http://www.ebi.ac.uk/metagenomics
Hubert DENISE
hudenise@ebi.ac.uk
1997 PhD. Molecular Parasitology
Univ. Bordeaux II, France
About me
2003 – 2005 Lecturer Molecular Biology,
Univ. Clermont-Ferrand II, France
1997 - 2003
PostDoc, WCMP
Univ. Glasgow, UK
2011 – 2012 MSc. Bioinformatics
Univ. Cranfield, UK
2005 - 2011
Sr. Scientist, Pfizer Ltd
Sandwich, UK
2012
Bioinformatician
Sanger Institute then EBI, Hinxton, UK
Where is the true cost of NGS ?
14.5 %
30 %
28 %
70 %
(~80 bp/$)
(~2m bp/$)
4.5 %
14.5 %
55 %
36.5 %
14.5 %
Sboner et al. Genome Biology (2011) 12:125
EBI Metagenomics pipeline
Data analysis using
selected EBI and
external software tools

Philosophy

Submission to EBI Metagenomics




QC steps
Overview of functional analysis
Overview of taxonomy analysis
Metagenome assembly

Result outputs

Others public pipelines
Philosophy behind EBI Metagenomics pipeline
Helping metagenomics researchers make sense of their data
From chaos to structure:
 archiving of data with metadata
 performing stringent QC filtering prior to analysis
 quality in, quality out
 performing robust taxonomy and functional analysis
 model-based rather than similarity-based approaches
 assignment done on reads rather than assembly
 intuitive navigation through website
 constant drive to improvement
 benchmarking and tool testing
EBI Metagenomics pipeline
Data analysis using
selected EBI and
external software tools

Philosophy

Submission to EBI Metagenomics




QC steps
Overview of functional analysis
Overview of taxonomy analysis
Metagenome assembly

Result outputs

Others public pipelines
http://www.ebi.ac.uk/metagenomics
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Latest data and news
Submitting to EBI Metagenomics
• Your data is valuable to you
• Raw sequence data
• Description of sample and experiment (sample metadata)
• Analysis steps and results
• All of this needs to be captured and stored to give
context to your data
• If so, your data can also be valuable to others
Submitting to EBI Metagenomics
• EBI Metagenomics want to encourage people to supply as much
detailed metadata as possible, but with the lowest possible
overhead
who
where, when, what
how
• Development of intuitive web-based tools : ENA Webin and ISA tools
• Use of templates and check-lists (MIGS/MIXS standards)
• Tutorial and direct support
EBI Metagenomics pipeline
Data analysis using
selected EBI and
external software tools

Phylosophy

Submission to EBI Metagenomics




QC steps
Overview of functional analysis
Overview of taxonomy analysis
Metagenome assembly

Result outputs

Others public pipelines
Metagenomics data analysis
Diversity analysis
Quality control
Functional analysis
Image credits:
(1) Christina Toft & Siv G. E. Andersson; (2) Dalebroux Z D et al. Microbiol. Mol. Biol. Rev. 2010;74:171-199
Overview of EBI Metagenomics Pipeline
raw reads
trim and QC
remove short
remove duplicates
processed
reads
rRNAselector
discarded
reads
Amplicon-based data
reads
with
rRNA
reads
without
rRNA
FragGeneScan
predicted
CDS
Qiime
InterProScan
Taxonomic
analysis
Function
assignment
Unknown
function
pCDS
EBI Metagenomics: QC rationale
Why ?
 Garbage in, garbage out
 Base call error: - each base call has a quality score associated
- specific platform-dependent errors
 Reads quality decreases with reads length
 NGS generates duplicate reads (false and real). Reducing
duplication reduces analysis time and prevent analysis bias.
EBI Metagenomics: QC step by step
 Clipping - low quality ends trimmed and adapter sequences
removed using Biopython SeqIO package
 Quality filtering - sequences with > 10% undetermined nucleotides removed
 Read length filtering - short sequences are removed
 Duplicate sequences removal - clustered on 99% identity (UCLUST v 1.1.579) and
representative sequence chosen
 Repeat masking - RepeatMasker (open-3.2.2), removed reads with 50% or more
nucleotides masked
EBI Metagenomics: QC consequences
Roche 454
Ion Torrent
Illumina
EBI Metagenomics: overview of functional analysis
reads
without
rRNA
FragGeneScan
predicted
CDS
InterProScan
Function
assignment
Unknown
function
pCDS
EBI Metagenomics: identification of coding sequences
Prediction of coding sequences is a challenge
 read length
 sequencing errors: frame-shift
Two main types of approaches:
 homology-based methods: identify only known coding sequences
 feature-based approaches: predict probability that ORFs are coding
EBI Metagenomics uses FragGeneScan :
 hidden Markov models to correct frame-shift using codon usage
 probabilistic identification of start and stop codons
 60 bp minimum ORF
Rho et al. (2010) NAR 38-20
EBI Metagenomics: annotation of coding sequences
Most available pipelines use pairwise alignment methods (such as BLAST)
 compare a query sequence with a database of sequences
 identify database sequences that resemble the query sequence with
homology score above a certain threshold
However sequences may appear to have low homology score because:
 proteins may share homology only in limited domains
 proteins from different species can differ in length
Example: first line of blast alignment of 60S acidic ribosomal protein P0 from 2
closely-related species
Using BLAST for annotation
EBI Metagenomics: advantage of InterPro
EBI Metagenomics pipeline do not use BLAST-based methods to associate
functions to predicted protein sequences: instead we use InterProScan to mine
the InterPro database.
InterPro database (HMM and profile –based functional analysis) is based on
presence of “signatures” (models) from eleven databases
 Specificity: mapping is manually curated
IPR024185: 5-formyltetrahydrofolate cyclo-ligase-like
IPR000847: Transcription regulator HTH, LysR
 Speed
Test set of 40,692 predicted protein sequences
 BLAST vs UniRef100 = 21.5 s/cds
 InterProScan (5 databases) = 3 s/cds
EBI Metagenomics: InterProScan annotations
member
database
pCDS
signature
accession
signature
description
SRR413626.9733695_1_1_105_- ProSitePatterns PS00194 Thioredoxin family
active site
1.0E-13 IPR017937
score
InterPro
accession
Thioredoxin, conserved site GO:0045454
InterPro
description
GO
annotation
EBI Metagenomics: InterProScan annotations
signatures
links
description
GO terms
Aims of the Gene Ontology
• Controlled vocabulary
• Unify the representation of gene and gene product attributes
across species
• Allow cross-species and/or cross-database comparisons
Inconsistency in naming of biological concepts
English is not a very precise language
• Same name for different concepts
• Different names for the same concept
An example …
Taction
Tactition
Tactile sense
Sensory perception of touch
? ; GO:0050975
The Gene Ontology
Less specific concepts
• A way to capture
biological knowledge
in a written and
computable form
• A set of concepts
and their relationships
to each other arranged
as a hierarchy
More specific concepts
www.ebi.ac.uk/QuickGO
The Concepts in GO
•
•
1. Molecular Function
An elemental activity or task or job
protein kinase activity
insulin receptor
activity
2. Biological Process
A commonly recognised series of events
•
cell division
• mitochondrion
3. Cellular Component
Where a gene product is located
• mitochondrial matrix
• mitochondrial inner membrane
The relationship between InterPro and GO
(InterPro2GO)
• Curators manually add relevant GO terms to InterPro entries
• When a sequence is searched against InterPro, it is assigned GO terms by
virtue of the entries it matches
SRR413626.11302948_1_1_133_+
8.9E-6 IPR003439
Pfam
PF00005 ABC transporter 6
ABC transporter-like
GO:0005524|GO:0016887
ATP binding
ATPase activity
EBI Metagenomics: overview of taxonomy analysis
processed
reads
rRNAselector
reads
with
rRNA
Amplicon-based data
Qiime
Taxonomic
analysis
EBI Metagenomics: identification of suitable sequences
Taxonomy analysis is generally based on identification and classification of rRNA
sequences

Prokaryotes: archaebacteria and eubacteria: 5S, 16S and 23S

Eukaryotes: 5S, 5.8S, 18S and 28S

there is no equivalent for virus so depend on DNA polymerase or part of
5’-UTR (internal ribosomal entry site [IRES]) sequences
EBI Metagenomics currently only provide taxonomy analysis for Prokaryotes.
rRNA sequences are identified using rRNASelector :
 hidden Markov models to identified rRNA sequences
 60 bp minimum overlap with well-curated HMM model
 E-value < 10-5
Lee et al (2011) J Microbiol. 49(4)
EBI Metagenomics: identification of suitable sequences
Once identified, rRNA sequences are clustered and classified using Qiime
“QIIME stands for Quantitative Insights Into Microbial Ecology. QIIME is an open
source software package for comparison and analysis of microbial communities”
The main steps are:
 clustering sequences in Operational Taxonomy Unit (OTU) using uclust
 picking a representative sequence set (one sequence from each OTU)
 aligning the representative sequence set
 assigning taxonomy to the representative sequence set using PyNAST
 generating output files:
 filtering the alignment prior to tree building
 building phylogenetic tree
 creating OTU table
EBI Metagenomics: validation of taxonomy analysis
Re-analysis of: Sutton et al, Appl. Environ. Microbiol (2013), 79(2):619
Impact of Long-Term Diesel Contamination on Soil Microbial Community Structure.
Alpha diversity analysis
clean
polluted
clean (outlier)
Assembly of metagenomics data
• Metagenomics: Not clear how you avoid assembling
sequences from different species together :
• No reference sequence to align against
chimaera
EBI Metagenomics currently do not perform assembly
We are still able to annotate metagenome as show by this re-analysis
of Rumen metagenomics by Hess et al, Science (1011) 331:463
What are the consequences ?
 cannot link taxonomy information to functional annotations
 cannot currently perform viral taxonomy analysis
EBI Metagenomics pipeline in a nut shell
 QC :
- trim adaptor sequences, low quality sequence ends
- remove duplicates and short sequences
- remove low complexity sequences,
“Powerful and sophisticated alternative to BLAST-based functional metagenomic
analysis”
 Diversity analysis :
- identify prokaryotic rRNAsequences (5, 16 and 23s)
- cluster rRNA-containing reads
- assign taxonomy classificationusing Qiime,
 Functional analysis :
- predict ORFs
- translate ORFs into peptides
- submit to InterProScan for functional annotation
EBI Metagenomics pipeline
Data analysis using
selected EBI and
external software tools

Submission


Philosophy
Overview data analysis




QC steps
Overview of functional analysis
Overview of taxonomy analysis
Metagenome assembly

Result outputs

Others public pipelines
Current outputs of EBI Metagenomics
pipeline
Visualisation
Download
- QC and sequence statistics
- Diversity analysis
- Functional analysis
Current outputs of EBI Metagenomics
pipeline
navigation tabs
Access via the Sample page
EBI Metagenomics pipeline: taxonomy visualisation
switch to bar chart,
column or Krona
interactive views
Krona interactive
representation
Google charts
dynamic
representation
EBI Metagenomics pipeline: functional visualisation
Google charts
dynamic
representation
links to InterPro website
switch to bar
chart view
EBI Metagenomics pipeline : download
options
470 MB: need high computing power to manipulate:
EBI Metagenomics take care of it and
extract meaningful information sets
relatively small files: can be manipulated on
labtop/desktop computer: users can filtered
them according to their needs
EBI Metagenomics pipeline
Data analysis using
selected EBI and
external software tools

Submission


Philosophy
Overview data analysis




QC steps
Overview of functional analysis
Overview of taxonomy analysis
Metagenome assembly

Result outputs

Others public pipelines
Metagenomics data analysis
Quality control
Quality control
Pipeline 1 Taxonomy analysis
Taxonomy analysis
Functional analysis
Functional analysis
results 1
results 2
 should share trends and main findings
 could differ in ratio and assignment
Pipeline 2
Public Metagenomics portals
http://www.ebi.ac.uk/metagenomics/
http://camera.calit2.net/
http://metagenomics.anl.gov/
http://img.jgi.doe.gov/
Simplified overview of MG-RAST pipeline
Sequencer output
Abundance profiles
• Community reconstruction
• Metabolic reconstruction
• Metabolic model
http://metagenomics.anl.gov/
Quality control
Similarities search
Blat
Feature prediction
(FragGeneScan)
Clustering (Uclust)
MG-RAST and EBI Metagenomics QC comparison
Example: Analysis of Prairie Soil Sample
Upload: bp Count
Upload: Sequences Count
Upload: Mean Sequence Length
Upload: Mean GC percent
MG-RAST
EBI Metagenomics
391,415,961 bp
391,415,961 bp
946,839
946,839
413 ± 125 bp
413.39 bp
61 ± 8 %
61.2 %
Artificial Duplicate Reads: Sequence Count
Post QC: bp Count
Post QC: Sequences Count
Post QC: Mean Sequence Length
Post QC: Mean GC percent
0
391,415,961 bp
946,839
413 ± 125 bp
61 ± 8 %
0
388,670,692 bp
908,602
380.43 bp
57.8 %
972,409
5
510,221
999,433
3
480,560
1,069
1,110
442,070
462,475
Processed: Predicted Protein Features
Processed: Predicted rRNA Features
Alignment: Identified Protein Features
Alignment: Identified rRNA Features
Annotation: Identified Functional Categories
MG-RAST and EBI Metagenomics Functional analysis
Example: Analysis of Prairie Soil Sample
ammonia monooxygenase: NH3 + A-H2 + O2
NH2OH + A + H2O
MG-RAST: 28 unique hits on 8 different protein databases
12
2
4
5
62
3
4
1 ammonia monooxygenase family protein
Ammonia monooxygenase
2 ammonia monooxygenase
subunit A
ammonia monooxygenase
family protein
1 ammonia monooxygenase,
putative
Ammonia monooxygenase
subunit A
6 putative ammonia
Ammonia monooxygenase,
putativemonooxygenase
2 Putative
ammonia monooxygenase
Putative ammonia
monooxygenase
1 putative
ammonia monooxygenase
subunit A
putative ammonia
monooxygenase
protein
putative ammonia
monooxygenase
subunit A
1 putative
ammonia monooxygenase
3 Putative ammonia monooxygenase
5 Ammonia monooxygenase
8
18
13
11
8
10
12
9
KEGG
eggNOG
GenBank
GenBank
IMG
PATRIC
RefSeq
TrEMBL
SEED
what do the abundance numbers mean ?
EBI Metagenomics:
3
25
IPR003393
IPR007820
Ammonia monooxygenase/particulate methane monooxygenase, subunit A
Putative ammonia monooxygenase/protein AbrB
MG-RAST and EBI Metagenomics Taxonomy analysis
Example: Analysis of Prairie Soil Sample
MG-RAST
domain level of taxonomy
Bacteria (55 categories)
Archaebacteria (15 categories)
Eukaryotes (98 categories)
Others (including virus) (3 types)
EBI Metagenomics
only Archae/Bacteria taxonomy (333 OTU)
Overview of CAMERA workflow
Integrated Microbial Genomes and Metagenomes analysis tools
Some other Metagenomics tools
http://ab.inf.uni-tuebingen.de/software/megan/
http://www.computationalbioenergy.org/software.html
http://cbcb.umd.edu/software/metAMOS
Overview of MEGAN
MEGAN
rdp,biome files
csv, tsv files
Comparative visualisation
abundance plots
PCA, clustering,
co-occurrence
seq comparison
and assignment
blast output
SAM files
csv, tsv files
QC ?
Taxonomy analysis
Functional analysis
SEED
KEGG
COG/EGGNOG
Example of taxonomy analysis using MEGAN
diverse single and multi-sample visualisations
Example of taxonomy analysis using MEGAN
Comparison, PCA and co-occurrence plots
EBI Metagenomics pipeline
Data analysis using
selected EBI and
external software tools

Submission


Philosophy
Overview data analysis




QC steps
Overview of functional analysis
Overview of taxonomy analysis
Metagenome assembly

Result outputs

Others public pipelines
http://www.ebi.ac.uk/metagenomics