Literature DB >> 24510651

Genome-scale sequencing to identify genes involved in Mendelian disorders.

Thomas C Markello1, David R Adams1.   

Abstract

The analysis of genome-scale sequence data can be defined as the interrogation of a complete set of genetic instructions in a search for individual loci that produce or contribute to a pathological state. Bioinformatic analysis of sequence data requires sufficient discriminant power to find this needle in a haystack. Current approaches make choices about selectivity and specificity thresholds, and the quality, quantity, and completeness of the data in these analyses. There are many software tools available for individual, analytic component-tasks, including commercial and open-source options. Three major types of techniques have been included in most published exome projects to date: frequency/population genetic analysis, inheritance state consistency, and predictions of deleteriousness. The required infrastructure and use of each technique during analysis of genomic sequence data for clinical and research applications are discussed. Future developments will alter the strategies and sequence of using these tools and are also discussed.
Copyright © 2013 John Wiley & Sons, Inc.

Entities:  

Keywords:  Mendelian inheritance; bioinformatics; clinical sequencing; exome; next generation sequencing

Mesh:

Year:  2013        PMID: 24510651      PMCID: PMC3959778          DOI: 10.1002/0471142905.hg0613s79

Source DB:  PubMed          Journal:  Curr Protoc Hum Genet        ISSN: 1934-8258


  30 in total

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Journal:  Annu Rev Genomics Hum Genet       Date:  2006       Impact factor: 8.929

6.  Distribution and intensity of constraint in mammalian genomic sequence.

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10.  Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells.

Authors:  Brock A Peters; Bahram G Kermani; Andrew B Sparks; Oleg Alferov; Peter Hong; Andrei Alexeev; Yuan Jiang; Fredrik Dahl; Y Tom Tang; Juergen Haas; Kimberly Robasky; Alexander Wait Zaranek; Je-Hyuk Lee; Madeleine Price Ball; Joseph E Peterson; Helena Perazich; George Yeung; Jia Liu; Linsu Chen; Michael I Kennemer; Kaliprasad Pothuraju; Karel Konvicka; Mike Tsoupko-Sitnikov; Krishna P Pant; Jessica C Ebert; Geoffrey B Nilsen; Jonathan Baccash; Aaron L Halpern; George M Church; Radoje Drmanac
Journal:  Nature       Date:  2012-07-11       Impact factor: 49.962

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3.  Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency.

Authors:  William P Bone; Nicole L Washington; Orion J Buske; David R Adams; Joie Davis; David Draper; Elise D Flynn; Marta Girdea; Rena Godfrey; Gretchen Golas; Catherine Groden; Julius Jacobsen; Sebastian Köhler; Elizabeth M J Lee; Amanda E Links; Thomas C Markello; Christopher J Mungall; Michele Nehrebecky; Peter N Robinson; Murat Sincan; Ariane G Soldatos; Cynthia J Tifft; Camilo Toro; Heather Trang; Elise Valkanas; Nicole Vasilevsky; Colleen Wahl; Lynne A Wolfe; Cornelius F Boerkoel; Michael Brudno; Melissa A Haendel; William A Gahl; Damian Smedley
Journal:  Genet Med       Date:  2015-11-12       Impact factor: 8.822

  3 in total

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