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      Investigation of ancestral alleles in the Bovinae subfamily

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          Abstract

          Background

          In evolutionary theory, divergence and speciation can arise from long periods of reproductive isolation, genetic mutation, selection and environmental adaptation. After divergence, alleles can either persist in their initial state (ancestral allele - AA), co-exist or be replaced by a mutated state (derived alleles -DA). In this study, we aligned whole genome sequences of individuals from the Bovinae subfamily to the cattle reference genome (ARS.UCD-1.2) for defining ancestral alleles necessary for selection signatures study.

          Results

          Accommodating independent divergent of each lineage from the initial ancestral state, AA were defined based on fixed alleles on at least two groups of yak, bison and gayal-gaur-banteng resulting in ~ 32.4 million variants. Using non-overlapping scanning windows of 10 Kb, we counted the AA observed within taurine and zebu cattle. We focused on the extreme points, regions with top 0. 1% (high count) and regions without any occurrence of AA (null count). High count regions preserved gene functions from ancestral states that are still beneficial in the current condition, while null counts regions were linked to mutated ones. For both cattle, high count regions were associated with basal lipid metabolism, essential for survival of various environmental pressures. Mutated regions were associated to productive traits in taurine, i.e. higher metabolism, cell development and behaviors and in immune response domain for zebu.

          Conclusions

          Our findings suggest that retaining and losing AA in some regions are varied and made it species-specific with possibility of overlapping as it depends on the selective pressure they had to experience.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-021-07412-9.

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          Most cited references66

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

            The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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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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                Author and article information

                Contributors
                agis.maulana12@gmail.com
                ytutsunomiya@gmail.com
                johann.soelkner@boku.ac.at
                ben.rosen@usda.gov
                gabor.meszaros@boku.ac.at
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                8 February 2021
                8 February 2021
                2021
                : 22
                : 108
                Affiliations
                [1 ]GRID grid.5173.0, ISNI 0000 0001 2298 5320, University of Natural Resources and Life Sciences (BOKU), ; Vienna, Austria
                [2 ]GRID grid.410543.7, ISNI 0000 0001 2188 478X, São Paulo State University (Unesp), School of Veterinary Medicine, Department of Production and Animal Health, ; Araçatuba, São Paulo Brazil
                [3 ]International Atomic Energy Agency (IAEA) Collaborating Centre on Animal Genomics and Bioinformatics, Araçatuba, São Paulo Brazil
                [4 ]AgroPartners Consulting. R. Floriano Peixoto, 120-Sala 43A-Centro, Araçatuba, SP 16010-220 Brazil
                [5 ]Personal-PEC. R. Sebastiao Lima, 1336-Centro, Campo Grande, MS 79004-600 Brazil
                [6 ]GRID grid.463419.d, ISNI 0000 0001 0946 3608, Agricultural Research Service USDA, ; Beltsville, MD USA
                Author information
                https://orcid.org/0000-0002-3264-2708
                https://orcid.org/0000-0002-6526-8337
                https://orcid.org/0000-0002-1517-5829
                http://orcid.org/0000-0001-9395-8346
                https://orcid.org/0000-0002-5937-0060
                Article
                7412
                10.1186/s12864-021-07412-9
                7871596
                33557747
                2298c3ea-5857-4ea5-9598-7088ec43553e
                © The Author(s) 2021

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

                History
                : 25 September 2020
                : 27 January 2021
                Funding
                Funded by: Ernst Mach Grant – ASEA UNINET (Austria)
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Genetics
                ancestral allele,bovinae,gene ontology,whole genome sequences
                Genetics
                ancestral allele, bovinae, gene ontology, whole genome sequences

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