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MIPgen: optimized modeling and design of molecular inversion probes for targeted resequencing.
Boyle, Evan A; O'Roak, Brian J; Martin, Beth K; Kumar, Akash; Shendure, Jay.
Afiliação
  • Boyle EA; Department of Genome Sciences, University of Washington, Seattle, WA 98105 and Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA.
  • O'Roak BJ; Department of Genome Sciences, University of Washington, Seattle, WA 98105 and Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA.
  • Martin BK; Department of Genome Sciences, University of Washington, Seattle, WA 98105 and Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA.
  • Kumar A; Department of Genome Sciences, University of Washington, Seattle, WA 98105 and Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA.
  • Shendure J; Department of Genome Sciences, University of Washington, Seattle, WA 98105 and Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR 97239, USA.
Bioinformatics ; 30(18): 2670-2, 2014 Sep 15.
Article em En | MEDLINE | ID: mdl-24867941
UNLABELLED: Molecular inversion probes (MIPs) enable cost-effective multiplex targeted gene resequencing in large cohorts. However, the design of individual MIPs is a critical parameter governing the performance of this technology with respect to capture uniformity and specificity. MIPgen is a user-friendly package that simplifies the process of designing custom MIP assays to arbitrary targets. New logistic and SVM-derived models enable in silico predictions of assay success, and assay redesign exhibits improved coverage uniformity relative to previous methods, which in turn improves the utility of MIPs for cost-effective targeted sequencing for candidate gene validation and for diagnostic sequencing in a clinical setting. AVAILABILITY AND IMPLEMENTATION: MIPgen is implemented in C++. Source code and accompanying Python scripts are available at http://shendurelab.github.io/MIPGEN/.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Sondas de DNA / Modelos Estatísticos / Análise de Sequência / Biologia Computacional Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2014 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Sondas de DNA / Modelos Estatísticos / Análise de Sequência / Biologia Computacional Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2014 Tipo de documento: Article