Comparative genomics: Overview & Tools + MUMmer algorithm

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1 Comparative genomics: Overview & Tools + MUMmer algorithm Urmila Kulkarni-Kale Bioinformatics Centre University of Pune, Pune urmila@bioinfo.ernet.in

2 Genome sequence: Fact file 1995: The first complete genome sequence of Haemophilus infuenzae Rd-was published Biological systems are dynamic and evolving The forth dimension: Time Genome sequence is a snapshot of evolution Correlation between Phenotypic properties and Genomic region is not straightforward as phenotypic properties are result of many to many interactions 2

3 Genomes: the current status Published complete genomes: 403 Ongoing:» Archaeal: 81» Bacterial: 1226» Eukaryal: 169» Archaeal: 107» Prokaryotic: 3478» Eukaryotic: 1209 GOLD database Metagenomics:203 As of Viral: >4500 3

4 Genome databases Genomes at NCBI, EBI, TIGR 4

5 H. influenzae Complete Genome 5

6 Function information clock of E. coli Generated on March 2K4 6

7 Comparison of the coding regions Begins with the gene identification algorithm: infer what portions of the genomic sequence actively code for genes. There are four basic approaches. 7

8 Knowledge of Full Genome sequence: Solutions or new questions? Correct # of genes? Still struggling with the gene counters 8

9 Genome analyses Variation in Genome size GC content Codon usage Amino acid composition Genome organisation Single circular chromosomes E. coli: 4.6Mbp M. pneumoniae: 0.81Mbp B. subtilis: 4.20Mbp B. burgdorferi: 29% M. tuberculosis: 68% G, A, P, R: GC rich I, F, Y, M, D: AT rich Linear chromosome + extra chromosomal elements 9

10 CG: Comparisons between genomes The stains of the same species The closely related species The distantly related species List of Orthologs Evolution of individual genes Evolution of organisms 10

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12 CG helps to ask some interesting questions Identification similarities/differences between genomes may allow us to understand : How 2 organisms evolved? Why certain bacteria cause diseases while others do not? Identification and prioritization of drug targets 12

13 CG: Unit of comparison Unit of comparison: Gene/Genome Number Content (sequence) Location (map position) Gene Order Gene Cluster (Genes that are part of a known metabolic pathway, are found to exist as a group) Colinearity of gene order is referred as synteny A conserved group of genes in the same order in two genomes as a syntenic groups or syntenic clusters Translocation: movement of genomic part from one position to another 13

14 Dandekar et al., 1998 Structure of tryptophan Numbers: Gene operon number Arrows: Direction of transcription //: Dispersion of operon by 50 genes Domain fusion trpd and trpg trpf and trpc trpb and trpa genetically linked separate genes 14

15 Important observations with regard to Gene Order Order is highly conserved in closely related species but gets changed by rearrangements With more evolutionary distance, no correspondence between the gene order of orthologous genes Group of genes having similar biochemical function tend to remain localized Genes required for synthesis of tryptophan (trp genes) in E. coli and other prokaryotes 15

16 Synteny Refers to regions of two genomes that show considerable similarity in terms of sequence and conservation of the order of genes likely to be related by common descent. 16

17 COGs: Phylogenetic classification of proteins encoded in complete genomes 17

18 Genome Pairwise genome comparison of protein homologs (symmetrical best hits) 18

19 Integr8: CG site at EBI 19

20 Comparative Genomics Tools BLAST2 MUMmer PipMaker AVID/VISTA Comparisons and analyses at both Nucleic acid and protein level 20

21 BLAST2 Available at NCBI Input: GI or FASTA sequence (range can be specified) Output: Graphical Alignment of 2 genomes 21

22 Genome Alignment Algorithm: MUMmer Developed by Dr. Steven Salzberg s group at TIGR NAR (1999) 27: NAR (2002) 30: Availability Free TIGR site 22

23 Features of MUMmer The algorithm assumes that sequences are closely related Can quickly compare millions of bases Outputs: Base to base alignment Highlights the exact matches and differences in the genomes Locates SNPs Large inserts Significant repeats Tandem repeats and reversals 23

24 Definitions are drawn from biology SNP: Single mutation surrounded by two matching regions Regions of DNA where 2 sequences have diverged by more than one SNP Large inserts: regions inserted into one of the genomes Sequence reversals, lateral gene transfer Repeats: the form of duplication that has occurred in either genome. Tandem repeats: regions of repeated DNA in immediate succession but with different copy number in different genomes. A repeat can occur 2.5 times 24

25 Techniques used in the MUMmer Algorithm Compute Suffix trees for every genome Longest Increasing Subsequence (LIS) Alignment using Smith & Waterman algorithm Integration of these techniques for genome alignment 25

26 MUMmer: Steps in the alignment process Read two genomes Perform Maximum Unique Match (MUM) of genomes Using SNPs, mutation regions, repeats, tandem repeats Close the gaps in the Alignment Sort and order the MUMs using LIS Output alignment MUMs regions that do not match exactly 26

27 MUMmer steps Locating MUMs Sorting MUMs Closure with gaps G1: ACTGATTACGTGAACTGGATCCA G2: ACTCTAGGTGAAGTGATCCA 27

28 Genome1: ACTGATTACGTGAACTGGATCCA Genome2: ACTCTAGGTGAAGTGATCCA Genome1: ACTGATTACGTGAACTGGATCCA Genome2: ACTCTAGGTGAAGTGATCCA ACTGATTACGTGAACTGGATCCA ACTC--TAGGTGAAGT-GATCCA 28

29 What is a MUM? MUM is a subsequence that occurs exactly once in both genomes and is NOT part of any longer sequence Two characters that bound a MUM are always mismatches GenA: tcgatcgacgatcgccgccgtagatcgaataacgagagagcataacgactta GenB: gcattagacgatcgccgccgtagatcgaataacgagagagcataatccagag Principle: if a long matching sequence occurs exactly once in each genome, it is certainly to be part of global alignment Similar to BLAST & FASTA!! 29

30 Sorting & ordering MUMs MUMs are sorted according to their position in Genome A The order of matching MUMs in Genome B is considered MUM3: Random match Inexact repeat 2 4 MUM5: transposition LIS algorithm to locate longest set of MUMs which occur in ascending order in both genomes Leads to Global MUM-alignment 30

31 MUMmer Results 2 strains of M. tuberculosis H37Rv & CDC1551 Genome size: 4Mb Time: 55 s Generating suffix tree: 5 s Sorting MUMs: 45s S&W alignment: 5 s 31

32 Alignment of M. tuberculosis strains CDC1551 (Top) & H37Rv (bottom) Single green lines indicate SNPs Blue lines indicate insertions 32

33 Comparison of 2 Mycoplasma genomes cousins that are distantly related M. genitalium: nt M. pneumoniae: ( ) Analysis of proteins tell us that all M.g. proteins are present in P.m. Alignment was carried using FASTA (dividing each genome into 1000 bp) All-against-all searches Fixed length of pattern (25) Using MUMmer (length = 25) 33

34 Comparison of 2 Mycoplasma genomes Using FASTA Fixed length patterns: 25mers MUMmer 34

35 Post-sequencing challenges Genome sequencing is just the beginning to appreciate biocomplexity Sequence-based function assignment approaches fail as the sequence similarity drops Structure-based function prediction approaches are limited by the availability of structures, association of structural motifs & associated functional descriptor As a result, in any genome, Genes with known function: ~ 40% Genes with unknown function: ~60% 35