A common goal in many microbiome studies is to identify features (i.e., species, OTUs, gene families, etc.) that differ according to some study condition of interest. While often done, this is a difficult task, and in the Introduction to the Statistical Analysis of Microbiome Data in R post I touch on some of the reasons for this.
Below I provide scripts to implement the current default workflow for taxonomic and functional profiling using the Huttenhower Lab’s Biobakery Tool Suite used by the Microbial Metagenomics Analysis Center (MMAC) at CCHMC for paired-end data.
Below I provide scripts to implement several workflows for denoising 16s rRNA gene sequences used by the Microbial Metagenomics Analysis Center (MMAC) at CCHMC for paired-end data. These scripts are written to run on the CCHMC high-performance computing (HPC) cluster.
This is a link to a talk I will be giving to the Cincinnati Children’s Hospital Medical Center R Users Group on November 6th, 2019. The goal of the talk is to introduce members to some of the functionality provided by Frank Harrell’s Hmisc and rms …
This is post is to introduce members of the Cincinnati Children’s Hospital Medical Center R Users Group (CCHMC-RUG) to some of the functionality provided by Frank Harrell’s Hmisc and rms packages for data description and predictive modeling.
During the Introduction to Metagenomics Summer Workshop we discussed denoising amplicon sequence variants and worked through Ben Callahan’s DADA2 tutorial. During that session, I mentioned several other approaches and algorithms for denoising or clustering amplicon sequence data including UNOISE3, DeBlur and Mothur.
This post is from a tutorial demonstrating the processing of amplicon short read data in R taught as part of the Introduction to Metagenomics Summer Workshop. It provides a quick introduction some of the functionality provided by phyloseq and follows some of Paul McMurdie’s excellent tutorials.
This post is also from the Introduction to Metagenomics Summer Workshop and provides a quick introduction to some common analytic methods used to analyze microbiome data. I thought it might be of interest to a broader audience so decided to post it here.
This was a talk given as part of the UC-CCHMC introductory workshop covering the basics of microbial metagenomic sequence data processing and statistical analysis. Topics covered in this talk include an introduction to challenging features of …
This was a talk given as part of the UC-CCHMC introductory workshop covering the basics of microbial metagenomic sequence data processing and statistical analysis. Topics covered include an introduction to denoising 16S rRNA gene sequencing data, a …