Empress enables tree-guided, interactive, and exploratory analyses of multi-omic data sets

Kalen Cantrell, Marcus W. Fedarko, Gibraan Rahman, Daniel McDonald, Yimeng Yang, Thant Zaw, Antonio Gonzalez, Stefan Janssen, Mehrbod Estaki, Niina Haiminen, Kristen L. Beck, Qiyun Zhu, Erfan Sayyari, James T. Morton, George Armstrong, Anupriya Tripathi, Julia M. Gauglitz, Clarisse Marotz, Nathaniel L. Matteson, Cameron MartinoJon G. Sanders, Anna Paola Carrieri, Se Jin Song, Austin D. Swafford, Pieter C. Dorrestein, Kristian G. Andersen, Laxmi Parida, Ho Cheol Kim, Yoshiki Vázquez-Baeza, Rob Knight

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere01216-20
JournalmSystems
Volume6
Issue number2
DOIs
StatePublished - Mar 2021
Externally publishedYes

Keywords

  • Bioinformatics
  • Microbial ecology

ASJC Scopus subject areas

  • Microbiology
  • Physiology
  • Biochemistry
  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computer Science Applications

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