Metagenomics dataset analysis

1. Metabolic profiling of microbial
communities using metagenomics datasets
of inflammatory bowel diseases patients
treated with fecal transplant therapy

2. INTRODUCTION
• Inflammatory bowel disease (IBD) is an umbrella term used to describe
disorders that involve chronic inflammation of your digestive tract.
Types of IBD include:
• Ulcerative colitis. This condition causes long-lasting inflammation and
sores (ulcers) in the innermost lining of your large intestine (colon) and
rectum.
• Crohn's disease. This type of IBD is characterized by inflammation of
the lining of your digestive tract, which often spreads deep into
affected tissues.
• The intestinal microbiota is thought to play a central role in the
etiopathogenesis of inflammatory bowel disease (IBD; Crohn's disease
and ulcerative colitis), a chronic inflammation of the gut mucosa.
• In healthy individuals, the microbiota is efficiently separated from the
mucosal immune system of the gut by the gut barrier, a single layer of
highly specialized epithelial cells, some of which are equipped with
innate immune functions to prevent or control access of bacterial
antigens to the mucosal immune cells.
• It is currently unclear whether the composition of the microbial flora or
individual bacterial strains or pathogens induces or supports the
pathogenesis of IBD.
• Further research will be necessary to carefully dissect the contribution
of individual bacterial species to this disease and to ascertain whether
specific modulation of the intestinal microbiome may represent a
valuable further option for future therapeutic strategies.

3. Fecal microbiota transplant
• There has been increasing interest in understanding the
role of the human gut microbiome to elucidate the
therapeutic potential of its manipulation.
• Fecal microbiota transplantation (FMT) is the
administration of a solution of fecal matter from a donor
into the intestinal tract of a recipient in order to directly
change the recipient’s gut microbial composition and
confer a health benefit.
• FMT has been used to successfully treat
recurrent Clostridium difficile infection. There are
preliminary indications to suggest that it may also carry
therapeutic potential for other conditions such as
inflammatory bowel disease, obesity, metabolic
syndrome, and functional gastrointestinal disorders.

4. Raw data 16s
Adaptor removal
Merge paired reads
ConvsPreFMT PreFMTvsPostFMT
Group samples
OTU picking, taxonomic
analysis
OTU picking, taxonomic analysis
Pathway analysis Pathway analysis
Metabolite prediction Metabolite prediction
Metabolites

5. QIIME – Quantitative Insight Into Microbial Ecology
• QIIME is an open-source bioinformatics pipeline for performing microbiome analysis from raw
DNA sequencing data.
• QIIME is designed to take users from raw sequencing data generated on the Illumina or other
platforms through publication quality graphics and statistics. This includes OTU picking,
taxonomic assignment, and phylogenetic reconstruction, and diversity analyses and visualizations.
• QIIME (Quantitative Insights Into Microbial Ecology) Pipeline to process data from high-
throughput 16S rRNA sequencing studies. IT has been applied to studies based on billions of
sequences from tens of thousands of samples.
• To process our data, we will perform the following steps, each of which is described in more detail
in the Data Analysis Steps:
Pick Operational Taxonomic Units (OTUs) based on sequence similarity within the reads, and pick
a representative sequence from each OTU.
Assign the OTU to a taxonomic identity using reference databases.
Align the OTU sequences and create a phylogenetic tree.
Calculate diversity metrics for each sample and compare the types of communities, using the
taxonomic and phylogenetic assignments.
Generate UPGMA and PCoA plots to visually depict the differences between the samples, and
dynamically work with these graphs to generate publication quality figures.

11. Alpha diversity
Species richness (OTU count) "How many?"
How many different species could be
detected in a microbial ecosystem?
Species richness is the number of different
species in a sample.
Practically, we count the number of
distinguishable taxa:

12. Species diversity (Shannon index) "How different?"
How are the microbes balanced to each other? Do we have
species evenness (similar abundance level) or do some species
dominate others?
Shannon index measures how evenly the microbes are
distributed in a sample.

13.  Beta-diversity metrics assess the
differences between microbial
communities. In general, these
metrics are calculated to study
diversity along an environmental
gradient (pH or temperature) or
different disease states (lean vs.
obese).
 The basic output of this comparison
is a square matrix where a
“distance” is calculated between
every pair of samples reflecting the
similarity between the samples. The
data in this distance matrix can be
visualized with clustering analyses,
namely Principal Coordinate
Analysis (PCoA) and UPGMA
clustering.

14. Functional profiling using Picrust
• PICRUSt (pronounced “pie crust”) is a
bioinformatics software package designed to
predict metagenome functional content from
marker gene (e.g., 16S rRNA) surveys and
full genomes.
• It creates the final metagenome functional
predictions. It multiplies each normalized
OTU abundance by each predicted functional
trait abundance to produce a table of
functions (rows) by samples (columns).
• PICRUSt by default uses the relatively
new biom format for representing OTU
tables and Gene tables (e.g. KOs by
samples). This has several benefits including
easier integration with other software (e.g.
QIIME and others in the future) and allows
embedding of extra metadata about both the
samples and observations (OTUs/KOs).

16. save.image("/data/shikha/integratomics_result/res/shikha_met
hylation_final_paper.RData")
https://acmedsci.ac.uk/grants-and-schemes/grant-
schemes/newton-international-fellowships
http://www.compbio.dundee.ac.uk/user/pschofield/Projects/teaching_
pg/workshops/biocNGS.html

17. Differential expression analysis of
functional profling by picrust

18. METABOLITE prediction using Melonnpan R
package
 The MelonnPan-Predict workflow takes
a table of microbial sequence features as
input (i.e. taxonomic or functional
abundances on a per sample basis) and
outputs a predicted metabolomic table
(i.e. relative abundances of metabolite
compounds across samples).
 The MelonnPan-Train workflow creates
a weight matrix that links an optimal set
of sequence features to a subset of
predictable metabolites following
rigorous internal validation, which is then
used to generate a table of predicted
metabolite compounds (i.e. relative
abundances of metabolite compounds per
sample). When sufficiently accurate,
these predicted metabolite relative
abundances can be used for downstream
statistical analysis and end-to-end

19. INPUT: Functional profiling OUTPUT: metabolite abundance
Melonpann
R package

20. https://github.com/mathelab/IntLIM