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      Whole-exome sequencing reveals a novel homozygous mutation in the COQ8B gene associated with nephrotic syndrome

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          Abstract

          Nephrotic syndrome arising from monogenic mutations differs substantially from acquired ones in their clinical prognosis, progression, and disease management. Several pathogenic mutations in the COQ8B gene are known to cause nephrotic syndrome. Here, we used the whole-exome sequencing (WES) technology to decipher the genetic cause of nephrotic syndrome (CKD stage-V) in a large affected consanguineous family. Our study exposed a novel missense homozygous mutation NC_000019.9:g.41209497C > T; NM_024876.4:c.748G > A; NP_079152.3:p.(Asp250Asn) in the 9th exon of the COQ8B gene, co-segregated well with the disease phenotype. Our study provides the first insight into this homozygous condition, which has not been previously reported in 1000Genome, ClinVar, ExAC, and genomAD databases. In addition to the pathogenic COQ8B variant, the WES data also revealed some novel and recurrent mutations in the GLA, NUP107, COQ2, COQ6, COQ7 and COQ9 genes. The novel variants observed in this study have been submitted to the ClinVar database and are publicly available online with the accessions: SCV001451361.1, SCV001451725.1 and SCV001451724.1. Based on the patient's clinical history and genomic data with in silico validation, we conclude that pathogenic mutation in the COQ8B gene was causing kidney failure in an autosomal recessive manner. We recommend WES technology for genetic testing in such a consanguineous family to not only prevent the future generation, but early detection can help in disease management and therapeutic interventions.

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          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            A framework for variation discovery and genotyping using next-generation DNA sequencing data

            Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
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              The Human Genome Browser at UCSC

              As vertebrate genome sequences near completion and research refocuses to their analysis, the issue of effective genome annotation display becomes critical. A mature web tool for rapid and reliable display of any requested portion of the genome at any scale, together with several dozen aligned annotation tracks, is provided at http://genome.ucsc.edu. This browser displays assembly contigs and gaps, mRNA and expressed sequence tag alignments, multiple gene predictions, cross-species homologies, single nucleotide polymorphisms, sequence-tagged sites, radiation hybrid data, transposon repeats, and more as a stack of coregistered tracks. Text and sequence-based searches provide quick and precise access to any region of specific interest. Secondary links from individual features lead to sequence details and supplementary off-site databases. One-half of the annotation tracks are computed at the University of California, Santa Cruz from publicly available sequence data; collaborators worldwide provide the rest. Users can stably add their own custom tracks to the browser for educational or research purposes. The conceptual and technical framework of the browser, its underlying MYSQL database, and overall use are described. The web site currently serves over 50,000 pages per day to over 3000 different users.
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                Author and article information

                Contributors
                mohdfareedk@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 June 2021
                25 June 2021
                2021
                : 11
                : 13337
                Affiliations
                [1 ]GRID grid.418225.8, ISNI 0000 0004 1802 6428, PK-PD Formulation and Toxicology Division, , CSIR Indian Institute of Integrative Medicine, ; Canal Road, Jammu, 180001 India
                [2 ]GRID grid.469887.c, Academy of Scientific & Innovative Research (AcSIR), ; Ghaziabad, Uttar Pradesh 201002 India
                [3 ]GRID grid.413495.e, ISNI 0000 0004 1767 3121, Department of Nephrology, , Dayanand Medical College and Hospital, ; Ludhiana, Punjab 141001 India
                [4 ]GRID grid.413495.e, ISNI 0000 0004 1767 3121, Visiting Consultant Renal Transplant, Dayanand Medical College and Hospital, ; Ludhiana, Punjab 141001 India
                [5 ]GRID grid.411340.3, ISNI 0000 0004 1937 0765, Human Genetics & Toxicology Laboratory, Section of Genetics, Department of Zoology, , Aligarh Muslim University, ; Aligarh, Uttar Pradesh 202002 India
                Article
                92023
                10.1038/s41598-021-92023-3
                8233304
                34172776
                73eecbab-f9c5-4f67-9bf6-c6b5fcda7c9f
                © The Author(s) 2021

                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/.

                History
                : 2 February 2021
                : 18 May 2021
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                Article
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                © The Author(s) 2021

                Uncategorized
                consanguinity,kidney,kidney diseases
                Uncategorized
                consanguinity, kidney, kidney diseases

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