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      Chromosome-level reference genome for North American bison ( Bison bison) and variant database aids in identifying albino mutation

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          Abstract

          We developed a highly contiguous chromosome-level reference genome for North American bison to provide a platform to evaluate the conservation, ecological, evolutionary, and population genomics of this species. Generated from a F1 hybrid between a North American bison dam and a domestic cattle bull, completeness and contiguity exceed that of other published bison genome assemblies. To demonstrate the utility for genome-wide variant frequency estimation, we compiled a genomic variant database consisting of 3 true albino bison and 44 wild-type pelage color bison. Through the examination of genomic variants fixed in the albino cohort and absent in the controls, we identified a nonsynonymous single nucleotide polymorphism (SNP) mutation on chromosome 29 in exon 3 of the tyrosinase gene (c.1114C>T). A TaqMan SNP Genotyping Assay was developed to genotype this SNP in a total of 283 animals across 29 herds. This assay confirmed the absence of homozygous variants in all animals except 7 true albino bison included in this study. In addition, the only heterozygous animals identified were 2 wild-type pelage color dams of albino offspring. Therefore, we propose that this new high-quality bison genome assembly and incipient variant database provides a highly robust and informative resource for genomics investigations for this iconic North American species.

          Most cited references60

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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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            Highly accurate protein structure prediction with AlphaFold

            Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                2160-1836
                October 2023
                22 July 2023
                22 July 2023
                : 13
                : 10
                : jkad156
                Affiliations
                Department of Veterinary Pathobiology, Texas A&M University School of Veterinary Medicine and Biomedical Science , College Station, TX 77843, USA
                Department of Veterinary Pathobiology, Texas A&M University School of Veterinary Medicine and Biomedical Science , College Station, TX 77843, USA
                National Park Service, Theodore Roosevelt National Park , Medora, ND 58645, USA
                Department of Veterinary Integrative Biosciences, Texas A&M University School of Veterinary Medicine and Biomedical Science , College Station, TX 77843, USA
                Department of Veterinary Integrative Biosciences, Texas A&M University School of Veterinary Medicine and Biomedical Science , College Station, TX 77843, USA
                Department of Veterinary Integrative Biosciences, Texas A&M University School of Veterinary Medicine and Biomedical Science , College Station, TX 77843, USA
                Texas Parks and Wildlife, Caprock Canyons State Park & Trailway , Quitaque, TX 79255, USA
                Department of Veterinary Integrative Biosciences, Texas A&M University School of Veterinary Medicine and Biomedical Science , College Station, TX 77843, USA
                Department of Small Animal Clinical Sciences, Texas A&M University School of Veterinary Medicine and Biomedical Science , College Station, TX 77843, USA
                Department of Veterinary Pathobiology, Texas A&M University School of Veterinary Medicine and Biomedical Science , College Station, TX 77843, USA
                Author notes
                Corresponding author: Department of Veterinary Pathobiology, Texas A&M University School of Veterinary Medicine and Biomedical Science, College Station, TX 77843, USA. Email: j-derr@ 123456tamu.edu (JND);
                Corresponding author: Department of Veterinary Integrative Biosciences, Texas A&M University School of Veterinary Medicine and Biomedical Science, College Station, TX 77843, USA; Department of Small Animal Clinical Sciences, Texas A&M University School of Veterinary Medicine and Biomedical Science, College Station, TX 77843, USA. Email: bdavis@ 123456cvm.tamu.edu (BWD)

                Conflicts of interest statement The author(s) declare no conflict of interest.

                Article
                jkad156
                10.1093/g3journal/jkad156
                10542314
                37481261
                af6adbf0-3407-462b-83f0-45693c9448b1
                © The Author(s) 2023. Published by Oxford University Press on behalf of The 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.

                History
                : 07 April 2023
                : 07 July 2023
                : 21 August 2023
                Page count
                Pages: 9
                Funding
                Funded by: National Park Service, DOI 10.13039/100007516;
                Funded by: Throlson American Bison Foundation Scholarship;
                Funded by: The Houston Safari Club;
                Funded by: College of Veterinary Medicine and Biomedical Sciences, DOI 10.13039/100013674;
                Funded by: Texas A&M University, DOI 10.13039/100007904;
                Funded by: Boehringer Ingelheim Veterinary Scholars Program;
                Funded by: Texas A&M College of Veterinary Medicine and Biomedical Sciences;
                Categories
                Genome Report
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140

                Genetics
                north american bison,genomics,albinism,tyrosinase gene,bison reference genome,genome variant database

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