Comparative Genomics Background & Strategy. Faction 2

Transcription

1 Comparative Genomics Background & Strategy Faction 2

2 Overview Introduction to comparative genomics Salmonella enterica subsp. enterica serovar Heidelberg Comparative Genomics Faction 2 Objectives Genomic Elements Phylogenetic Analysis Whole Genome Visualizers Comparative Genomics Functional Implications Proposed Workflow

3 Introduction to comparative genomics Comparison of multiple genomes to discover similarities and differences: Gains, losses, and rearrangements of sequences Syntenic loci across organisms, relatedness to different species Factors affecting virulence, survivability, antimicrobial resistance, etc.

4 Goals Develop a typing method utilizing data from WGS capable of distinguishing isolates from the same and different outbreaks Find elements responsible for differences between the sporadic and outbreak groups

5 Salmonella enterica subsp. enterica serovar Heidelberg Salmonella enterica is a gram-negative, rod shaped bacteria that can infect many different hosts Facultative intracellular pathogen Divided into 6 subspecies (enterica (I), salamae (II), arizonae (IIIa), diarizonae (IIIb), houtenae (IV) and indica (VI)) Samonella enterica subsp. enterica has 2610 different serovars Serovars are characterized by two surface antigens: the flagellar H antigen and the oligosaccharide O antigen Antigenic Formula for serovar Heidelberg: 1,4,[5],12:r:1,2 7th most common serovar isolated from humans in the U.S This serovar has caused multiple outbreaks in U.S linked to poultry

6 Genomic Elements Genome Size ranging from 4.73 to 4.98 Mb GC% content of 52.1% Salmonella Pathogenicity Islands: Segments of DNA that encode for genes that are virulence factors and are required for the infection of the host Display higher AT content than rest of the genome Can play a role in virulence, host specification, and bacterial invasiveness from host immune system Antimicrobial resistance genes Possible elements for HGT Mobilome: Mobile genetic elements are important for bacterial adaptation and evolution, allowing the transfer of genes from different species in the same environment (i.e. Human gut) Plasmids Phages

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8 Phylogenetic Analysis Analysis to determine evolutionary history of individuals or groups of organisms Used to construct phylogenetic trees which infer the evolutionary ancestry of a set of individuals or of genes Global Based: Phylogenetic trees reveal the level of similarities between sequences in an alignment Whole-genome Alignment Marker Based: MLST / WG-MLST SNPs Pan/core genes

9 Phylogenetic Analysis Whole Genome Alignment: RAxML MASH MLST: Classic MLST + Whole genome MLST (wgmlst) SNP Analysis: CSI phylogeny

10 RAxML Randomized Axelerated Maximum Likelihood Program for phylogenetic analyses of large datasets using maximum likelihood Maximum Likelihood in terms of phylogenetic trees: Examines the space of all possible trees given a dataset Assigns a likelihood based on how likely the tree can reproduce the observed data Bootstrapping: tests the sampling error of the tree by running pseudoreplications of the original data Probability-like score Can handle whole-genome alignment on large datasets to generate whole-genome maximum likelihood phylogenetic trees

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22 snptree Leekitcharoenphon, Pimlapas et al (2012)

23 snptree.out of order

24 CSI Phylogeny (Call SNPs & Infer Phylogeny) Finds SNPs in the same manner as snptree Strict sorting of SNPs Requires all SNPs to be significant Z-score > 1.96 Z=(X -Y)/ (X+Y) X is the number of reads of the most common nucleotide at that position Y is the number of reads with any other nucleotide Kaas RS, Leekitcharoenphon P, Aarestrup FM, Lund O (2014)

25 Kaas RS, Leekitcharoenphon P, Aarestrup FM, Lund O (2014)

26 Whole Genome Visualization Mauve BRIG

27 BRIG: Blast Ring Image Generator Creates circular images of genomes Allows comparison of many genomes at once Utilizes Blast+ for genome alignment Allows addition of custom annotations With user-defined set of genes as input, BRIG can display: gene presence gene absence gene truncation sequence variation Works with set of complete genomes, draft genomes or raw, unassembled sequence data

28 Multi-alignment and visualization tool for large structure differences. Original Mauve algorithm Seeds -> Anchors -> LCBs(local collinear blocks) -> Gapped global alignment for each LCB Many problems(exp, parameters and thresholds) Progressive Mauve algorithm Pairwise genome distance -> Form guide tree and decide thresholds -> Alignment -> Refinement Consume more time and memory(too slow for over genomes)

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30 Comparative Genomics Pangenomics: Roary, BPGA, PGAP Genomic Elements Islands: (Pathogenicity Islands and Resistance Islands) with GIpsy Prophage: PHAST BRIG

31 Pan-Genome Analysis Pan Genome represents entire gene set of all strains of a species. (

32 Pan Genome Analysis Tools > 30 Tools Available Tools differ in: -Program Algorithm : Different approaches affect the sensitivity, accuracy, speed of the Tool -Output Features

33 Basic Algorithm Protein seq. Alignment Clustering Orthologous Genes absence of genes in individual strains Presence/ PanOCT and PGAP: require an all-against-all comparison using BLAST Roary, BPGA : Clusters relative prokaryotic isolates before BLAST

34 (Page et al. 2015)

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36 Genetic Islands Salmonella Pathogenicity Islands: Regions of the genome containing genes that encode for proteins required for virulence. Located both on the chromosome and plasmids Allow for intracellular colonization and pathogenicity Can be acquired through HGT (cross species) GIPSy: Genomic Island Prediction Software Version 1.1.2

37 GIPSy Software for the prediction of genomic islands into the classes: Predicts GEIs based on commonly the shared feature Pathogenicity islands, resistance islands, metabolic islands and symbiotic islands Genomic signature deviation (GC content and codon usages), presence of transposase genes, virulence and antibiotic genes, flanking trna genes, and cross-species genes. This is done in a 8 step process Perform on our sporadic isolates to analyze presence/absence of specific SPIs and compare to known outbreak isolates and known metadata

38 GIPSy Step 1: Load query genomes Step 2: GC Content Analysis Step 3: Codon usage Analysis Step 4: Search for transposase genes using HMMer Step 5: Search for specific factors (virulence, resistance) using blastp Step 6: Visualize amino acid content Step 7:tRNA prediction using HMMer results Step 8:Visualization of Pathogenicity Islands

39 Plasmids Analysis with Plasmid Profiler A plasmid comparative analysis pipeline. Steps Inputs: WGS short reads in FastQ format, plasmid sequence database, replicon sequence and gene of interest database. Use KAT to remove unrepresented plasmid sequences to create an individualized plasmid database per sample. Use SRST2 to identify putative plasmid hits from the individual databases. Use Blast to identify the incompatibility groups and genes of interest found in the sequences found at last step. Use its R package to visualize.

40 Plasmid Profiler

41 Prophage identification with PHAST Mobilome: prophages, plasmids, and other mobile genetic elements Horizontal gene transfer of mobile elements can increase microbial resistance Purpose - use prophage identification to differentiate between line list metadata PHAST (PHAge Search Tool): Accepts raw DNA sequence data or annotated GenBank formatted data Locates, annotates, and displays prophage sequences and features ~3 minutes for typical bacterial genome Zhou (2011)

42 Zhou (2011)

43 PHAST (2011) Arndt (2016) PHASTER (2016) > 4.3x faster analysis than PHAST > Able to handle multiple queries > improvement of genome visualization tools

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45 Pathway Analysis In-depth and contextualized findings to help understand the mechanisms of disease in question Identification of genes and proteins associated with the etiology of a specific disease Prediction of drug targets Understand how to intervene therapeutically in disease processes Data integration: integrate diverse biological information Functional discovery: assign function to genes Conduct targeted literature searches

46 Pathway Analysis Tools/Databases DAVID (david.abcc.ncifcrf.gov) KEGG ( Ingenuity Pathway Analysis ( GeneGo/MetaCore ( GenMAPP (www. genmapp.com) BioCyc ( Pubgene ( PANTHER (www. pantherdb.org)

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48 Proposed Workflow

49 References Leekitcharoenphon, P., Kaas, R. S., Thomsen, M. C. F., Friis, C., Rasmussen, S., & Aarestrup, F. M. (2012). snptree - a web-server to identify and construct SNP trees from whole genome sequence data. BMC Genomics, 13(Suppl 7), S6. Kaas RS, Leekitcharoenphon P, Aarestrup FM, Lund O (2014) Solving the Problem of Comparing Whole Bacterial Genomes across Different Sequencing Platforms. PLoS ONE 9(8): e doi: /journal.pone Ondov, Brian D, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biology (2016): DOI: /s x Maiden, Martin CJ, et al. MLST revisited: the gene-by-gene approach to bacterial genomics. Nat Rev Microbiol (2013): Maiden, Martin CJ, et al. "Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms." Proceedings of the National Academy of Sciences 95.6 (1998): Gupta, Anuj, et al. stringmlst: a fast k-mer based tool for multilocus sequence typing. Bioinformatics (2017) 33 (1): Liu, Yi-Yen, et al. Construction of a Pan-Genome Allele Database of Salmonella enterica Serovar Enteritidis for Molecular Subtyping and Disease Cluster Identification. Front. Microbiol (2016), doi: /fmicb Zhou, You, Yongjie Liang, Karlene H. Lynch, Jonathan J. Dennis, and David S. Wishart. "PHAST: A Fast Phage Search Tool." Nucleic Acids Research. Oxford University Press, 14 June Web. 28 Mar Arndt, David, Jason R. Grant, Ana Marcu, Tanvir Sajed, Allison Pon, Yongjie Liang, and David S. Wishart. "PHASTER: A Better, Faster Version of the PHAST Phage Search Tool." Nucleic Acids Research 44.W1 (2016): n. pag. Web. Zhulin IB. Databases for Microbiologists. Margolin WW, ed. Journal of Bacteriology. 2015;197(15): doi: /jb Viswanathan GA, Seto J, Patil S, Nudelman G, Sealfon SC (2008) Getting Started in Biological Pathway Construction and Analysis. PLoS Comput Biol 4(2): e16. doi: /journal.pcbi A. Stamatakis: "RAxML Version 8: A tool for Phylogenetic Analysis and Post-Analysis of Large Phylogenies". Bioinformatics, 2014

50 Thank You