Literature DB >> 33944776

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3.

Francesco Beghini1, Lauren J McIver2, Aitor Blanco-Míguez1, Leonard Dubois1, Francesco Asnicar1, Sagun Maharjan2,3, Ana Mailyan2,3, Paolo Manghi1, Matthias Scholz4, Andrew Maltez Thomas1, Mireia Valles-Colomer1, George Weingart2,3, Yancong Zhang2,3, Moreno Zolfo1, Curtis Huttenhower2,3, Eric A Franzosa2,3, Nicola Segata1,5.   

Abstract

Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.
© 2021, Beghini et al.

Entities:  

Keywords:  computational analysis; computational biology; human; infectious disease; metagenomics; microbial genomics; microbiology; microbiome; systems biology

Mesh:

Year:  2021        PMID: 33944776      PMCID: PMC8096432          DOI: 10.7554/eLife.65088

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.140


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