Genômica comparativa. João Carlos Setubal IQ-USP outubro /5/2012 J. C. Setubal

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1 Genômica comparativa João Carlos Setubal IQ-USP outubro /5/2012 J. C. Setubal 1

2 Comparative genomics There are currently (out/2012) 2,230 completed sequenced microbial genomes publicly available Many are of closely related species Why compare? How to do it? 2

3 Why comparative genomics? To understand the genomic basis of the present Differences in lifestyle pathogen vs. nonpathogen Obligate vs. free-living Host specificity animals vs. plants, plant X vs. plant Y, etc In the case of pathogens: this understanding should help us in fighting disease To understand the past How organisms evolved to be what they are 3

4 Citrus canker Xanthomonas axonopodis pathovar citri 4

5 Black rot: Xanthomonas campestris pathovar campestris 5

6 What is comparative genomics Assuming input is the sequence and its annotation There are many ways that genomes can be compared Different resolutions Whole genome Genome alignments Synteny (gene order conservation) Anomalous regions Gene-centric Gene families and unique genes Gene clustering by function Gene sequence variations Codon usage, SNPs, indels, pseudogenes 6

7 Resolution Low resolution Scope: entire genomes Example event: rearrangement High resolution Scope: nucleotide sequences Example event: single mutation 7

8 Genome-wide evolutionary events Replicon rearrangements Gene/region duplication Gene/region loss Chromosome plasmid DNA exchange Lateral transfer 8

9 Whole replicon alignments: the pairwise case If the sequences were identical we would see B A 9

10 an inversion A B C D A D C B 10

11 D B C A A B C D Such inversions seem to happen around 5 November 2012 the origin or terminus JC Setubal of replication 11

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16 E. coli K12 Promer alignment Red: direct; green: reverse Xanthomonas axonopodis pv citri Both are γ proteobacteria! 16

18 Eisen JA, Heidelberg JF, White O, Salzberg SL. Evidence for JC symmetric Setubal chromosomal inversions around the replication origin in bacteria. Genome Biol. 2000;1(6):RESEARCH November

19 Replicon sequence comparisons Basic tool: MUMmer Delcher AL, Phillippy A, Carlton J, Salzberg SL. Fast algorithms for large-scale genome alignment and comparison. Nucleic Acids Res Jun 1;30(11): Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, Salzberg SL. Versatile and open software for comparing large genomes. Genome Biol. 2004;5(2):R

20 Basics of MUMmer It finds Maximal Unique Matches These are exact matches above a user-specified threshold that are unique Exact matches found are clustered and extended (using dynamic programming) Result is approximate matches Data structure for exact match finding: suffix tree Difficult to build but very fast Nucmer and promer Both very fast O(n + #MUMs), n = genome lengths 20

21 Whole replicon multiple alignment The program MAUVE Darling AC, Mau B, Blattner FR, Perna NT. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res Jul;14(7):

22 Main chromosome alignment MAUVE 22

23 Chromosome 2 alignment MAUVE 23

24 Chromosome alignment MAUVE Dugway RSA 493 RSA

25 Genome Alignments MAUVE 25

26 How MAUVE works Seed-and-extend hashing Seeds/anchors: Maximal Multiple Unique Matches of minimum length k Result: Local collinear blocks (LCBs) O(G 2 n + Gn log Gn), G = # genomes, n = average genome length 26

27 Alignment algorithm 1. Find Multi-MUMs 2. Use the multi-mums to calculate a phylogenetic guide tree 3. Find LCBs (subset of multi-mums; filter out spurious matches; requires minimum weight) 4. Recursive anchoring to identify additional anchors (extension of LCBs) 5. Progressive alignment (CLUSTALW) using guide tree 27

28 Gene-centric comparisons Homologs: genes that have the same ancestor; in general retain the same function Orthologs: homologs from different species (arise from speciation) Paralogs: homologs from the same species (arise from duplication) Duplication before speciation (ancient duplication) Out-paralogs; may not have the same function Duplication after speciation (recent duplication) In-paralogs; likely to have the same function 28

29 Gene Set Computations Given a set of genomes, represented by their proteomes or sets of protein sequences Given homlogous relationships (as given for example by orthomcl) Which genes are shared by genomes X and Y? Which genes are unique to genome Z? Venn or extended Venn diagrams 29

30 3-way genome comparison A B C 30

34 Fig. 4. Net gene loss or gain throughout the evolution of the {alpha}-proteobacterial species Boussau, Bastien et al. (2004) Proc. Natl. Acad. Sci. USA 101, Copyright 2004 by the National Academy of Sciences

35 Proteome alignment done with LCS (top: Xcc; bottom: Xac ) Blue: BBHs that are in the LCS; dark blue: BBHs not in the LCS; red: Xac specifics; yellow: Xcc specifics 35

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39 What do the tables show conserved blocks (aka microsyntenic regions ), and how these blocks appear in different replicons across the genomes compared some of these blocks are not operons (would need to show strand) possible block losses 39

42 Polymorphism detection indels, SNPs pseudogenes 42

43 Figure 4. I II

44 Gluconate isomerase A Brucella gene in the process a decay 44

I519 Introduction to Bioinformatics, Genome Comparison. Yuzhen Ye School of Informatics & Computing, IUB

I519 Introduction to Bioinformatics, 2015 Genome Comparison Yuzhen Ye (yye@indiana.edu) School of Informatics & Computing, IUB Whole genome comparison/alignment Build better phylogenies Identify polymorphism