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EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets.
Cantrell, Kalen; Fedarko, Marcus W; Rahman, Gibraan; McDonald, Daniel; Yang, Yimeng; Zaw, Thant; Gonzalez, Antonio; Janssen, Stefan; Estaki, Mehrbod; Haiminen, Niina; Beck, Kristen L; Zhu, Qiyun; Sayyari, Erfan; Morton, James T; Armstrong, George; Tripathi, Anupriya; Gauglitz, Julia M; Marotz, Clarisse; Matteson, Nathaniel L; Martino, Cameron; Sanders, Jon G; Carrieri, Anna Paola; Song, Se Jin; Swafford, Austin D; Dorrestein, Pieter C; Andersen, Kristian G; Parida, Laxmi; Kim, Ho-Cheol; Vázquez-Baeza, Yoshiki; Knight, Rob.
Afiliación
  • Cantrell K; Department of Computer Science, Jacobs School of Engineering, University of California, San Diego, California, USA.
  • Fedarko MW; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, California, USA.
  • Rahman G; Department of Computer Science, Jacobs School of Engineering, University of California, San Diego, California, USA.
  • McDonald D; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, California, USA.
  • Yang Y; Bioinformatics and Systems Biology Program, University of California, San Diego, California, USA.
  • Zaw T; Department of Pediatrics, School of Medicine, University of California, San Diego, California, USA.
  • Gonzalez A; Department of Pediatrics, School of Medicine, University of California, San Diego, California, USA.
  • Janssen S; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, California, USA.
  • Estaki M; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, California, USA.
  • Haiminen N; Department of Pediatrics, School of Medicine, University of California, San Diego, California, USA.
  • Beck KL; Algorithmic Bioinformatics, Justus Liebig University, Giessen, Germany.
  • Zhu Q; Department of Pediatrics, School of Medicine, University of California, San Diego, California, USA.
  • Sayyari E; IBM T. J. Watson Research Center, Yorktown Heights, New York, USA.
  • Morton JT; IBM Almaden Research Center, San Jose, California, USA.
  • Armstrong G; Department of Pediatrics, School of Medicine, University of California, San Diego, California, USA.
  • Tripathi A; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, California, USA.
  • Gauglitz JM; Department of Electrical and Computer Engineering, University of California, San Diego, California, USA.
  • Marotz C; Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA.
  • Matteson NL; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, California, USA.
  • Martino C; Bioinformatics and Systems Biology Program, University of California, San Diego, California, USA.
  • Sanders JG; Department of Pediatrics, School of Medicine, University of California, San Diego, California, USA.
  • Carrieri AP; Department of Pediatrics, School of Medicine, University of California, San Diego, California, USA.
  • Song SJ; Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, California, USA.
  • Swafford AD; Department of Pediatrics, School of Medicine, University of California, San Diego, California, USA.
  • Dorrestein PC; Scripps Institution of Oceanography, University of California, San Diego, California, USA.
  • Andersen KG; Scripps Research Institute, San Diego, California, USA.
  • Parida L; Center for Microbiome Innovation, Jacobs School of Engineering, University of California, San Diego, California, USA.
  • Kim HC; Bioinformatics and Systems Biology Program, University of California, San Diego, California, USA.
  • Vázquez-Baeza Y; Department of Pediatrics, School of Medicine, University of California, San Diego, California, USA.
  • Knight R; Cornell Institute for Host-Microbe Interaction and Disease, Cornell University, Ithaca, New York, USA.
mSystems ; 6(2)2021 Mar 16.
Article en En | MEDLINE | ID: mdl-33727399
ABSTRACT
Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality-including ordination integration and animations-alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of 'omic data.IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MSystems Año: 2021 Tipo del documento: Article País de afiliación: 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 Idioma: En Revista: MSystems Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos