Abstract

Functional profiles of microbial communities are typically generated using comprehensive metagenomic or metatranscriptomic sequence read searches, which are time-consuming, prone to spurious mapping, and often limited to community-level quantification. We developed HUMAnN2, a tiered search strategy that enables fast, accurate, and species-resolved functional profiling of host-associated and environmental communities. HUMAnN2 identifies a community's known species, aligns reads to their pangenomes, performs translated search on unclassified reads, and finally quantifies gene families and pathways. Relative to pure translated search, HUMAnN2 is faster and produces more accurate gene family profiles. We applied HUMAnN2 to study clinal variation in marine metabolism, ecological contribution patterns among human microbiome pathways, variation in species' genomic versus transcriptional contributions, and strain profiling. Further, we introduce 'contributional diversity' to explain patterns of ecological assembly across different microbial community types.

Keywords

MetagenomicsBiologyProfiling (computer programming)Computational biologyMicrobiomeSpurious relationshipHuman Microbiome ProjectHuman microbiomeEvolutionary biologyFunctional diversityGeneEcologyBioinformaticsGeneticsComputer scienceMachine learning

MeSH Terms

BacteriaBacterial ProteinsGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansMetagenomeMicrobiotaSoftwareSpecies SpecificityTranscriptome

Affiliated Institutions

Related Publications

Publication Info

Year
2018
Type
article
Volume
15
Issue
11
Pages
962-968
Citations
1591
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1591
OpenAlex
102
Influential

Cite This

Eric A. Franzosa, Lauren J. McIver, Ali Rahnavard et al. (2018). Species-level functional profiling of metagenomes and metatranscriptomes. Nature Methods , 15 (11) , 962-968. https://doi.org/10.1038/s41592-018-0176-y

Identifiers

DOI
10.1038/s41592-018-0176-y
PMID
30377376
PMCID
PMC6235447

Data Quality

Data completeness: 86%