JL Weissman, Shengwei Hou and Jed Fuhrman (2022), Using DNA to predict how fast bacteria can grow, Frontiers for Young Minds, doi: 10.3389. frym.2022.714713
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NEW CBIOMES PUBLICATION
Tianqi Tang, Shengwei Hou, Jed A Fuhrman, Fengzhu Sun (2022), Phage–bacterial contig association prediction with a convolutional neural network, Bioinformatics, doi: 10.1093/bioinformatics/btac239
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Predicting maximum growth rates from genomes and metagenomes for prokaryotes and eukaryotes with gRodon2 – J.L. Weissman (USC)
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The Fuhrman Lab goes to Annual Meeting 2021
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2021 Annual Meeting microtalk – DNA Tags + Metagenomes for Whole Community Analysis – Delaney Nolin (USC)
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2021 Annual Meeting microtalk: Predicting maximal growth rates from genomes and metagenomes for prokaryotes and eukaryotes – Jake Weissman (USC Guest)
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2021 Annual Meeting microtalk: A Metagenomic Survey of Prokaryote, Eukaryote and Virus Patterns in the Open Oceans, and Implications for Virus Infection Patterns – Shengwei Hou (USC)
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NEW CBIOMES PUBLICATION
Weissman, Jake L, Shengwei Hou, Jed A Fuhrman (2021), Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns, PNAS, doi: 10.1073/pnas.2016810118
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DeepEukFinder: A versatile multiclass classifier enables accurate classification of metagenomic contigs – Shengwei Hou (USC)
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Using Metagenomics to Measure In-Situ Microbial Growth Rates
Growth rates are central to understanding microbial interactions and community dynamics. The Fuhrman Lab, which uses ‘omics data to seek a better understanding of microbial growth, interactions, and biogeographies has been evaluating a promising new approach to simultaneously determine the growth rates of many different kinds of microbes from the within-genome distributions of DNA extracted from in-situ (mixed) ocean populations. Continue reading “Using Metagenomics to Measure In-Situ Microbial Growth Rates”