Species-level functional profiling of metagenomes and metatranscriptomes

By Eric A. Franzosa, Lauren J. McIver, Gholamali Rahnavard, Luke R. Thompson, M. Schirmer, George Weingart, Karen Schwarzberg Lipson, Rob Knight, J. Gregory Caporaso, Nicola Segata and 1 more. Nature Methods, 2018

1,427 citations recorded by the source corpus.

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. HUMAnN2 uses a tiered sequence search to provide rapid and accurate species-level functional profiles of microbial communities from metagenomic and metatranscriptomic data.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6981d49752230595163e89a8b7652089d03c664a

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Citations in the corpus

Enterotypes of the human gut mycobiome (2022, background): “1(61) by mapping reads to Uniref90(62) and MetaCyc(63) 510 reference databases.”
Advances and Challenges in Metatranscriptomic Analysis (2019, methodology): “Almost all eight workflows include a form of preprocessing or quality control of raw data, with the exception of HUMAnN2.”
Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice (2019, methodology): “HUMAnN2 and ShortBRED analyses (Franzosa et al., 2018; Kaminski et al., 2015) of metagenomic data enabled exploring possible pathways for taurine and 5AV metabolism.”
Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 (2020, methodology): “1A ) (McIver et al., 2018) .”
Multivariable association discovery in population-scale meta-omics studies (2021, methodology): “Metagenomes and metatranscriptomes were functionally profiled using HUMAnN 2 [40] to quantify MetaCyc pathways [41], and taxonomic profiles from metagenomes were determined using MetaPhlAn 2 [42] (S3 Text).”
Using clusterProfiler to characterize multiomics data (2024, neutral): “76), organizing gene abundances by KEGG KO numbers or EC numbers.”
Bifidobacteria-mediated immune system imprinting early in life (2020, neutral): “1 (Franzosa et al., 2018) bypassing all steps except ‘‘nucleotide-search’’ and ‘‘evalue 0.”
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021, background): “(Franzosa et al., 2018) for coverage and abundance of metabolic pathways and gene families”
Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4 (2023, methodology): “…studies at a larger scale than what has been described so far 40–42 ,45, From this SGB genome catalog, we built the pangenome of each SGB (collection of all gene families found in at least one genome in identify and quantify the known taxa and genes present in a microbiome by homology instead 4–7 .”
Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle (2019, background): “…are available in Table S1 and are included in the curatedMetagenomicData package (Pa-solli et al., 2017) together with all the taxonomic (Segata et al., 2012b; Truong et al., 2015) and functional potential proﬁles (Franzosa et al., 2018) of the microbial species with available reference genomes.”