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      Expansion of clinico-genetic spectrum of PRDX3 disease: a literature review with two additional cases

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          Cho et al. identified two Korean ataxia patients with novel variants, thereby broadening the clinico-genomic findings of PRDX3 disease. The novel variants (Asp171Gly and Arg207Ter) were found in compound heterozygotes with the previously reported variant (Arg170Ter). Identification of these pathogenic PRDX3 variants in East Asians highlights the need for increased awareness of PRDX3 disease.

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

          Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)
          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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            The mutational constraint spectrum quantified from variation in 141,456 humans

            Konrad J. Karczewski, Laurent C. Francioli, Grace Tiao … (2021)
            Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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              ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

              Kai Wang, Mingyao Li, Hakon Hakonarson (2010)
              High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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                Author and article information

                Contributors
                Jaeso Cho
                Jihoon G Yoon:
                ORCID: https://orcid.org/0000-0002-4401-7803
                Seungbok Lee
                Sheehyun Kim
                Soo Yeon Kim
                Man Jin Kim
                Jangsup Moon
                Jong-Hee Chae
                Journal
                Journal ID (nlm-ta): Brain Commun
                Journal ID (iso-abbrev): Brain Commun
                Journal ID (publisher-id): braincomms
                Title: Brain Communications
                Publisher: Oxford University Press (US )
                ISSN (Electronic): 2632-1297
                Publication date Collection: 2023
                Publication date (Electronic): 28 August 2023
                Publication date PMC-release: 28 August 2023
                Volume: 5
                Issue: 5
                Electronic Location Identifier: fcad233
                Affiliations
                Department of Genomic Medicine, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Genomic Medicine, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Genomic Medicine, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Genomic Medicine, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Genomic Medicine, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Pediatrics, Seoul National University College of Medicine , Seoul03080, Republic of Korea
                Department of Genomic Medicine, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Laboratory Medicine, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Genomic Medicine, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Neurology, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Genomic Medicine, Seoul National University Hospital , Seoul 03080, Republic of Korea
                Department of Pediatrics, Seoul National University College of Medicine , Seoul03080, Republic of Korea
                Author notes
                Correspondence to: Jangsup Moon, MD, PhD Department of Genomic Medicine and Department of Neurology Seoul National University Hospital 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea E-mail: jangsup.moon@ 123456gmail.com
                Correspondence may also be addressed to: Jong-Hee Chae, MD, PhD Department of Genomic Medicine and Department of Pediatrics Seoul National University Hospital, 101 Daehak-ro Jongno-gu, Seoul 03080, Republic of Korea E-mail: chaeped1@ 123456snu.ac.kr

                Jaeso Cho, Jihoon G Yoon, Jangsup Moon and Jong-Hee Chae contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-4401-7803
                Article
                Publisher ID: fcad233
                DOI: 10.1093/braincomms/fcad233
                PMC ID: 10507740
                PubMed ID: 37731903
                SO-VID: 537df0a7-a4ab-46a4-bc61-6835c3f8436b
                Copyright © © The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain.
                License:

                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
                Date received : 25 April 2023
                Date revision received : 19 June 2023
                Date accepted : 25 August 2023
                Page count
                Pages: 5
                Funding
                Funded by: Centers for Disease Control and Prevention, DOI 10.13039/100000030;
                Award ID: 2020-ER6904-01
                Award ID: 2021-ER0701-01
                Categories
                Subject: Letter to the Editor
                Subject: AcademicSubjects/MED00310
                Subject: AcademicSubjects/SCI01870

                Data availability:

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