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      The PUF binding landscape in metazoan germ cells

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          Abstract

          PUF ( Pumilio/ FBF) proteins are RNA-binding proteins and conserved stem cell regulators. The Caenorhabditis elegans PUF proteins FBF-1 and FBF-2 (collectively FBF) regulate mRNAs in germ cells. Without FBF, adult germlines lose all stem cells. A major gap in our understanding of PUF proteins, including FBF, is a global view of their binding sites in their native context (i.e., their “binding landscape”). To understand the interactions underlying FBF function, we used iCLIP (individual-nucleotide resolution UV crosslinking and immunoprecipitation) to determine binding landscapes of C. elegans FBF-1 and FBF-2 in the germline tissue of intact animals. Multiple iCLIP peak-calling methods were compared to maximize identification of both established FBF binding sites and positive control target mRNAs in our iCLIP data. We discovered that FBF-1 and FBF-2 bind to RNAs through canonical as well as alternate motifs. We also analyzed crosslinking-induced mutations to map binding sites precisely and to identify key nucleotides that may be critical for FBF–RNA interactions. FBF-1 and FBF-2 can bind sites in the 5′UTR, coding region, or 3′UTR, but have a strong bias for the 3′ end of transcripts. FBF-1 and FBF-2 have strongly overlapping target profiles, including mRNAs and noncoding RNAs. From a statistically robust list of 1404 common FBF targets, 847 were previously unknown, 154 were related to cell cycle regulation, three were lincRNAs, and 335 were shared with the human PUF protein PUM2.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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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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                Author and article information

                Journal
                RNA
                RNA
                RNA
                RNA
                Cold Spring Harbor Laboratory Press
                1355-8382
                1469-9001
                July 2016
                July 2016
                : 22
                : 7
                : 1026-1043
                Affiliations
                [1 ]Department of Biochemistry, and Howard Hughes Medical Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
                [2 ]Howard Hughes Medical Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
                Author notes

                3These authors contributed equally to this work.

                Abbreviations: CDS, coding sequence; CIMS, crosslinking-induced mutation site; CITS, crosslinking-induced truncation site; FBE, FBF binding element; GO, Gene Ontology; GSC, germline stem cell; HITS-CLIP, high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation; iCLIP, individual-nucleotide resolution UV crosslinking and immunoprecipitation; IP, immunoprecipitation; lincRNA, long intervening noncoding RNA; mRNA, messenger RNA; MS, mass spectrometry; ncRNA, noncoding RNA; PAR-CLIP, photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation; PBE, PUF binding element; RBP, RNA binding protein; RIP-chip, RNA immunoprecipitation followed by microarray analysis of associated mRNAs; SEQRS, selection, high-throughput sequencing of RNA, and sequence specificity landscapes; UTR, untranslated region; UV, ultraviolet; SAM, significance analysis of microarrays

                Corresponding author: jekimble@ 123456wisc.edu
                Article
                9509184 RA
                10.1261/rna.055871.116
                4911911
                27165521
                ad29f6ef-cb57-4ef1-ad45-a917bd3d70d7
                © 2016 Prasad et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society

                This article, published in RNA, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 January 2016
                : 14 April 2016
                Funding
                Funded by: National Institutes of Health http://dx.doi.org/10.13039/100000002
                Award ID: 5T32GM00869217
                Award ID: 5T32GM08349
                Award ID: GM050942
                Award ID: GM069454
                Funded by: Howard Hughes Medical Institute http://dx.doi.org/10.13039/100000011
                Categories
                Article

                fbf,iclip,peak-calling methods,target mrnas,c. elegans,puf proteins,rna,rna-binding proteins

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