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      Degree of foot process effacement in patients with genetic focal segmental glomerulosclerosis: a single-center analysis and review of the literature

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          Abstract

          Determining the cause of focal segmental glomerulosclerosis (FSGS) has crucial implications for evaluating the risk of posttransplant recurrence. The degree of foot process effacement (FPE) on electron micrographs (EM) of native kidney biopsies can reportedly differentiate primary FSGS from secondary FSGS. However, no systematic evaluation of FPE in genetic FSGS has been performed. In this study, percentage of FPE and foot process width (FPW) in native kidney biopsies were analyzed in eight genetic FSGS patients and nine primary FSGS patients. All genetic FSGS patients showed segmental FPE up to 38% and FPW below 2000 nm, while all primary FSGS patients showed diffuse FPE above 88% and FPW above 3000 nm. We reviewed the literature which described the degree of FPE in genetic FSGS patients and identified 38 patients with a description of the degree of FPE. The degree of FPE in patients with mutations in the genes encoding proteins associated with slit diaphragm and cytoskeletal proteins was varied, while almost all patients with mutations in other FSGS genes showed segmental FPE. In conclusion, the present study suggests that the degree of FPE in native kidney biopsies may be useful for differentiating some genetic FSGS patients from primary FSGS patients.

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          Most cited references54

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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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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              Analysis of protein-coding genetic variation in 60,706 humans

              Summary Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes.
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                Author and article information

                Contributors
                hattori@twmu.ac.jp
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 June 2021
                8 June 2021
                2021
                : 11
                : 12008
                Affiliations
                [1 ]GRID grid.410818.4, ISNI 0000 0001 0720 6587, Department of Pediatric Nephrology, School of Medicine, , Tokyo Women’s Medical University, ; 8-1 Kawada-cho, Shinjuku-ku, Tokyo, 162-8666 Japan
                [2 ]GRID grid.268394.2, ISNI 0000 0001 0674 7277, Department of Pediatrics, , Yamagata University School of Medicine, ; Yamagata, Japan
                [3 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Department of Pediatrics, Graduate School of Medicine, , The University of Tokyo, ; Tokyo, Japan
                [4 ]GRID grid.411321.4, ISNI 0000 0004 0632 2959, Department of Nephrology, , Chiba Children’s Hospital, ; Chiba, Japan
                [5 ]GRID grid.416697.b, ISNI 0000 0004 0569 8102, Division of Nephrology, , Saitama Children’s Medical Center, ; 1-2 Shintoshin, Chuo-ku, Saitama city, Saitama, 330-8777 Japan
                [6 ]GRID grid.414532.5, ISNI 0000 0004 1764 8129, Department of Pediatrics, , Tokyo Metropolitan Bokutoh Hospital, ; Tokyo, Japan
                [7 ]Yamaguchi’s Pathology Laboratory, Chiba, Japan
                Article
                91520
                10.1038/s41598-021-91520-9
                8187362
                34103591
                40ddb8c5-035f-474b-b128-81e56a4574bd
                © 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
                : 1 February 2021
                : 25 May 2021
                Funding
                Funded by: Grant-in-Aid for Scientific Research (KAKENHI)
                Award ID: JP18K07029
                Award ID: JP18K07830
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                kidney diseases,genetics,diseases,nephrology
                Uncategorized
                kidney diseases, genetics, diseases, nephrology

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