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      Identification of differentially expressed genes through RNA sequencing in goats ( Capra hircus) at different postnatal stages

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          Abstract

          Intramuscular fat (IMF) content and fatty acid composition of longissimus dorsi muscle (LM) change with growth, which partially determines the flavor and nutritional value of goat ( Capra hircus) meat. However, unlike cattle, little information is available on the transcriptome-wide changes during different postnatal stages in small ruminants, especially goats. In this study, the sequencing reads of goat LM tissues collected from kid, youth, and adult period were mapped to the goat genome. Results showed that out of total 24 689 Unigenes, 20 435 Unigenes were annotated. Based on expected number of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM), 111 annotated differentially expressed genes (DEGs) were identified among different postnatal stages, which were subsequently assigned to 16 possible expression patterns by series-cluster analysis. Functional classification by Gene Ontology (GO) analysis was used for selecting the genes showing highest expression related to lipid metabolism. Finally, we identified the node genes for lipid metabolism regulation using co-expression analysis. In conclusion, these data may uncover candidate genes having functional roles in regulation of goat muscle development and lipid metabolism during the various growth stages in goats.

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          STEM: a tool for the analysis of short time series gene expression data

          Background Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at .
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            Network modeling links breast cancer susceptibility and centrosome dysfunction.

            Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.
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              Sequencing and automated whole-genome optical mapping of the genome of a domestic goat (Capra hircus).

              We report the ∼2.66-Gb genome sequence of a female Yunnan black goat. The sequence was obtained by combining short-read sequencing data and optical mapping data from a high-throughput whole-genome mapping instrument. The whole-genome mapping data facilitated the assembly of super-scaffolds >5× longer by the N50 metric than scaffolds augmented by fosmid end sequencing (scaffold N50 = 3.06 Mb, super-scaffold N50 = 16.3 Mb). Super-scaffolds are anchored on chromosomes based on conserved synteny with cattle, and the assembly is well supported by two radiation hybrid maps of chromosome 1. We annotate 22,175 protein-coding genes, most of which were recovered in the RNA-seq data of ten tissues. Comparative transcriptomic analysis of the primary and secondary follicles of a cashmere goat reveal 51 genes that are differentially expressed between the two types of hair follicles. This study, whose results will facilitate goat genomics, shows that whole-genome mapping technology can be used for the de novo assembly of large genomes.
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                Author and article information

                Contributors
                Role: Data curationRole: Writing – review & editing
                Role: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: Supervision
                Role: MethodologyRole: Validation
                Role: MethodologyRole: Validation
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 August 2017
                2017
                : 12
                : 8
                : e0182602
                Affiliations
                [001]Key Laboratory of Sichuan Province for Qinghai-Tibetan Plateau Animal Genetic Reservation and Exploitation, Chengdu, Sichuan, P. R. China
                Kunming University of Science and Technology, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9238-3317
                Article
                PONE-D-17-15292
                10.1371/journal.pone.0182602
                5553645
                28800357
                cdebd456-c185-48c0-92a8-8c197ff96db2
                © 2017 Lin et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 19 April 2017
                : 23 July 2017
                Page count
                Figures: 5, Tables: 0, Pages: 15
                Funding
                Funded by: Science and technology support program of Sichuan Province
                Award ID: 2016NYZ0045
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31672395
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31601921
                Award Recipient :
                Funded by: Basic Research Programs of Sichuan Province (CN)
                Award ID: 2016JY0147
                Award Recipient :
                Funded by: Animal Science Discipline Program of Southwest University for Nationalities
                Award ID: 2014XWD-S0905
                Award Recipient :
                This work was jointly supported by the ‘Science and Technology Support Program of Sichuan Province (2016NYZ0045)’, ‘National Natural Science Foundation of China (31672395 and 31601921)’, ‘Basic Research Programs of Sichuan Province (2016JY0147)’ and ‘Animal Science Discipline Program of Southwest University for Nationalities’ (2014XWD-S0905).
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Mammals
                Ruminants
                Goats
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Biochemistry
                Lipids
                Lipid Metabolism
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Lipid Metabolism
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Biology and Life Sciences
                Biochemistry
                Lipids
                Fatty Acids
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Gene Ontologies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Gene Ontologies
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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