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      A novel TBK1 mutation in a family with diverse frontotemporal dementia spectrum disorders

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          Abstract

          Mutations in the TANK-binding kinase 1 ( TBK1) gene have recently been shown to cause frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). The phenotype is highly variable and has been associated with behavioral variant FTD, primary progressive aphasia, and pure ALS. We describe the clinical, anatomical, and pathological features of a patient who developed corticobasal syndrome (CBS)/progressive nonfluent aphasia (PNFA) overlap. The patient presented with progressive speech difficulties and later developed an asymmetric akinetic–rigid syndrome. Neuroimaging showed asymmetrical frontal atrophy, predominantly affecting the right side. There was a strong family history of neurodegenerative disease with four out of seven siblings developing either dementia or ALS in their 50s and 60s. The patient died at the age of 71 and the brain was donated for postmortem analysis. Histopathological examination showed frontotemporal lobar degeneration TDP-43 type A pathology. Genetic screening did not reveal a mutation in the GRN, MAPT, or C9orf72 genes, but exome sequencing revealed a novel p.E703X mutation in the TBK1 gene. Although segregation data were not available, this loss-of-function mutation is highly likely to be pathogenic because it is predicted to disrupt TBK1/optineurin interaction and impair cellular autophagy. In conclusion, we show that TBK1 mutations can be a cause of an atypical parkinsonian syndrome and screening should be considered in CBS patients with a family history of dementia or ALS.

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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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              ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

              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

                Journal
                Cold Spring Harb Mol Case Stud
                Cold Spring Harb Mol Case Stud
                cshmcs
                cshmcs
                cshmcs
                Cold Spring Harbor Molecular Case Studies
                Cold Spring Harbor Laboratory Press
                2373-2873
                June 2019
                : 5
                : 3
                : a003913
                Affiliations
                [1 ]Department of Clinical and Movement Neurosciences, Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, United Kingdom;
                [2 ]Department of Neurodegenerative Disease, Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, United Kingdom;
                [3 ]Ken and Ruth Davee Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA;
                [4 ]Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff CF24 4HQ, United Kingdom;
                [5 ]Department of Neurology, Morriston Hospital, Swansea SA6 6NL, United Kingdom;
                [6 ]Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London WC1N 1PJ, United Kingdom
                Author notes
                Corresponding author: h.morris@ 123456ucl.ac.uk
                Author information
                http://orcid.org/0000-0001-8117-742X
                Article
                MCS003913Lam
                10.1101/mcs.a003913
                6549548
                31160356
                cfce70c1-f632-4761-a05a-93fa464a59d5
                © 2019 Lamb et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits reuse and redistribution, except for commercial purposes, provided that the original author and source are credited.

                History
                : 20 December 2018
                : 20 March 2019
                Page count
                Pages: 10
                Funding
                Funded by: National Institute for Health Research , open-funder-registry 10.13039/501100000272;
                Funded by: NIHR , open-funder-registry 10.13039/100006662;
                Funded by: Queen Square Biomedical Research Unit in Dementia
                Funded by: University College London Hospitals UCLH , open-funder-registry 10.13039/501100008721;
                Funded by: University College London , open-funder-registry 10.13039/501100000765;
                Funded by: Alzheimer's Research UK
                Funded by: Brain Research Trust
                Funded by: Wolfson Foundation
                Funded by: NIHR Queen Square Dementia Biomedical Research Unit
                Funded by: NIHR UCL/H Biomedical Research Centre
                Funded by: Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility
                Funded by: MRC Clinician Scientist Fellowship
                Award ID: MR/M008525/1
                Funded by: NIHR Rare Disease Translational Research Collaboration
                Award ID: BRC149/NS/MH
                Funded by: BRACE , open-funder-registry 10.13039/100011699;
                Funded by: Alzheimer's Research UK senior fellowship
                Funded by: Karin & Sten Mortstedt CBD Solutions
                Funded by: Multiple System Atrophy Trust , open-funder-registry 10.13039/100013128;
                Funded by: Multiple System Atrophy Coalition
                Funded by: Fund Sophia
                Funded by: King Baudouin Foundation , open-funder-registry 10.13039/501100006282;
                Funded by: Alzheimer's Research UK
                Funded by: CBD Solutions
                Funded by: Reta Lila Weston Institute of Neurological Studies
                Funded by: Medical Research Council UK
                Categories
                Research Report

                progressive extrapyramidal movement disorder

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