Community-led, integrated, reproducible multi-omics with anvi'o

A. Murat Eren*, Evan Kiefl, Alon Shaiber, Iva Veseli, Samuel E. Miller, Matthew S. Schechter, Isaac Fink, Jessica N. Pan, Mahmoud Yousef, Emily C. Fogarty, Florian Trigodet, Andrea R. Watson, Ozcan C. Esen, Ryan M. Moore, Quentin Clayssen, Michael D. Lee, Veronika Kivenson, Elaina D. Graham, Bryan D. Merrill, Antti KarkmanDaniel Blankenberg, John M. Eppley, Andreas Sjodin, Jarrod J. Scott, Xabier Vazquez-Campos, Luke J. McKay, Elizabeth A. McDaniel, Sarah L. R. Stevens, Rika E. Anderson, Jessika Fuessel, Antonio Fernandez-Guerra, Lois Maignien, Tom O. Delmont, Amy D. Willis

*Corresponding author for this work

Research output: Contribution to journalEditorialResearchpeer-review

403 Citations (Scopus)

Abstract

Big data abound in microbiology, but the workflows designed to enable researchers to interpret data can constrain the biological questions that can be asked. Five years after anvi'o was first published, this community-led multi-omics platform is maturing into an open software ecosystem that reduces constraints in 'omics data analyses.

Original languageEnglish
JournalNature Microbiology
Volume6
Issue number1
Pages (from-to)3-6
Number of pages4
ISSN2058-5276
DOIs
Publication statusPublished - 2021

Keywords

  • MICROBIAL GENOMES

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