Statistical Analysis of Metagenomic Sequence Data
This is a self-guided, open-source course that guides students through use of R to perform statistical analyses on count matrices generated from microbiome and metagenomic sequence data. The course was first conducted in summer 2020 and is available through our GitHub: https://github.com/EnriqueDoster/MEG_intro_stats_course
The "openROAMER" project provides open-source resources to the dairy community to support microbiome-based education, research and development. This USDA NIFA-funded project is ongoing, and we will be posting resources on the project website.
Meta-MARC is a computational method for identifying antimicrobial resistance sequences in metagenomic data using DNA-based Hidden Markov Models. Because of its increased sensitivity, Meta-MARC is able to detect novel antimicrobial resistance sequences that are divergent from known sequences. Meta-MARC is developed, published and maintained by the Microbial Ecology Group, and was published in 2019 in Communications Biology.
The MEGaRICH enrichment system utilizes cDNA biotinylated "baits" to capture and amplify antimicrobial resistance genes and virulence factors within metagenomic DNA. Developed, published and maintained by the Microbial Ecology Group (including Chris and Noelle), the MEGaRICH system was published in 2018 in Microbiome.
The MEGaRES database contains the sequences of antimicrobial resistance genes, and is specifically designed for use with shotgun metagenomic data (i.e., "resistome" analysis). Developed, published and maintained by the Microbial Ecology Group, MEGaRES was originally published in 2017 and then substantially updated in 2019 in Nucleic Acids Research.
AMR++ is a bioinformatics pipeline that enables user-friendly resistome analysis of shotgun metagenomic data -- either through Galaxy, or through your own server. Developed, published and maintained by the Microbial Ecology Group, AMR++ was published in 2017 and updated in 2019 in Nucleic Acids Research.
Tychus is an open-source bioinformatics pipeline that enables massively parallel whole genome sequence (WGS) analysis of bacterial genomes. Key features include assembly, annotation, alignment, variant discovery and phylogenetic inference of large numbers of WGS isolates. Tychus is published through bioRxiv and is developed and maintained by Zaid Abdo's lab.