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Hubert DENISE
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About me 1997 PhD. Molecular Parasitology Univ. Bordeaux II, France
2003 – Lecturer Molecular Biology, Univ. Clermont-Ferrand II, France PostDoc, WCMP Univ. Glasgow, UK 2011 – MSc. Bioinformatics Univ. Cranfield, UK Sr. Scientist, Pfizer Ltd Sandwich, UK 2012 Bioinformatician Sanger Institute then EBI, Hinxton, UK
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Where is the true cost of NGS ?
14.5 % 30 % 28 % (~2m bp/$) 4.5 % 70 % (~80 bp/$) 14.5 % 55 % 36.5 % 14.5 % Sboner et al. Genome Biology (2011) 12:125
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Data analysis using selected EBI and external software tools
EBI Metagenomics pipeline Philosophy Submission to EBI Metagenomics QC steps Overview of functional analysis Overview of taxonomy analysis Metagenome assembly Result outputs Others public pipelines Data analysis using selected EBI and external software tools
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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
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Data analysis using selected EBI and external software tools
EBI Metagenomics pipeline Philosophy Submission to EBI Metagenomics QC steps Overview of functional analysis Overview of taxonomy analysis Metagenome assembly Result outputs Others public pipelines Data analysis using selected EBI and external software tools
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http://www.ebi.ac.uk/metagenomics secure login Resource stats
Navigation panes Resource stats Latest data and news
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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
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Submitting to EBI Metagenomics
EBI Metagenomics want to encourage people to supply as much detailed metadata as possible, but with the lowest possible overhead Development of intuitive web-based tools : ENA Webin and ISA tools Use of templates and check-lists (MIGS/MIXS standards) Tutorial and direct support who where, when, what how
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Data analysis using selected EBI and external software tools
EBI Metagenomics pipeline Phylosophy Submission to EBI Metagenomics QC steps Overview of functional analysis Overview of taxonomy analysis Metagenome assembly Result outputs Others public pipelines Data analysis using selected EBI and external software tools
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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:
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Overview of EBI Metagenomics Pipeline
raw reads processed reads discarded reads trim and QC remove short remove duplicates rRNAselector reads with rRNA reads without rRNA FragGeneScan predicted CDS InterProScan Function assignment Unknown function pCDS Amplicon-based data Qiime Taxonomic analysis
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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.
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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 ) and representative sequence chosen Repeat masking - RepeatMasker (open-3.2.2), removed reads with 50% or more nucleotides masked
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EBI Metagenomics: QC consequences
Roche 454 Ion Torrent Illumina
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EBI Metagenomics: overview of functional analysis
reads without rRNA predicted CDS FragGeneScan Unknown function pCDS InterProScan Function assignment
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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
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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
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Using BLAST for annotation
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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
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EBI Metagenomics: InterProScan annotations
member database signature accession signature description pCDS SRR _1_1_105_- ProSitePatterns PS00194 Thioredoxin family active site 1.0E-13 IPR Thioredoxin, conserved site GO: score InterPro accession InterPro description GO annotation
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EBI Metagenomics: InterProScan annotations
links signatures description GO terms
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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
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? Inconsistency in naming of biological concepts An example …
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:
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The Gene Ontology A way to capture biological knowledge
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
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The Concepts in GO 1. Molecular Function 2. Biological Process
protein kinase activity insulin receptor activity An elemental activity or task or job 2. Biological Process A commonly recognised series of events cell division mitochondrion mitochondrial matrix mitochondrial inner membrane 3. Cellular Component Where a gene product is located
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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 SRR _1_1_133_+ Pfam PF00005 ABC transporter 6 8.9E-6 IPR ABC transporter-like GO: |GO: ATP binding ATPase activity 27
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EBI Metagenomics: overview of taxonomy analysis
processed reads rRNAselector reads with rRNA Amplicon-based data Qiime Taxonomic analysis
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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)
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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
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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)
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Assembly of metagenomics data
Metagenomics: Not clear how you avoid assembling sequences from different species together : chimaera No reference sequence to align against
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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
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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
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Data analysis using selected EBI and external software tools
EBI Metagenomics pipeline Submission Philosophy Overview data analysis QC steps Overview of functional analysis Overview of taxonomy analysis Metagenome assembly Result outputs Others public pipelines Data analysis using selected EBI and external software tools
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Current outputs of EBI Metagenomics pipeline
Visualisation Download - QC and sequence statistics - Diversity analysis - Functional analysis
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Current outputs of EBI Metagenomics pipeline
navigation tabs Access via the Sample page
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EBI Metagenomics pipeline: taxonomy visualisation
switch to bar chart, column or Krona interactive views Krona interactive representation Google charts dynamic representation
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EBI Metagenomics pipeline: functional visualisation
Google charts dynamic representation links to InterPro website switch to bar chart view
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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
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Data analysis using selected EBI and external software tools
EBI Metagenomics pipeline Submission Philosophy Overview data analysis QC steps Overview of functional analysis Overview of taxonomy analysis Metagenome assembly Result outputs Others public pipelines Data analysis using selected EBI and external software tools
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Metagenomics data analysis
Quality control Quality control Pipeline 1 Taxonomy analysis Taxonomy analysis Pipeline 2 Functional analysis Functional analysis results 1 results 2 should share trends and main findings could differ in ratio and assignment
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Public Metagenomics portals
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Simplified overview of MG-RAST pipeline
Sequencer output Quality control Feature prediction (FragGeneScan) Abundance profiles Similarities search Blat Clustering (Uclust) Community reconstruction Metabolic reconstruction Metabolic model
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Example: Analysis of Prairie Soil Sample
MG-RAST and EBI Metagenomics QC comparison Example: Analysis of Prairie Soil Sample MG-RAST EBI Metagenomics Upload: bp Count 391,415,961 bp 391,415,961 bp Upload: Sequences Count 946,839 Upload: Mean Sequence Length 413 ± 125 bp bp Upload: Mean GC percent 61 ± 8 % 61.2 % Artificial Duplicate Reads: Sequence Count 0 Post QC: bp Count 388,670,692 bp Post QC: Sequences Count 908,602 Post QC: Mean Sequence Length bp Post QC: Mean GC percent 57.8 % Processed: Predicted Protein Features 972,409 999,433 Processed: Predicted rRNA Features 5 3 Alignment: Identified Protein Features 510,221 480,560 Alignment: Identified rRNA Features 1,069 1,110 Annotation: Identified Functional Categories 442,070 462,475
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Example: Analysis of Prairie Soil Sample
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 1 putative ammonia monooxygenase 3 Putative ammonia monooxygenase 5 Ammonia monooxygenase 1 ammonia monooxygenase family protein 2 ammonia monooxygenase subunit A 1 ammonia monooxygenase, putative 6 putative ammonia monooxygenase 2 Putative ammonia monooxygenase 1 putative ammonia monooxygenase subunit A 13 GenBank 9 SEED 12 Ammonia monooxygenase 2 ammonia monooxygenase family protein 4 Ammonia monooxygenase subunit A 5 Ammonia monooxygenase, putative 62 Putative ammonia monooxygenase 3 putative ammonia monooxygenase protein 4 putative ammonia monooxygenase subunit A 8 KEGG 18 eggNOG 13 GenBank 11 IMG 8 PATRIC 10 RefSeq 12 TrEMBL 9 SEED what do the abundance numbers mean ? EBI Metagenomics: 3 IPR Ammonia monooxygenase/particulate methane monooxygenase, subunit A 25 IPR Putative ammonia monooxygenase/protein AbrB
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MG-RAST and EBI Metagenomics Taxonomy analysis
Example: Analysis of Prairie Soil Sample MG-RAST domain level of taxonomy (55 categories) (15 categories) (98 categories) (3 types) EBI Metagenomics only Archae/Bacteria taxonomy (333 OTU)
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Overview of CAMERA workflow
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Integrated Microbial Genomes and Metagenomes analysis tools
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Some other Metagenomics tools
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Overview of MEGAN MEGAN QC ? Taxonomy analysis
rdp,biome files csv, tsv files Taxonomy analysis seq comparison and assignment Comparative visualisation abundance plots PCA, clustering, co-occurrence blast output SAM files csv, tsv files Functional analysis SEED KEGG COG/EGGNOG QC ?
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Example of taxonomy analysis using MEGAN
diverse single and multi-sample visualisations
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Example of taxonomy analysis using MEGAN
Comparison, PCA and co-occurrence plots
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Data analysis using selected EBI and external software tools
EBI Metagenomics pipeline Submission Philosophy Overview data analysis QC steps Overview of functional analysis Overview of taxonomy analysis Metagenome assembly Result outputs Others public pipelines Data analysis using selected EBI and external software tools
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