comparative genomics: overview & tools + mummer algorithm · comparative genomics: overview...
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Comparative genomics:
Overview & Tools +
MUMmer algorithm
Urmila Kulkarni-Kale
Bioinformatics Centre
University of Pune, Pune 411 007.
urmila@bioinfo.ernet.in
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Genomes: the current status• Published complete genomes: 403
» Archaeal: 81
» Bacterial: 1226
» Eukaryal: 169
• Ongoing:
» Archaeal: 107
» Prokaryotic: 3478
» Eukaryotic: 1209
As of Jan 21, 2010
GOLD database
Viral: >4500
Metagenomics:203
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Genome databases
• Genomes at NCBI, EBI, TIGR
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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H. influenzae Complete Genome
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Function information clock of E. coli
Generated on March 2K4
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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.
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Knowledge of Full Genome sequence:
Solutions or new questions…?
• Still struggling
with the gene
counters…
Correct #
of
genes…?
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Genome analyses
• Variation in
– Genome size
– GC content
– Codon usage
– Amino acid composition
– Genome organisation
• Single circular chromosomes
• Linear chromosome + extra chromosomal elements
G, A, P, R: GC rich
I, F, Y, M, D: AT rich
E. coli: 4.6Mbp
M. pneumoniae: 0.81Mbp
B. subtilis: 4.20Mbp
B. burgdorferi: 29%
M. tuberculosis: 68%
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Structure of tryptophan operon • Numbers: Gene 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
Dan
dek
ar e
t al
., 1
998
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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.
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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COGs: Phylogenetic classification of proteins
encoded in complete genomes
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Genome analyses@NCBIPairwise genome comparison of protein
homologs (symmetrical best hits)
http://www.ncbi.nlm.nih.gov/sutils/geneplot.cgi
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Integr8: CG site at EBI
http://www.ebi.ac.uk/integr8
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Comparative Genomics Tools
• BLAST2
• MUMmer
• PipMaker
• AVID/VISTA
• Comparisons and analyses at both
– Nucleic acid and protein level
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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BLAST2
• Available at NCBI
• Input: GI or FASTA sequence (range can be
specified)
• Output:
– Graphical
– Alignment of 2 genomes
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Genome Alignment Algorithm:
MUMmer
• Developed by
– Dr. Steven Salzberg’s group at TIGR
– NAR (1999) 27:2369-2376
– NAR (2002) 30:2478-2483
• Availability
– Free
– TIGR site
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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MUMmer: Steps in the alignment process
Read two
genomesPerform Maximum Unique
Match (MUM) of genomes
Sort and order the
MUMs using LIS
Close the gaps
in the
Alignment
Using SNPs,
mutation regions,
repeats, tandem
repeats
Output
alignment
• MUMs
• regions that do not
match exactly
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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MUMmer steps
• Locating MUMs
• Sorting MUMs
• Closure with gaps
G1: ACTGATTACGTGAACTGGATCCA
G2: ACTCTAGGTGAAGTGATCCA
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Genome1: ACTGATTACGTGAACTGGATCCA
Genome2: ACTCTAGGTGAAGTGATCCA
Genome1: ACTGATTACGTGAACTGGATCCA
Genome2: ACTCTAGGTGAAGTGATCCA
ACTGATTACGTGAACTGGATCCA
ACTC--TAGGTGAAGT-GATCCA
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
• Principle: if a long matching sequence occurs exactly once in each genome, it is certainly to be part of global alignment
GenA: tcgatcGACGATCGCCGCCGTAGATCGAATAACGAGAGAGCATAAcgactta
GenB: gcattaGACGATCGCCGCCGTAGATCGAATAACGAGAGAGCATAAtccagag
Similar to
BLAST & FASTA!!
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Sorting & ordering MUMs• MUMs are sorted according to their position in
Genome A
• The order of matching MUMs in Genome B is considered
• LIS algorithm to locate longest set of MUMs which occur in ascending order in both genomes
2 4
MUM5:transposition
MUM3:
Random match
Inexact repeat
Leads to Global MUM-alignment
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Alignment of M. tuberculosis strains
CDC1551 (Top) & H37Rv (bottom)
Single green lines
indicate SNPs
Blue lines
indicate insertions
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Comparison of 2 Mycoplasma genomes
cousins that are distantly related
• M. genitalium: 580 074 nt
• M. pneumoniae: 816 394 (+226 000)
• 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)
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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Comparison of 2 Mycoplasma genomes
Using FASTA
Fixed length
patterns: 25mers
MUMmer
Jan 21, 2010 © UKK, Bioinformatics Centre,
University of Pune.
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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 unknown
function: ~60%
Genes with known
function: ~ 40%
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