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      Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure

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          Abstract

          RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome and a subsequent motif enrichment analysis suggest potential links of RNA structural variation and mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome.

          Abstract

          The authors present DiffScan, an advanced tool for normalization and differential analysis of RNA structure probing experiments, combining their power in deciphering the dynamic RNA structurome and facilitating the discovery of RNA regulatory functions.

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

            Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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              MEME Suite: tools for motif discovery and searching

              The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://meme.nbcr.net.
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                Author and article information

                Contributors
                qczhang@tsinghua.edu.cn
                houl@tsinghua.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                22 July 2022
                22 July 2022
                2022
                : 13
                : 4227
                Affiliations
                [1 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Center for Statistical Science, Department of Industrial Engineering, , Tsinghua University, ; Beijing, China
                [2 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, MOE Key Laboratory of Bioinformatics, School of Life Sciences, , Tsinghua University, ; Beijing, China
                [3 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Center for Synthetic and Systems Biology, Beijing Advanced Innovation Center for Structural Biology, Tsinghua-Peking Joint Center for Life Sciences, School of Life Sciences, , Tsinghua University, ; Beijing, China
                Author information
                http://orcid.org/0000-0003-4689-9341
                http://orcid.org/0000-0002-4913-0338
                http://orcid.org/0000-0002-4283-8501
                Article
                31875
                10.1038/s41467-022-31875-3
                9307511
                30176c56-5364-483a-b0ad-b8c276c012d0
                © 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
                : 14 August 2021
                : 5 July 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 91940306
                Award Recipient :
                Funded by: National Natural Science Foundation of China, Grant No. 12071243
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                statistical methods,transcriptomics
                Uncategorized
                statistical methods, transcriptomics

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