Microbiome

Wrench Normalization for Sparse Microbiome Data

I recently attended a seminar where Wrench normalization was shown to have good performance in the presented simulation studies. So I thought I would give it another look.

Identifying Differentially Abundant Features in Microbiome Data

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.

Taxonomic and Functional Profiling using Biobakery Workflows

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.

Denoising Amplicon Sequence Variants Using DADA2, DeBlur, and UNOISE3 with QIIME2

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.

Profiling of Shotgun Metagenomic Sequence Data using Kraken2/Bracken and HUMAnN2

Below I provide scripts to implement the current default workflow for taxonomic profiling using Kraken2 and Bracken and functional profiling using HUMAnN2 used by the Microbial Metagenomics Analysis Center (MMAC) at CCHMC for paired-end data.

Observation Weights for Differential Abundance of Zero-Inflated Microbiome Data with DESeq2

I recently read through Calgaro et. al. “Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data” where they examined the performance of statistical models developed for bulk RNA (RNA-seq), single-cell RNA-seq (scRNA-seq), and microbial metagenomics to:

Denoising Amplicon Sequence Data Using USEARCH and UNOISE3

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.

Downloading Amplicon Sequence Runs from the NCBI SRA

A collaborator recently asked if I could help pull down a few thousand sequence files from the NCBI Sequence Read Archive (SRA) for a secondary analysis. This is a short post primarily to help me (and hopefully others) remember how to do this once you have a set of SRR IDs of interest.

Introduction to Phyloseq

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.

Introduction to the Statistical Analysis of Microbiome Data in R

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.