Integrating phylogenetic inference and metadata visualization for NGS data
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This document provides an overview of phylogenetic inference and metadata visualization for next-generation sequencing data. It discusses various software for phylogenetic tree construction based on sequence alignments. It also describes different sequence-based typing methods like multilocus sequence typing and single nucleotide polymorphism typing that can be applied to NGS data. Finally, it introduces the PhyloViz software for integrating phylogenetic analysis using the goeBURST algorithm with metadata visualization.
Integrating phylogenetic inference and metadata visualization for NGS data
- 1. João André Carriço, PhD Microbiology Institute/Institute for Molecular Medicine Faculty of Medicine, University of Lisbon Portugal Integrating phylogenetic inference and metadata visualization for NGS data http://im.fm.ul.pt http://imm.fm.ul.pt http://www.joaocarrico.info Workshop 20: Typing of Bacterial Pathogens in 2015: Expanding the scope of NGS
- 2. Conflicts of Interest NOTHING TO DISCLOSE
- 3. Charles Darwin ‘s “tree of life” in Notebook B, 1837-1838 Darwin and the tree of life
- 4. Phylogenetics methods aim to infer the relationships between the taxa trying to define the common ancestors between taxa Assumptions: the characters being compared are homologous and independent, i.e. they had shared a common ancestor and each character suffered evolutive forces individually Phylogenetic Inference ATTGGGG ATGGGGG AT?GGGG
- 5. Software for Phylogenetic trees: based on sequence alignments• MEGA • http://www.megasoftware.net/ • Splitstree • http://www.splitstree.org/ • Geneious (http://www.geneious.com/) • www.geneious.com • FastTree • http://www.microbesonline.org/fasttree • RAxML • http://sco.h- its.org/exelixis/web/software/raxml/index.html • PHYLIP • http://evolution.genetics.washington.edu/phylip.ht ml • BEAST • http://beast.bio.ed.ac.uk/ And many many others…
- 6. Sequence Alignment methods Kos, V.N. et al., 2012. Comparative genomics of vancomycin-resistant Staphylococcus aureus strains and their positions within the clade most commonly associated with Methicillin-resistant S. aureus hospital-acquired infection in the United States. mBio, 3(3). Maximum Likelihood tree of concatenated SICOs
- 7. Sequence Alignment methods Maximum Likelihood tree of concatenated SICOs Caveats: • Computationally intensive: some methods can’t be applied to hundreds to thousands of strains • Require specialized method and software knowledge for parameter definition • Some phenomena violate the assumptions (recombination, convergent evolution,etc)
- 8. Sequence Based Typing Methodsx Strain genomic information encoded as a numeric sequence Sanger sequencing: MLST: Gene allele identifier MLVA: Number of repeats NGS approaches: Gene-by-Gene / allele based: wgMLST: core + pan genome genes are represented cgMLST: just core genome SNP Typing : Polymorphism
- 9. To each unique gene sequence (allele) is attributed an integer ID, by comparison with online DBs Allelic profile: 12 - 9 - 11 - 7 - 11 - 20 - 3 Each allelic profile, aka ST, is unequivocally identified by an integer. Single locus variant (SLV): Double locus variant (DLV): Triple locus variant (TLV): 12 12 10 - 10 - 10 - 10 - 11 - 11 - 11 - 7 - 11 - 11 - 11 - 11 - 11 - 20 - 20 - 2 - 3 - 3 - 3 Bacterial chromosome MLST
- 10. SNP NGS Approach Good approach in Monomorphic species. For non-monomorphic species , SNPs in genome areas where recombination was detected need to be removed to avoid confounding the phylogenetic signal. sample NGS WGS reads Mapping to reference Fasta File with SNPs fastq files BAM files VCF files
- 11. Gene by Gene NGS Approach Software currently available: BIGSDB (Jolley, K.A. & Maiden, M.C.J., 2010. BIGSdb: Scalable analysis of bacterial genome variation at the population level. BMC Bioinformatics) RIDOM™ SEQSPHERE+ (http://www.ridom.com/seqsphere/) Central nomenclature server: Schemas, Allele definitions and identifiers sample NGS WGS reads assembly contigs Output :Allelic Profile
- 12. Algorithms for Phylogenetic Inference Based on the distance matrix: •Hierarchical clustering methods: UPGMA, Single Linkage and Complete linkage •Neighbor-joining •Minimum Spanning Trees Maximum Parsimony methods Based on rules (Graphic Matroids) •goeBURST Maximum Likelihood methods Bayesian inference methods Sequence alignments Sequence alignments Sequence alignments Sequence alignments Allelic Profiles Allelic Profiles
- 13. Infering phylogeny from allelic profiles Assume that you have only 3 genes and each number corresponds to a different allele for each gene. The minimum assumption is assuming that a SLV may correspond to a possible phylogenetic descent. 1-1-1 1-1-2 1-2-1 1-2-2 1-2-3 SLV SLV SLV SLV SLV SLV 11 possible trees….
- 14. eBURST model More similar STs should denote closely related strains from an evolutionary point of view. STs with more SLVs can be regarded has a common ancestor. Links between STs depict descent relations. With these assumptions, connected STs should share an evolutionary path. Maynard Smith J., et al. 2000. Bioessays 22:1115- eBURST Feil E. et al, J Bac 2004
- 15. 1-1-1 1-1-2 1-2-1 1-2-2 1-2-3 goeBURST #SLVs #DLVs #TLVs Freq STid 2 2 0 1 1 2 2 0 1 2 3 1 0 1 3 3 1 0 1 4 2 2 0 1 5 Implementation of the eBURST rules as a graphic matroid problem, allows for a globally optimal solution of the placement of the ST links. Francisco et al, BMC Bioinf, 2009 More SLVs / lower ID Connects to ST4 because #SLVs Final goeBURST tree : unique solution guaranteed
- 16. Applying goeBURST 1-1-1 1-1-2 1-2-1 1-2-2 1-2-3 SLV SLV SLV SLV SLV SLV 11 possible trees…. All these are valid goeBURST solutions. The tie break would need to be the ST ID if all of them would have the same frequency in the dataset
- 17. goeBURST output examples Largest S. aureus MLST CC 1067 of 2650 STs total 2nd largest S. aureus CC 252 Sts
- 18. goeBURST FULL MST • The goeBURST rules can be expanded to any number of loci while maintaining the same assumptions of the evolutionary model behind • Adds an evolutionary model to the basic Minnimum Spanning Tree approach • Advantage: very fast to calculate compared to phylogenetic analysis algorithms • Advantage: If the strains are closely related we have the internal nodes defined as strains as opposed to any traditional phylogenetic methodology • Disadvantage: does not create internal nodes as putative recent common ancestral
- 19. Allelic profiles Accessory data (“metadata”) Antibiogram Serotype Origin info (patient) …. Analysis (goeBURST) Other typing method Present the data in a meaningful way Integrating Data Analysis and Visualization
- 21. PHYLOViZ Can be easily applied to: -MLST -MLVA -SNP data* -Gene Presence/absence *Conversion of VCF to PHYLOViZ: https://github.com/nickloman/misc-genomics-tools/blob/master/scripts/vcf2phyloviz.py (Thanks Nick!)
- 22. PHYLOViZ Example of visualization with MLST+ (core genome) data of VRSA and MRSA strains
- 23. Core genome comparison - Workflow Core genome from all available fully sequenced S.aureus Strains in NCBI Using strain COL genes as reference 1866 target loci found for a cgMLST schema (RIDOM Seqsphere+) Call alleles for strains under study Removing loci with missing data in the strains under analysis 1542 target genes kept for whole genome comparison goeBURST Minimum Spanning Tree of the resulting allelic profiles (PHYLOViZ software)
- 24. Core genome comparison VRSA NCBI strains US VRSA strains (Kos et al) HSM strains MRSA srp VRS5 MLST+: 1542 genes Core genome genes found in all strains 65
- 26. PHYLOViZ PROs: Handles thousands of profiles Fast calculation Easy to annotate and explore metadata Allows for basic statistics on profiles and metadata Allows for advanced statistics on MSTs (PLoS One. 2015 Mar 23;10(3):e0119315) Exports high quality graphical formats Allows plugin development CONs: goeBURST and goeBURST MST only (Neighbour Joining and UPGMA soon) JAVA knowledge to code new plugins
- 27. Final Remarks Phylogenetic inference has always an underlying model. The choice of method depends on what data is being analyzed and the underlying question With the increasing availability of bacterial genomes, the methods that allow their comparison need to be efficient and scalable Metadata should always be use to evaluate the algorithm results PHYLOViZ provides a visualization framework to analyze inferred patterns of descent based on goeBURST , including detailed statistics and allows easy integration of metadata on algorithm results Any sequence-based typing method that generates allelic profiles can be analyzed by this framework, including any NGS derived schema (ie cgMLST, SNPs)
- 28. Ongoing Phyloviz work Modular plugin architecture Allows expansion and addition of new capabilities Other analysis algorithms/ custom rules New visualization modules Allow the analysis of other data types Complementary statistics modules Try to address user’s needs… We need your feedback! Phyloviz is open-source freeware software
- 29. Alexandre Francisco Cátia Vaz Pedro Monteiro Mário Ramirez José Melo-Cristino Acknowledgements Initial funding from Fundação para a Ciência e Tecnologia
- 30. Draft Scientific Programme: Plenaries: 1)Small Scale Microbial Epidemiology 2)Large Scale Microbial Epidemiology 3)Bioinformatics for Genome-based Microbial Epidemiology 4)Population Genetics: Pathogen Emergence 5)Population Dynamics : Transmission networks and surveillance 6)Molecular Epidemiology for Global Health and One Health Parallel Sessions 1)Food and Environmental pathogens 2)Microbial Forensics 3)Virus 4)Fungi and Yeasts 5)Novel Diagnostics methodologies 6)Novel Typing approaches 7)Phylogenetic Inference 8)Interactive Illustration Platforms Save thedate !