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      Environment-driven reprogramming of gamete DNA methylation occurs during maturation and is transmitted intergenerationally in Atlantic Salmon


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          An epigenetic basis for transgenerational plasticity in animals is widely theorized, but convincing empirical support is limited by taxa-specific differences in the presence and role of epigenetic mechanisms. In teleost fishes, DNA methylation generally does not undergo extensive reprogramming and has been linked with environmentally induced intergenerational effects, but solely in the context of early life environmental differences. Using whole-genome bisulfite sequencing, we demonstrate that differential methylation of sperm occurs in response to captivity during the maturation of Atlantic Salmon ( Salmo salar), a species of major economic and conservation significance. We show that adult captive exposure further induces differential methylation in an F1 generation that is associated with fitness-related phenotypic differences. Some genes targeted with differential methylation were consistent with genes differential methylated in other salmonid fishes experiencing early-life hatchery rearing, as well as genes under selection in domesticated species. Our results support a mechanism of transgenerational plasticity mediated by intergenerational inheritance of DNA methylation acquired late in life for salmon. To our knowledge, this is the first-time environmental variation experienced later in life has been directly demonstrated to influence gamete DNA methylation in fish.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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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              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press
                December 2021
                07 October 2021
                07 October 2021
                : 11
                : 12
                : jkab353
                [1 ] Institut de Biologie Intégrative et des Systèmes, Université Laval , Québec, QC G1V 0A6, Canada
                [2 ] Department of Biology, University of New Brunswick , Fredericton, NB E3B 5A3, Canada
                [3 ] Canadian Rivers Institute, University of New Brunswick , Fredericton, NB E3B 5A3, Canada
                [4 ] Faculty of Forestry and Environmental Management, University of New Brunswick , Fredericton, NB E3B 5A3, Canada
                Author notes
                Corresponding author: Pacific Science Enterprise Centre, Fisheries and Oceans Canada, 4160 Marine Dr. West, Vancouver, BC V7V 1N6, Canada. Email: kyle.wellband@ 123456gmail.com
                Author information
                © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 26 August 2021
                : 24 September 2021
                : 04 December 2021
                Page count
                Pages: 13
                Funded by: Collaboration for Atlantic Salmon Tomorrow Inc;
                Funded by: Cooke Aquaculture Inc.;
                Funded by: J.D. Irving Ltd.;
                Funded by: Atlantic Canada Opportunities Agency, DOI 10.13039/501100004952;
                Funded by: MITACS, DOI 10.13039/501100004489;
                Funded by: NSERC Postdoctoral Fellowship;

                dna methylation,epigenetic inheritance,transgenerational plasticity,salmon,hatchery,domestication


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