Challenges in benchmarking metagenomic profilers is a research paper published in Nature Methods (2021). On theSindex it has a DataRank of 0. It has been cited 157 times.
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U.S. Department of Health & Human Services | National Institutes of Health
Grant: R01AI141529
U.S. Department of Health & Human Services | National Institutes of Health
Grant: R01HD093761
U.S. Department of Health & Human Services | National Institutes of Health
Grant: UH3OD023268
U.S. Department of Health & Human Services | National Institutes of Health
Grant: U19AI095219
U.S. Department of Health & Human Services | National Institutes of Health
Grant: U01HL089856
NIA NIH HHS
Grant: R01 AG067744
NIA NIH HHS
Grant: RF1 AG067744
NIAID NIH HHS
Grant: T32 AI007306
Rob Knight was supported by IBM Research through the AI Horizons Network, UC San Diego AI for Healthy Living program in partnership with the UC San Diego Center for Microbiome Innovation
MeSH Terms
Additional file 1 of Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M
Additional file 1 of Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M
Additional file 3 of Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M
Additional file 3 of Species-resolved sequencing of low-biomass or degraded microbiomes using 2bRAD-M
Additional file 1 of Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools
Additional file 1 of Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools
Additional file 2 of Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools
Additional file 2 of Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools
Additional file 1 of Comparative analysis of metagenomic classifiers for long-read sequencing datasets
Additional file 1 of Comparative analysis of metagenomic classifiers for long-read sequencing datasets
Additional file 3 of Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools
Additional file 3 of Modeling the limits of detection for antimicrobial resistance genes in agri-food samples: a comparative analysis of bioinformatics tools
Additional file 2 of A culture-independent approach, supervised machine learning, and the characterization of the microbial community composition of coastal areas across the Bay of Bengal and the Arabian Sea
Additional file 2 of A culture-independent approach, supervised machine learning, and the characterization of the microbial community composition of coastal areas across the Bay of Bengal and the Arabian Sea
Additional file 2 of Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes
Additional file 2 of Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes
Additional file 3 of Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes
Additional file 3 of Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes
Additional file 1 of Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes
Additional file 1 of Melon: metagenomic long-read-based taxonomic identification and quantification using marker genes