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Using Galaxy to Perform Large-Scale Interactive Data Analyses-An Update.
Ostrovsky, Alexander; Hillman-Jackson, Jennifer; Bouvier, Dave; Clements, Dave; Afgan, Enis; Blankenberg, Daniel; Schatz, Michael C; Nekrutenko, Anton; Taylor, James; Team, The Galaxy; Lariviere, Delphine.
Afiliación
  • Ostrovsky A; Johns Hopkins University, Baltimore, Maryland.
  • Hillman-Jackson J; Penn State University, University Park, Pennsylvania.
  • Bouvier D; Penn State University, University Park, Pennsylvania.
  • Clements D; Johns Hopkins University, Baltimore, Maryland.
  • Afgan E; Johns Hopkins University, Baltimore, Maryland.
  • Blankenberg D; Cleveland Clinic, Lerner Research Institute, Cleveland, Ohio.
  • Schatz MC; Johns Hopkins University, Baltimore, Maryland.
  • Nekrutenko A; Penn State University, University Park, Pennsylvania.
  • Taylor J; Johns Hopkins University, Baltimore, Maryland.
  • Team TG; Johns Hopkins University, Baltimore, Maryland.
  • Lariviere D; Penn State University, University Park, Pennsylvania.
Curr Protoc ; 1(2): e31, 2021 Feb.
Article en En | MEDLINE | ID: mdl-33583104
Modern biology continues to become increasingly computational. Datasets are becoming progressively larger, more complex, and more abundant. The computational savviness necessary to analyze these data creates an ongoing obstacle for experimental biologists. Galaxy (galaxyproject.org) provides access to computational biology tools in a web-based interface. It also provides access to major public biological data repositories, allowing private data to be combined with public datasets. Galaxy is hosted on high-capacity servers worldwide and is accessible for free, with an option to be installed locally. This article demonstrates how to employ Galaxy to perform biologically relevant analyses on publicly available datasets. These protocols use both standard and custom tools, serving as a tutorial and jumping-off point for more intensive and/or more specific analyses using Galaxy. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Finding human coding exons with highest SNP density Basic Protocol 2: Calling peaks for ChIP-seq data Basic Protocol 3: Compare datasets using genomic coordinates Basic Protocol 4: Working with multiple alignments Basic Protocol 5: Single cell RNA-seq.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Análisis de Datos Límite: Humans Idioma: En Revista: Curr Protoc Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Análisis de Datos Límite: Humans Idioma: En Revista: Curr Protoc Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos