Your browser doesn't support javascript.
loading
genomeSidekick: A user-friendly epigenomics data analysis tool.
Chen, Junjie; Zhu, Ashley J; Packard, René R S; Vondriska, Thomas M; Chapski, Douglas J.
Afiliación
  • Chen J; Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
  • Zhu AJ; Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
  • Packard RRS; Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
  • Vondriska TM; Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.
  • Chapski DJ; Ronald Reagan UCLA Medical Center, Los Angeles, CA, United States.
Front Bioinform ; 2: 831025, 2022.
Article en En | MEDLINE | ID: mdl-36304311
Recent advances in epigenomics measurements have resulted in a preponderance of genomic sequencing datasets that require focused analyses to discover mechanisms governing biological processes. In addition, multiple epigenomics experiments are typically performed within the same study, thereby increasing the complexity and difficulty of making meaningful inferences from large datasets. One gap in the sequencing data analysis pipeline is the availability of tools to efficiently browse genomic data for scientists that do not have bioinformatics training. To bridge this gap, we developed genomeSidekick, a graphical user interface written in R that allows researchers to perform bespoke analyses on their transcriptomic and chromatin accessibility or chromatin immunoprecipitation data without the need for command line tools. Importantly, genomeSidekick outputs lists of up- and downregulated genes or chromatin features with differential accessibility or occupancy; visualizes omics data using interactive volcano plots; performs Gene Ontology analyses locally; and queries PubMed for selected gene candidates for further evaluation. Outputs can be saved using the user interface and the code underlying genomeSidekick can be edited for custom analyses. In summary, genomeSidekick brings wet lab scientists and bioinformaticians into a shared fluency with the end goal of driving mechanistic discovery.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Bioinform Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Bioinform Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza