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      Transcriptome analysis reveals long intergenic non-coding RNAs involved in skeletal muscle growth and development in pig

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          Abstract

          Long intergenic non-coding RNAs (lincRNAs) play essential roles in numerous biological processes and are widely studied. The skeletal muscle is an important tissue that plays an essential role in individual movement ability. However, lincRNAs in pig skeletal muscles are largely undiscovered and their biological functions remain elusive. In this study, we assembled transcriptomes using RNA-seq data published in previous studies of our laboratory group and identified 323 lincRNAs in porcine leg muscle. We found that these lincRNAs have shorter transcript length, fewer exons and lower expression level than protein-coding genes. Gene ontology and pathway analyses indicated that many potential target genes (PTGs) of lincRNAs were involved in skeletal-muscle-related processes, such as muscle contraction and muscle system process. Combined our previous studies, we found a potential regulatory mechanism in which the promoter methylation of lincRNAs can negatively regulate lincRNA expression and then positively regulate PTG expression, which can finally result in abnormal phenotypes of cloned piglets through a certain unknown pathway. This work detailed a number of lincRNAs and their target genes involved in skeletal muscle growth and development and can facilitate future studies on their roles in skeletal muscle growth and development.

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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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            BEDTools: a flexible suite of utilities for comparing genomic features

            Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              TopHat: discovering splice junctions with RNA-Seq

              Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                lichangchun@mail.hzau.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 August 2017
                18 August 2017
                2017
                : 7
                Affiliations
                ISNI 0000 0004 1790 4137, GRID grid.35155.37, Key Lab of Agriculture Animal Genetics, Breeding, , and Reproduction of Ministry of Education, College of Animal Sciences and Technology, Huazhong Agricultural University, ; Wuhan, 430070 People’s Republic of China
                Article
                7998
                10.1038/s41598-017-07998-9
                5562803
                28821716
                © The Author(s) 2017

                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/.

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