SHAMAN : SHiny Application for Metagenomic ANalysis
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1 SHAMAN : SHiny Application for Metagenomic ANalysis Stevenn Volant, Amine Ghozlane Hub Bioinformatique et Biostatistique C3BI, USR 3756 IP CNRS Biomics CITECH
2 Ribosome ITS (1) : located between 18S and 5.8S rrna genes Image from Alberts Molecular Biology of the Cell 5th 2 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
3 Quantitative metagenomics pipeline 16S/18S/ITS Samples 1 Clustering Merging Mapping 3 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016 OTU OTU OTU N
4 HUB 16S/18S/ITS pipeline Sample A A A A A A Amplicon sequences Alientrimmer1 Filter Human PhiX reads FastQC Dereplication Read processing Chimera filtering Gold database Amplicon 100 samples 50 pairs 30 min OTU Taxonomic Annotation UNITE OTU building Clustering FLASH2 Findley Vsearch3 RDP Silva SSU 123 Greengenes 150 min 13,4 Mapping 26 min Phylogeny Mafft/Fastree Count matrix 1.Genomics, 102(5), Bioinformatics Vol.27 no , p Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
5 Uparse/Vsearch Integrated in QIIME, MOTHUR and LotuS Nature methods, 10(10), Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
6 Quantitative metagenomics pipeline 16S/18S/ITS Samples 1 Clustering Merging Mapping 6 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016 OTU OTU OTU N SHAMAN «être éclairé» (Toungouse - Sibérie)
7 SHAMAN : shaman.c3bi.pasteur.fr «There is no disputing the importance of statistical analysis in biological research, but too often it is considered only after an experiment is completed, when it may be too late.» 7 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
8 SHAMAN : shaman.c3bi.pasteur.fr Counts 8 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016 Annotation
9 SHAMAN : shaman.c3bi.pasteur.fr «There is no disputing the importance of statistical analysis in biological research, but too often it is considered only after an experiment is completed, when it may be too late.» 9 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
10 Metagenomic vs RNA-seq DESeq2 approach is usually used for RNA-seq dataset Metagenomic RNA-seq Distribution Overdispersed counts Negative binomial Overdispersed counts Negative binomial Constraints Highly abundant species Highly expressed genes Goal Find differentially abundant Find differentially expressed genes: features (species, familly, ): Distributions and expression vary OTU distributions and abundances between conditions vary between conditions Metagenomic data are similar to RNA-seq data 10 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
11 Data normalization Why? To correct technical biaises and make samples comparables. How? Fitting the distributions (Total Read Count, UpperQuartile, Median, Full Quantile) Account for the feature length (RPKM) Concept of «effective reads number» (TMM, DESeq2) Remarks? Some methods normalize the counts, others the library sizes Some are designed for differential analysis 11 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
12 DESeq2 normalization (OTU level) Assumption Most of the OTU have the «same» abundance between 2 conditions Normalization factor : where Xij : Number of mapped reads of the OTU i in sample j n: Number of samples 12 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
13 Comparison with RPKM (1/3) RPKM : Reads Per Kilobase per Million mapped reads Assumption Counts are proportional to abundance, the length and the sequencing deepth. Method per Million Normalized counts = Number of reads of sample j Length 13 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016 Per Kilobase
14 Comparison with RPKM (2/3) Comparison of 7 normalization methods 14 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
15 Comparison with RPKM (3/3) Results on real data (7 samples) FDR and Power Dillies M. et al., Bioinformatics Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
16 To sum up DESeq2 normalization provides better results Recommend using DESeq2 to perform analysis of differential abundance 16 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
17 Statistical model of DESeq2 Generalized Linear Model Moyenne Dispersion Size factor Log2 fold change Advantages Allows complex experimental designs. 17 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
18 Dispersion estimation Problem Get a good estimate of the dispersion with a small number of samples. Modelisation of the dispersion : Function of the mean of normalized count Local parametric regression 18 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
19 Contrasts (comparisons) Aim Testing a specific effect without having to re-fit the model. Contrast vector Coefficients Covariance matrix Advantages Parameters are estimated with all samples. 19 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
20 Conclusions SHAMAN 16s/18s/its analysis Strong statistical approach Several visualizations available Access : Incoming features WGS analysis New visualizations (Taxonomy plot, Krona, continuous data) Compatibility with FROGS
21 CIB FROGS 16S/18S GALAXY Pasteur Vsearch swarm Galaxy team : Mathieu Valade, Fabien Mareuil Emmanuel Quevillon, Eric Deveaud 21 Stevenn Volant, Amine Ghozlane SHAMAN : Shiny Application for Metagenomic ANalysis 29/01/2016
22 Acknowledgements C3BI Bioinformatics Biostatistics Hub Olivier GASCUEL Marie-Agnès DILLIES Christophe MALABAT Hugo VARET Nicolas MAILLET Pierre LECHAT Anna ZHUKOVA Rachel TORCHET CiTech Biomics Pole Sean KENNEDY Béatrice REGNAULT
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