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.

Urinary Microbiota Associated with Preterm Birth: Results from the Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) Study

Preterm birth (PTB) is the leading cause of infant morbidity and mortality. Genitourinary infection is implicated in the initiation of spontaneous PTB; however, examination of the urinary microbiota in relation to preterm delivery using …

Cervical Microbiota Associated With Higher Grade Cervical Intraepithelial Neoplasia in Women Infected With High Risk Human Papillomaviruses

It is increasingly recognized that microbes that reside in and on human body sites play major roles in modifying the pathogenesis of several diseases, including cancer. However, specific microbes or microbial communities that can be mechanistically …

The human milk oligosaccharide 2'-fucosyllactose augments the adaptive response to extensive intestinal resection

Intestinal resection resulting in short bowel syndrome (SBS) carries a heavy burden of long-term morbidity, mortality, and cost of care, which can be attenuated with strategies that improve intestinal adaptation. SBS infants fed human milk, compared …