Using INFERNO to Infer the Molecular Mechanisms Underlying Noncoding Genetic Associations.

Amlie-Wolf, Alexandre; Kuksa, Pavel P; Lee, Chien-Yueh; Mlynarski, Elisabeth; Leung, Yuk Yee; Wang, Li-San

Amlie-Wolf, Alexandre; Kuksa, Pavel P; Lee, Chien-Yueh; Mlynarski, Elisabeth; Leung, Yuk Yee; Wang, Li-San.

Afiliação

Amlie-Wolf A; Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Kuksa PP; Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Lee CY; Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Mlynarski E; Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Leung YY; Penn Neurodegeneration Genomics Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Wang LS; Department of Pathology & Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Methods Mol Biol ; 2254: 73-91, 2021.

Article em En | MEDLINE | ID: mdl-33326071

ABSTRACT

ABSTRACT

The INFERNO method provides an integrative computational framework for characterizing the causal variants, tissue contexts, affected regulatory mechanisms, and target genes underlying noncoding genetic variants associated with any phenotype or disease of interest. Here we describe the computational steps required to run the full INFERNO pipeline on any dataset of interest.

Assuntos

Predisposição Genética para Doença; Estudo de Associação Genômica Ampla/métodos; RNA Longo não Codificante/genética; Software; Humanos; Anotação de Sequência Molecular

Palavras-chave

Bioinformatics; Computational biology; Enhancers; Genomics; INFERNO; Noncoding genetics; Regulatory variation; eQTL; lncRNA

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Predisposição Genética para Doença / Estudo de Associação Genômica Ampla / RNA Longo não Codificante Tipo de estudo: Risk_factors_studies Limite: Humans Idioma: En Revista: Methods Mol Biol Assunto da revista: BIOLOGIA MOLECULAR Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

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