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      Hoyeraal-Hreidarsson syndrome caused by a germline mutation in the TEL patch of the telomere protein TPP1

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          Abstract

          Germline mutations in telomere biology genes cause dyskeratosis congenita (DC), an inherited bone marrow failure and cancer predisposition syndrome. Hoyeraal-Hreidarsson syndrome (HH) is a clinically severe variant of DC. Using exome sequencing, Kocak et al. identified mutations in ACD (encoding TPP1), a component of the telomeric shelterin complex, in one family affected by HH. Characterization of the mutations revealed that the single-amino-acid deletion affecting the TEL patch surface of the TPP1 protein significantly compromises both telomerase recruitment and processivity.

          Abstract

          Germline mutations in telomere biology genes cause dyskeratosis congenita (DC), an inherited bone marrow failure and cancer predisposition syndrome. DC is a clinically heterogeneous disorder diagnosed by the triad of dysplastic nails, abnormal skin pigmentation, and oral leukoplakia; Hoyeraal-Hreidarsson syndrome (HH), a clinically severe variant of DC, also includes cerebellar hypoplasia, immunodeficiency, and intrauterine growth retardation. Approximately 70% of DC cases are associated with a germline mutation in one of nine genes, the products of which are all involved in telomere biology. Using exome sequencing, we identified mutations in Adrenocortical Dysplasia Homolog ( ACD) (encoding TPP1), a component of the telomeric shelterin complex, in one family affected by HH. The proband inherited a deletion from his father and a missense mutation from his mother, resulting in extremely short telomeres and a severe clinical phenotype. Characterization of the mutations revealed that the single-amino-acid deletion affecting the TEL patch surface of the TPP1 protein significantly compromises both telomerase recruitment and processivity, while the missense mutation in the TIN2-binding region of TPP1 is not as clearly deleterious to TPP1 function. Our results emphasize the critical roles of the TEL patch in proper stem cell function and demonstrate that TPP1 is the second shelterin component (in addition to TIN2) to be implicated in DC.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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              A method and server for predicting damaging missense mutations

              To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
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                Author and article information

                Journal
                Genes Dev
                Genes Dev
                genesdev
                genesdev
                GAD
                Genes & Development
                Cold Spring Harbor Laboratory Press
                0890-9369
                1549-5477
                1 October 2014
                : 28
                : 19
                : 2090-2102
                Affiliations
                [1 ]Department of Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA;
                [2 ]Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI), Rockville, Maryland 20850, USA;
                [3 ]Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan 48109, USA;
                [4 ]Cancer Genomics Research Laboratory, Leidos Biomedical Research, NCI-Frederick, Rockville, Maryland 20850, USA;
                [5 ]Life Sciences Institute, University of Michigan, Ann Arbor, Michigan 48109, USA;
                [6 ]Division of Hematology-Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan 48109, USA;
                [7 ]Department of Cell and Developmental Biology, Ann Arbor, Michigan 48109, USA;
                [8 ]Department of Pediatrics, University of Michigan, Ann Arbor, Michigan 48109, USA
                Author notes
                [9]

                The NCI DCEG Cancer Genomics Research Laboratory is Sara Bass, Joseph Boland, Laurie Burdett, Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A. Hutchinson, Kristine Jones, Sally Larson, Kerrie Lashley, Hyo Jung Lee, Wen Luo, Michael Malasky, Michelle Manning, Jason Mitchell, David Roberson, Aurelie Vogt, Mingyi Wang, Meredith Yeager, and Xijun Zhang.

                [10]

                The NCI DCEG Cancer Sequencing Working Group is Neil E. Caporaso, Stephen J. Chanock, Mark H. Greene, Lynn R. Goldin, Alisa M. Goldstein, Allan Hildesheim, Nan Hu, Maria Teresa Landi, Jennifer Loud, Phuong L. Mai, Mary L. McMaster, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Melissa Rotunno, Douglas R. Stewart, Phil Taylor, Geoffrey S. Tobias, Margaret A. Tucker, Jeannette Wong, Xiaohong R. Yang, and Guoqin Yu.

                [11]

                These authors contributed equally to this work.

                Article
                8711660
                10.1101/gad.248567.114
                4180972
                25233904
                c2ef6064-ea09-47ed-a9b3-77133d6b4c25
                © 2014 Kocak et al.; Published by Cold Spring Harbor Laboratory Press

                This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genesdev.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 4 July 2014
                : 29 August 2014
                Page count
                Pages: 13
                Categories
                Research Paper

                dyskeratosis congenita,hoyeraal-hreidarsson syndrome,telomere,tpp1,acd,telomerase

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