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      Genome-wide identification and characterization of long non-coding RNAs in developmental skeletal muscle of fetal goat

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          Abstract

          Background

          Long non-coding RNAs (lncRNAs) have been studied extensively over the past few years. Large numbers of lncRNAs have been identified in mouse, rat, and human, and some of them have been shown to play important roles in muscle development and myogenesis. However, there are few reports on the characterization of lncRNAs covering all the development stages of skeletal muscle in livestock.

          Results

          RNA libraries constructed from developing longissimus dorsi muscle of fetal (45, 60, and 105 days of gestation) and postnatal (3 days after birth) goat ( Capra hircus) were sequenced. A total of 1,034,049,894 clean reads were generated. Among them, 3981 lncRNA transcripts corresponding to 2739 lncRNA genes were identified, including 3515 intergenic lncRNAs and 466 anti-sense lncRNAs. Notably, in pairwise comparisons between the libraries of skeletal muscle at the different development stages, a total of 577 transcripts were differentially expressed ( P < 0.05) which were validated by qPCR using randomly selected six lncRNA genes. The identified goat lncRNAs shared some characteristics, such as fewer exons and shorter length, with the lncRNAs in other mammals. We also found 1153 lncRNAs genes were neighbored 1455 protein-coding genes (<10 kb upstream and downstream) and functionally enriched in transcriptional regulation and development-related processes, indicating they may be in cis-regulatory relationships. Additionally, Pearson’s correlation coefficients of co-expression levels suggested 1737 lncRNAs and 19,422 mRNAs were possibly in trans-regulatory relationships ( r > 0.95 or r < −0.95). These co-expressed mRNAs were enriched in development-related biological processes such as muscle system processes, regulation of cell growth, muscle cell development, regulation of transcription, and embryonic morphogenesis.

          Conclusions

          This study provides a catalog of goat muscle-related lncRNAs, and will contribute to a fuller understanding of the molecular mechanism underpinning muscle development in mammals.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-016-3009-3) contains supplementary material, which is available to authorized users.

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          Most cited references 75

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

           K Livak,  T Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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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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              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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                Author and article information

                Contributors
                siyuan_zhan@163.com
                13882446475@163.com
                qiangweifenfei1991@163.com
                jiazhong.guo@sicau.edu.cn
                zhongtao@sicau.edu.cn
                wanglinjie@sicau.edu.cn
                lilyzh002@sohu.com
                zhp@sicau.edu.cn
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                22 August 2016
                22 August 2016
                2016
                : 17
                : 1
                Affiliations
                Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130 China
                Article
                3009
                10.1186/s12864-016-3009-3
                4994410
                27550073
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 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.

                Funding
                Funded by: Sichuan Province Science and Technology Support Program
                Award ID: 2014NZ0077, 2015NZ0112, 16ZC2849
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Genetics

                lncrna, muscle development, goat, transcriptome, cis-acting, trans-acting, differential expression

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