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Data analysis and interpretation

eagle-i ID

http://harvard.eagle-i.net/i/00000147-1d2a-0401-3e27-e08b80000000

Resource Type

  1. Data analysis service

Properties

  1. Fee for service
    Yes
  2. Resource Description
    Amplicon (16S rRNA / ITS) or shotgun metagenomic, metatranscriptomic (SG) sequencing data is passed through a quality control pipeline using the bioBakery (http://huttenhower.sph.harvard.edu/biobakery_workflows) workflows. 16S / ITS: The amplicon sequence data pipelines consist of two approaches, USEARCH / VSEARCH and DADA2 (https://bitbucket.org/biobakery/biobakery_workflows/wiki/Home#!16s-rrna-16s) to identify operational taxonomic units (OTUs) and amplicon sequence variates (ASVs), respectively. These taxonomic profiles are then passed to PICRUSt (http://picrust.github.io/picrust/index.html), which infers gene content and abundance of taxa, to predict the metagenome composition of the 16S-resolved community. PICRUSt predicted metagenomes are amenable to similar downstream analysis as metagenomes identified from shotgun sequencing data, but with taxonomic resolution limited by 16S. In tiered-design studies, MicroPita (http://huttenhower.sph.harvard.edu/micropita) takes as input results from 16S surveys to inform sample subset selection for SG follow-up work, governed by user-specified features of interest (clinical/environmental metadata, diversity measures, etc.). SG: Microbiome composition (bacteria, archaea, viruses and eukaryotic microbes) is gleaned from SG sequencing data using MetaPhlAn2 (http://huttenhower.sph.harvard.edu/metaphlan2), which resolves taxonomic diversity and abundance at the subspecies level. Metagenomes, both PICRUSt-predicted and SG-sequenced, can further be passed through the HUMAnN2 (http://huttenhower.sph.harvard.edu/humann2) pipeline. HUMAnN2 determines conservation and abundance of gene modules (sets of genes related by sequence and function) and biochemical pathways to reveal the metabolic potential of the microbial community. Data features derived with these algorithms, including gene/pathway presence and abundance, gene expression, microbiome composition, OTUs, ASVs, or peptide identifications from metaproteomics and compound tables from meta-metabolomics, can be integrated with clinical and environmental metadata using LEfSe (https://bitbucket.org/biobakery/biobakery/wiki/lefse) and MaAsLin2 (http://huttenhower.sph.harvard.edu/maaslin2) along with other packages within R statistical software. LEfSe identifies those data features that are distinct between a pair of metadatums (e.g. differences between two sampling sites, two clinical outcomes, two biochemical markers, two modalities, etc.). MaAsLin2 extends the functionality of LEfSe to identify associations between data features and multiple metadata factors, which can be discrete and/or continuous and can include time series data.
  3. Related Resource
    bioBakery - an easy to use computing environment for analyses of microbiome data
  4. Service Provided by
    Harvard Chan Microbiome Analysis Core
  5. Website(s)
    http://www.hsph.harvard.edu/hmac/services/
  6. Related Technique
    Quality control
  7. Related Technique
    Data analysis
 
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Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016