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      VariantscanR: an R-package as a clinical tool for variant filtering of known phenotype-associated variants in domestic animals

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          Abstract

          Background

          Since the introduction of next-generation sequencing (NGS) techniques, whole-exome sequencing (WES) and whole-genome sequencing (WGS) have not only revolutionized research, but also diagnostics. The gradual switch from single gene testing to WES and WGS required a different set of skills, given the amount and type of data generated, while the demand for standardization remained. However, most of the tools currently available are solely applicable for human analysis because they require access to specific databases and/or simply do not support other species. Additionally, a complicating factor in clinical genetics in animals is that genetic diversity is often dangerously low due to the breeding history. Combined, there is a clear need for an easy-to-use, flexible tool that allows standardized data processing and preferably, monitoring of genetic diversity as well. To fill these gaps, we developed the R-package variantscanR that allows an easy and straightforward identification and prioritization of known phenotype-associated variants identified in dogs and other domestic animals.

          Results

          The R-package variantscanR enables the filtering of variant call format (VCF) files for the presence of known phenotype-associated variants and allows for the estimation of genetic diversity using multi-sample VCF files. Next to this, additional functions are available for the quality control and processing of user-defined input files to make the workflow as easy and straightforward as possible. This user-friendly approach enables the standardisation of complex data analysis in clinical settings.

          Conclusion

          We developed an R-package for the identification of known phenotype-associated variants and calculation of genetic diversity.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12859-023-05426-6.

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          Most cited references33

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          The variant call format and VCFtools

          Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. Availability: http://vcftools.sourceforge.net Contact: rd@sanger.ac.uk
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            Oncotator: cancer variant annotation tool.

            Oncotator is a tool for annotating genomic point mutations and short nucleotide insertions/deletions (indels) with variant- and gene-centric information relevant to cancer researchers. This information is drawn from 14 different publicly available resources that have been pooled and indexed, and we provide an extensible framework to add additional data sources. Annotations linked to variants range from basic information, such as gene names and functional classification (e.g. missense), to cancer-specific data from resources such as the Catalogue of Somatic Mutations in Cancer (COSMIC), the Cancer Gene Census, and The Cancer Genome Atlas (TCGA). For local use, Oncotator is freely available as a python module hosted on Github (https://github.com/broadinstitute/oncotator). Furthermore, Oncotator is also available as a web service and web application at http://www.broadinstitute.org/oncotator/.
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              Clinical whole-exome sequencing for the diagnosis of mendelian disorders.

              Whole-exome sequencing is a diagnostic approach for the identification of molecular defects in patients with suspected genetic disorders. We developed technical, bioinformatic, interpretive, and validation pipelines for whole-exome sequencing in a certified clinical laboratory to identify sequence variants underlying disease phenotypes in patients. We present data on the first 250 probands for whom referring physicians ordered whole-exome sequencing. Patients presented with a range of phenotypes suggesting potential genetic causes. Approximately 80% were children with neurologic phenotypes. Insurance coverage was similar to that for established genetic tests. We identified 86 mutated alleles that were highly likely to be causative in 62 of the 250 patients, achieving a 25% molecular diagnostic rate (95% confidence interval, 20 to 31). Among the 62 patients, 33 had autosomal dominant disease, 16 had autosomal recessive disease, and 9 had X-linked disease. A total of 4 probands received two nonoverlapping molecular diagnoses, which potentially challenged the clinical diagnosis that had been made on the basis of history and physical examination. A total of 83% of the autosomal dominant mutant alleles and 40% of the X-linked mutant alleles occurred de novo. Recurrent clinical phenotypes occurred in patients with mutations that were highly likely to be causative in the same genes and in different genes responsible for genetically heterogeneous disorders. Whole-exome sequencing identified the underlying genetic defect in 25% of consecutive patients referred for evaluation of a possible genetic condition. (Funded by the National Human Genome Research Institute.).
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                Author and article information

                Contributors
                frederique.boeykens@ugent.be
                bart.broeckx@ugent.be
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                1 August 2023
                1 August 2023
                2023
                : 24
                : 305
                Affiliations
                [1 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Laboratory of Animal Genetics, Department of Veterinary and Biosciences, Faculty of Veterinary Medicine, , Ghent University, ; Heidestraat 19, 9820 Merelbeke, Belgium
                [2 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Small Animal Department, Faculty of Veterinary Medicine, , Ghent University, ; Salisburylaan 133, 9820 Merelbeke, Belgium
                Article
                5426
                10.1186/s12859-023-05426-6
                10394849
                37528412
                bba851cb-0ab7-4049-8252-ea9db00da328
                © The Author(s) 2023

                Open Access This 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
                : 22 February 2023
                : 20 July 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100007229, Bijzonder Onderzoeksfonds UGent;
                Award ID: 01N04119
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003130, Fonds Wetenschappelijk Onderzoek;
                Award ID: K1B3222N
                Award ID: K1B2222N
                Award Recipient :
                Categories
                Software
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Bioinformatics & Computational biology
                sequencing,variant filtering,precision medicine,clinical tool,wes,wgs

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