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Toolbox for bacterial population analysis using NGS

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A short lecture where I collected tools for population analysis. "Current Challenges in Next generation Sequencing 7-11.12" University of Helsinki

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Toolbox for bacterial population analysis using NGS

  2. 2. I’m a vet and not a bioinformatics.. I’m a good example of end-user! I do not want to teach population genetics today … just give you some tips how to do it using NGS in bacteria If you are interested in bacterial population analysis … we are organizing an ad hoc course in Spring .. There are several more software/pipelines.. These are the ones I like/I know/I apply If you want the slides send an Email to me If you are a MSc in bioinformatics and interested in thesis in applied bioinformatics in public health microbiology and pathogen surveillance please contact me ..
  3. 3. Bacterial population A group of individuals of the same species POPULATIONS, not individuals, evolve Population and community are two different concepts … WE ARE SPEAKING OF INDIVIDUALS OF THE SAME SPECIES!!!! … although the definition of species in bacteriology is quite vague Population genomics attend to understand the population by whole genome analysis a sample of it  investigating the variation of a subset of individual members of the population “Sequence data is ideal for this, as the differences between individuals are often tiny (i.e. there is very little variation) since they belong to a single population, and DNA sequence data allows us to detect single nucleotide changes (ie provides high resolution)” (Kate Hold)
  4. 4. The sample is a subset of the population 4 Population Universe Reality State of nature Truth parameters Sample Finite, random noise error perturbation statistics Statistical inference: Extract maximum information from sample in order to draw conclusions about population Inductive not deductive Source John Bunge
  5. 5. How many samples do I need to sequence? It depends on your question! Accuracy is important.. but big numbers help! Draft genomes are enough. Closing a genome is a waste of time and money! good draft 100 €/s  closed > 3000 €/s Include in your analysis as much diversity as possible (time, space, phenotypes,...) Sequence as much as you can … just stop before you get broke!! 1000 strains < 100 000 €
  6. 6. Bacterial population… different levels Population of H. pylori living in a single stomach Population of H. pylori circulating globally
  7. 7. What do we want to measure? Genetic Drift ◦ the change in the gene pool of a small population due to chance Natural Selection ◦ Allele increasing fitness will accumulate in the population ◦ Cause ADAPTATION of Populations Gene Flow ◦ is genetic exchange due to the migration of individuals between populations
  8. 8. How do we measure (using NGS)? Identify variants: ◦SNPapproach ◦Gene-by-geneapproach Define which part of the gene pool is common in all the individuals of the population (core) and which part is not (accessory) Use of phylogenetic frameworks for reconstructing genealogy and non-phylogenetic clustering methods for inferring population structure
  9. 9. Applications Outbreak determination Pathogen transmission Understanding epidemics Pathogen surveillance Understanding evolution of bacteria …. @jennifergardy
  10. 10. Identifying variants: SNP approaches sample NGS WGS reads Mapping to reference VCF/Fasta File with SNPs • Needs a reference strain • Monomorphic (Clonal) species • Recombination/Horizontal gene transfer is a problem • Difficult to create a nomenclature Source J. Carriço
  11. 11. Identifying variants: Gene-by-gene sample NGS WGS reads • No need for reference strain • Buffers recombination effect • Simpler to create a nomenclature • Population structure of non-monomorphic species • Multiple Schemas can be defined for a single species assembly contigs Central nomenclature server: Schemas, Allele definitions and identifiers Output :Allelic Profile Source J. Carriço
  12. 12. Sequence platforms Loman et al., 2012 Nature Review Microbiology
  13. 13. … I’m just using Illumina For both de novo and re-sequencing At the moment Illumina gives the best benefit-cost ratio: • High throughput • Accuracy • Possibility for multiplex • Reasonable work flow time • Easy accessible For small genomes (1 to 2 Mb) it is nowadays possible to sequence at ~90 euro/sample with minimum x40 coverage
  14. 14. I have the reads for each strain.. OK, and now? An overview of main programs, platforms and approaches … sometime it is a question of style!
  15. 15. I want some results from reads… You can always map your reads against a close reference genome using ”classical” short reads aligners and extract SNPs: BWA for example Here just a (long) list Now you just need to decide the reference genome Note that you might need to select more than one reference genome to tune your analysis …Be aware that there are available software designed specifically for bacterial genomes
  16. 16. Assembly-free analyses SNP CALLING AND CORE GENOME ALIGNMENTS - REFERENCE BASED MAPPING Snippy ◦ One-by-one ◦ a set results using the same reference to generate a core SNP alignment ◦ A lot of output files ◦ Variants: SNPs, MNPs, INDELs, MIX Input Requirements ◦ a reference genome in FASTA or GENBANK format (can be in multiple contigs) ◦ query sequence read files in FASTQ or FASTA format (can be .gz compressed) format Wombac ◦ Fast and “dirty”´; several samples in a run ◦ Computations can re-used for building new trees ◦ looks for substitution SNPs, not indels, and it may miss some SNPs Input Requirements ◦ a reference genome in FASTA or GENBANK format (can be in multiple contigs) ◦ query sequences in ◦ a folder containing FASTQ short reads: eg. R1.fq.fz R2.fq.gz ◦ a multi-FASTA file: eg. contigs.fa or NC_273461.fna ◦ a .tar.gz file containing FASTA contig files: eg. Ecoli_K12mut.contig.tar.gz (from EBI/NCBI) @torstenseemann
  17. 17. Assembly-free analyses SHORT READ SEQUENCE TYPING Srst2 ◦ design specifically for bacterial genomes ◦ Query Illumina sequence data, against an MLST database and/or a database of gene sequences ◦ Report the presence of STs (allele designation) and/or reference genes Input Requirements ◦ Query: illumina reads (fastq.gz format, but other options) ◦ A fasta reference sequence database to match to: ◦ For MLST, this means a fasta file of all allele sequences. If you want to assign STs, you also need a tab-delim file which defines the ST profiles as a combination of alleles. ◦ For resistance/virulence genes, this means a fasta file of all the resistance genes/alleles that you want to screen for, clustered into gene groups. @DrKatHolt
  18. 18. Stand-alone pipeline for SNP variant Nullarbor ◦ Clean reads ◦ Species identification  k-mer analysis against known genome database (Kraken) ◦ De novo assembly ◦ Annotation ◦ MLST ◦ Resistome ◦ SNP Variants @torstenseemann
  19. 19. … or you might prefer assemble your genome! When you know little or nothing of your dataset (it is not possible to select a reference genome) In case of deep comparative genomics when you also are interest in the accessory genome (genes absence in your reference) To extract the pangenome Because having all your dataset assembled will facilitate downstream applications To develop common NOMENCLATURE
  20. 20. The never ending nomenclature story… Source J. Carriço
  21. 21. Assembly short reads REFERENCE BASED ASSEMBLY Mira (best assembler … for geeks since 1999 ) ◦ multi-pass assembler/mapper for small genomes (up to 150 Mb) ◦ has full overview on the whole project at any time of the assembly, using all available data and learning from mistakes ◦ Marks places of interest with tags so that these can be found quickly in finishing programs ◦ can do also de novo and hybrid assembling Input Requirements ◦ various formats (CAF, FASTA, FASTQ or PHD) from Sanger, 454, Ion Torrent, illumina DE NOVO ASSEMBLY Spades (a very good assembler for lazy people) ◦ is intended for both standard isolates and single- cell MDA bacteria assemblies ◦ It does its work and very well ◦ Simple to run --careful -1 R1.fastq.gz -2 R2.fastq.gz –o output folder ◦ Can use Nanopore and PacBio for hydrid assembly Andrey’s lecture from WBG2014 duTAQjcDsBHTkWQ/edit#slide=id.g47b5b1626_0793 @BaCh_mira
  22. 22. Pangenome alignment (up to 50 strains) MUGSY Genomes should be very similar Mugsy (also Mauve) alignment generated a multiple block local alignment Alignment format is in MAF MAUVE Large-scale evolutionary events It can align more divergent strains than Mugsy: as little as 50% nucleotide identity It aligns the pan-genome Complete genome alignment in the eXtended Multi-FastA (XMFA) List groups of genes that are predicted to be positionally orthologous GUI available
  23. 23. Core genome alignment PARSNP Designed to align the core genome of hundreds to thousands of bacterial genomes within a few minutes to few hours Very very similar strains… it use MUMi to select the nearest genomes only the ones with distance <= 0.01 are included, all others are discarded. Input can be both draft assemblies and finished genomes, and output includes variant (SNP) calls, core genome phylogeny ad multi-alignments Results are visualized using a GUI
  24. 24. Gene-by-gene: pangenome, coregenome, accessory genome assembly Structural annotation Ortholog clustering Prodigal Prokka RAST OrthAgogue Roary
  25. 25. Structural annotation PRODIGAL Gene finders Very fast  3000 genomes in ~ a week (8 cpu 16 Gb RAM) Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own. PROKKA Structural and functional annotation Fast automatic annotation  in multi-core < 15 min Several dependencies  tedious to install (… I told you I’m very lazy!) rokka-rapid-bacterial-genome-annotation- abphm-2013?related=1
  26. 26. Ortholog clustering ORTHAGOGUE high speed estimation of homology relations within and between species in massive data sets easy to use and offers flexibility through a r Input = all-against-all BLAST tabular output; range of optional parameters Output = mcl file -u -o XX  ignore e-value, use BLAST score, esclude protein with overlap < XX ROARY high speed stand alone pan genome pipeline 128 samples can be analysed in under 1 hour using 1 GB of RAM and a single processor Input = GFF3 format produced by Prokka Roary –e –mafft *.gff FastTree –nt –gtr core_gene_alignment.aln > my_tree.newick Output = several files
  27. 27. Gene-by-gene: pangenome, coregenome, accessory genome Ortholog clustering results ad hoc scripts Core Genome Accessory Genome Pangenome Phylogeny RAxML Fastree BEAST Everything included in Roary but not in OrthAgogue Population structure BAPS STRUCTURE Recombination BRATNEXTGEN GUBBINS
  28. 28. cgMLST and wgMLST Strain 1 Strain 2 Strain 3 Strain 4 Strain 5 Strain 6 L1 L2L2 L3L4 L5 L6L7 L8 L9 Core Genome -> cgMLST Accessory genome Core Genome+ Accessory Genome = PanGenome -> wgMLST Source J. Carriço @jacarrico
  29. 29. cgMLST and wgMLST Open source BACTERIAL ISOLATE GENOME SEQUENCE DATABASE ◦ Jolley & Maiden 2010, BMC Bioinformatics 11:595 - ◦ PROs: Freely available, open-source, handles thousands of genomes, has several schemas implemented for MLSTfor several bacterial species, and some extended MLST and core genome MLST (mainly Neisseria sp. but soon to be expanded) ◦ CONs: Requires Perl knowledge to install and maintain Source J. Carriço @jacarrico
  30. 30. cgMLST and wgMLST Commercial software RIDOM SEQSPHERE+ ◦ ◦ with client server solutions from assembly to allele calling and visualization for core genome MLST (MLST+/ cgMLST) APPLIED MATHS - BIONUMERICS 7.5 ◦ ◦ Commercial software with client server solutions from assembly to allele calling and visualization for whole genome MLST (wgMLST) Source J. Carriço @jacarrico
  31. 31. cgMLST with Genome Profiler Index alleles of the loci that shared by the bacterial isolates implementing both BLASTN and BLASTX Transforms WGS data into allele profile data Using a reference genome  it attempted to account for gene paralogy using conserved gene neighborhoods
  32. 32. cgMLST with Genome Profiler Input files ◦ reference genome in gbk format (even in multi-gbk format from RAST) or a multi-FASTA file the allele sequences ◦ Query genomes in FASTA format (complete or draft – in contigs) If you run the data for the first time, you use one of the genome as reference to built a new cgMLST scheme (ad hoc mode): ◦ perl -r NC_017282.gbk -g genome_list.txt Data can be run with the cgMLST scheme created previously by GeP: ◦ perl -g genome_list.txt –o Or you could use a multi-Fasta file of the the allele sequences (nt) as reference (in this case all possible paralogs are excluded - a fix number of 999999999 will be assigned to expect-d) ◦ perl -r NC_017282.ffn -g genome_list.txt -n
  33. 33. cgMLST with Genome Profiler Output files: ◦ output.txt  records the information of all the loci in each of the test genome sequences ◦ difference_matrix.html  contains a summary of the analysis and a matrix of pairwise differences between the allelic profiles of the samples. ◦ Splitstree.nex  allele profile of the isolates in NEXUS format, which can be opened in Splitstree 4 ◦ allele_profile.txt  matrix of allele profile (input file of STRUCTURE and BAPS) ◦ core_genomes.fas  alignment of the core genome in FASTA format
  34. 34. Infering recombination events GUBBINS Iteratively identifies loci containing elevated densities of base substitutions while concurrently constructing a phylogeny based on the putative point mutations outside of these regions Run in only a few hours on alignments of hundreds of bacterial genome sequences. BRATNEXTGEN Bayesian analysis of recombinations in whole- genome DNA sequence data Use a GUI Divides the genome into segments, then for each segment, detects genetically distinct clusters of isolates and estimates the probabilities of recombination events Run efficiently on a desktop computer .. I tested up to 100 .. Results after O/N
  35. 35. Phylogeny (phylogeography) visualization A directory for tree visualization My favorite tree editor/viewer A very nice tool for phylogeography