they are sufficiently powerful to predict Nova action de novo. HITS-CLIP revealed a large number of Nova-RNA interactions in 3′ UTRs, leading to the discovery that Nova regulates alternative polyadenylation in the brain. HITS-CLIP, therefore, provides a robust, unbiased means to identify functional protein-RNA interactions in vivo.
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                Author and article information

                Contributors
                fan.chen@wisc.edu
                keles@stat.wisc.edu
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                12 June 2020
                12 June 2020
                2020
                : 21
                : 139
                Affiliations
                [1 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Statistics, University of Wisconsin-Madison, ; 1220 Medical Sciences Center, 1300 University Avenue, Madison, 53706 WI USA
                [2 ]GRID grid.14003.36, ISNI 0000 0001 2167 3675, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, ; K6/446 Clinical Sciences Center, 600 Highland Avenue, Madison, 53792-4675 WI USA
                Author information
                http://orcid.org/0000-0001-9048-0922
                Article
                2039
                10.1186/s13059-020-02039-7
                7291511
                efe01e86-cb49-43c3-aeaf-64243c12a2fc
                © The Author(s) 2020

                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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 2 December 2019
                : 8 May 2020
                Categories
                Method
                Custom metadata
                © The Author(s) 2020

                Genetics
                rna-binding protein,clip-seq,rna-seq,integrative modeling,differential analysis,alternative splicing,alternative polyadenylation,transcriptional regulation

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