comparative bacterial genomics joão carlos setubal vbi/virginia tech for embo course florianopolis,...

Comparative bacterial genomics

João Carlos SetubalVBI/Virginia Techfor EMBO course

Florianopolis, July 2008

Contents

• Tree of Life• Basic notions of genomics• Motivation for comparative genomics• Whole replicon alignment: pairwise and

multiple• Gene-centric comparisons• Orthology and Synteny• Exercises

April 21, 2023 JC Setubal 2

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Ciccarelli et al, Science, 2006


5Williams, Sobral, and DickermanJBAC, 2007

proteobacteria

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Genomes

• The entire DNA complement of a single cell• Abstraction

– a string s in the alphabet = {A, C, G, T}– Example

CTTCCAGTTCAACCGGCCGGTCGTCGCGGACGACGCGGCCGCCGGCGCCGCGATGCTGGCGGACGTACCGCACACCCGCCCCATCTCCATCTTCGCTTC


Genome sizes

• Genomes are measured in – kb (kilo base pairs), Mb (mega), or Gb (giga)

• Viruses: |s| = [5 – 200] kb• Bacteria: |s| = [1 – 10] Mb• Eukaryotes: |s| = [10 Mb – 100 Gb]• Humans: 3 Gb• marbled lungfish: 130 Gb T. Gregory, www.genomesize.com


Famous bacteria

• Haemophilus influenzae (1.8 Mb)– Human pathogen, first genome to be sequenced (1995)

• Escherichia coli (4.6 Mb)– Human pathogen and model organism (1997)

• Agrobacterium tumefaciens (6 Mb)– Plant pathogen and biotechnology tool (2001)


What is a gene

• A small substring of s that contains information

• Bacteria generally have 1 gene every 1 kb– 5 Mb genome = 5,000 genes


>A small section of a genomeAGCTCGCGCTCCGCATCCATCCAGTAGGGTTCGGTGTCGACGAGCGTGCC

GTCCATATCCCAGAAGACGGCGGCCGGCATCGCGTGCGGAGTCAGTTCGG

TCACGGCTGACAAGTCTATCCCGGCGGCCCCGGGCCTATTCTTGAGGGAC

GGCGTCCTGACCGGTCGCCGGATGAAAGGACCAGAACGCCCCGTGACTGA

CGCGAACAGCATCCTCGGAGGGCGCATCCTCGTGGTGGCCTTCGAAGGGT

GGAACGACGCTGGCGAGGCCGCCAGCGGGGCCGTCAAGACGCTCAAGGAC

CAGCTGGATGTCGTCCCGGTCGCCGAGGTCGATCCCGAGCTGTACTTCGA

CTTCCAGTTCAACCGGCCGGTCGTCGCGGACGACGACGGCCGCCGGCGCC

TCATCTGGCCGTCCGCGGAGATCCTGGGCCCAGCTCGCCCCGGCGACACC

GGCGATGCGCGCCTGGACGCCACCGGCGCCAACGCGGGCAATATCTTCCT

TCTCCTCGGCACCGAGCCGTCGCGCAGCTGGCGCAGCTTCACCGCGGAGA

TCATGGATGCGGCCCTGGCCTCCGACATCGGCGCCATCGTCTTCCTCGGT

GCGATGCTGGCGGACGTACCGCACACCCGCCCCATCTCCATCTTCGCTTC

GAGCGAGAACGCGGCCGTCCGTGCGGAGCTCGGCATCGAACGCTCTTCGT

ACGAGGGGCCGGTCGGTATCCTGAGCGCGCTCGCCGAAGGGGCGGAGGAC

GTGGGCATTCCGACCATCTCCATCTGGGCGTCGGTTCCGCACTATGTCCA

CAATGCGCCCAGCCCGAAGGCGGTGCTCGCACTGATCGACAAGCTCGAAG

AGCTGGTGAATGTCACCATCCCGCGTGGCTCGCTGGTGGAGGAGGCCACG

GCCTGGGAAGCCGGGATCGACGCGCTGGCTCTGGACGACGACGAGATGGC

TACGTACATCCAGCAGCTGGAGCAGGCACGCGACACCGTGGACTCCCCTG

AGGCCAGCGGCGAGGCGATCGCCCAGGAGTTCGAGCGCTACCTCCGCCGC

CGCGACGGCCGCGCCGGCGATGACCCCCGCCGTGGCTGACGTCACCCCCT

CTCTGCGTCCGCCGTCCTCTGTTCCCCCCGCTCGGCCTCCCCTGAGGCCG

AGGAGTCGCGCCCACATGCCGGAAACTCCTCCTTTCCTGACTTTCTGGAG

A bacterial gene


“Central Dogma” of molecular biology

• gene (DNA) messenger (RNA) protein (aminoacids)

transcription translation

Proteins are 3D objectsmade out of a linear sequence

of amino acids


A protein

www.berkeley.edu/.../ images/ras-rid-protein.gif April 21, 2023 12JC Setubal

Molecular Plant-Microbe Interactions

Sugar cane pathogen

Rattoon-stunting disease

Monteiro-Vitorello et al 2004


Comparative genomics

• There are currently more than 300 completed sequenced microbial genomes publicly available

• Many are of closely related species• In a few years there will be thousands• Why compare?• How to do it?


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


Citrus cankerXanthomonas

axonopodis pathovar citri


Black rot: Xanthomonas campestris pathovar campestris


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


Resolution

• Low resolution– Scope: entire genomes– Example event: rearrangement

• High resolution– Scope: nucleotide sequences– Example event: single mutation


Genome-wide evolutionary events

• Replicon rearrangements• Gene/region duplication• Gene/region loss• Chromosome plasmid DNA exchange• Lateral transfer


Copyright ©2004 by the National Academy of Sciences

Boussau, Bastien et al. (2004) Proc. Natl. Acad. Sci. USA 101, 9722-9727

Fig. 4. Net gene loss or gain throughout the evolution of the {alpha}-proteobacterial species


Example of a “multipartite genome”

Agrobacterium tumefaciens C58


Replicon structure in all completely sequenced rhizobiaceae plus M. loti

c58 s4 k84 Retli Rleg Sm Ml

1 2.84 3.73 4.00 4.38 5.06 3.65 7.04

2 2.07 1.28 2.65 0.64 0.87 1.68 0.35

3 0.54 0.63 0.39 0.51 0.68 1.35 0.21

4 0.21 0.26 0.18 0.37 0.49

5 0.21 0.04 0.25 0.35

6 0.13 0.19 0.15

7 0.08 0.18 0.15

Numbers are replicon size in Mbp

replicon

genome


Whole replicon alignments: the pairwise case

If the sequences were identical we would see

B

AApril 21, 2023 24JC Setubal

an inversion

A B C D

A

C B

D


A B C D

A

C

D

B

Such inversions seem to happen around the origin or terminus of replication


Eisen JA, Heidelberg JF, White O, Salzberg SL. Evidence for symmetric chromosomal inversions around the replication origin in bacteria. Genome Biol. 2000;1(6):RESEARCH0011


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. 2002 Jun 1;30(11):2478-83.

– 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):R12

• http://mummer.sourceforge.net


Xanthomonas axonopodis pv citri

E. coli K12 Promer alignment

Both are proteobacteria!Red: direct; green: reverse


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


sample nucmer output (coords file)• /home/setubal/agro/comp/mummer/../../rhizogenes/v1/ctgs.fasta

/home/setubal/agro/comp/mummer/../../vitis/v3/all.fasta• NUCMER

• [S1] [E1] | [S2] [E2] | [LEN 1] [LEN 2] | [% IDY] | [TAGS]• =====================================================================================• 73024 73193 | 242351 242181 | 170 171 | 93.60 | Contig789 Contig608• 220 6244 | 38759 32766 | 6025 5994 | 86.64 | Contig791 Contig604• 2798 6297 | 174039 177532 | 3500 3494 | 83.31 | Contig791 Contig606• 3828 6297 | 124183 126645 | 2470 2463 | 81.80 | Contig791 Contig606• 4767 5392 | 551684 551059 | 626 626 | 82.11 | Contig791 Contig607• 8214 8453 | 30747 30508 | 240 240 | 84.65 | Contig791 Contig604• 15408 15987 | 181050 181624 | 580 575 | 86.23 | Contig791 Contig606• 63864 74254 | 191954 181567 | 10391 10388 | 89.08 | Contig791 Contig604• 77203 79534 | 178882 176555 | 2332 2328 | 84.35 | Contig791 Contig604• 157451 158456 | 139804 140812 | 1006 1009 | 82.09 | Contig791 Contig606• 157483 157800 | 58429 58110 | 318 320 | 89.13 | Contig791 Contig604• 163575 166223 | 62781 60133 | 2649 2649 | 78.80 | Contig791 Contig605• 166754 168442 | 49403 47716 | 1689 1688 | 85.79 | Contig791 Contig604• 171247 173701 | 45005 42556 | 2455 2450 | 88.17 | Contig791 Contig604• 171261 172115 | 157617 158476 | 855 860 | 86.30 | Contig791 Contig606• 181828 184458 | 41748 39140 | 2631 2609 | 93.13 | Contig791 Contig604• 184829 185852 | 38838 37821 | 1024 1018 | 91.61 | Contig791 Contig604



A suffix tree for BANANAS

www.somethinkodd.com/.../2006/01/suffixtree.png

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


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. 2004 Jul;14(7):1394-403.


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RSA 493

RSA 331

Dugway

Chromosome alignmentMAUVE


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Genome Alignments MAUVE


How MAUVE works

• Seed-and-extend hashing• Seeds/anchors: Maximal Multiple Unique

Matches of minimum length k• Result: Local collinear blocks (LCBs)• O(G2n + Gn log Gn), G = # genomes, n =

average genome length


Alignment algorithm

1. Find Multi-MUMs2. Use the multi-MUMs to calculate a phylogenetic

guide tree3. 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


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


Orthologs


speciation

Out-paralogs



In-paralogs

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Published April 16, 2008

10 genomes

Orthology+

Phylogeny

45

AG: ancestral (belli [2], canadensis) TG: typhus (prowasekii, typhi)TRG: transitional (akari, felis) SFG: spotted fever (rickettsii, conorii, sibirica)

How to find orthologs

• Desired features of ortholog clustering– Ability to distinguish between in- and out-paralogs

• In-paralogs should be clustered with their orthologs

– Ability to cluster genes that have the same domain architecture, rather than simply sharing just one domain

• Methods– Phylogenetic trees– BLAST– MCL– orthoMCL


OrthoMCL

• Li L, Stoeckert CJ Jr, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003 Sep;13(9):2178-89

• Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002 Apr 1;30(7):1575-84


OrthoMCL

1. BLAST all-against-all2. weighting scheme 3. MCL algorithm• Nota bene: orthoMCL is not perfect!

– Two or more families may be wrongly joined– One family may be wrongly split


Li Li et al. Genome Res. 2003; 13: 2178-2189

orthoMCL pipeline

Li Li et al. Genome Res. 2003; 13: 2178-2189

OrthoMCL weighting scheme for similarity graph

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(Tribe)MCL

• Enright, Van Dongen, Ouzonis [2002]• Adaptation of MCL clustering algorithm of Van Dongen• Markov cluster• Simulates random walks in the graph• Expands and inflates certain matrices until equilibrium is

reached• Expansion: matrix squaring• Inflation: make expanded matrix become stochastic• Has been reasonably validated

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


3-way genome comparison


AB

C

Brucella gene set computations


Joining synteny and homology


Ortholog setBuilder (orthoMCL)

Genome 1

Genome 2

Genome n

Script 1

HTML Tables

Script 2

OAK: ortholog alignment for prokaryotes

graph

report annotatorsApril 21, 2023 57JC Setubal

R/G S4 C58 K84 R. etliR. leguminosarum

S. melilotiM. loti MAFF

M. loti BNC

12nd chromosome

linear chromosome

2nd chromosome

- - - - -

2 plasmid 630kb AT plasmid plasmid 390kbplasmid F 640kb

plasmid pRL12 870kb

plasmid pSymA

plasmid 1 plasmid 1

3 plasmid 259kb Ti plasmid plasmid 179kbplasmid E 510kb

plasmid pRL11 680kb

plasmid pSymB

plasmid 2 plasmid 2

4 plasmid 210kb - plasmid 44kbplasmid D 370kb

plasmid pRL10 490kb

- - plasmid 3

5 plasmid 130kb - -plasmid C 250kb

plasmid pRL9 350kb

- - -

6 plasmid 79kb - -plasmid A 190kb

plasmid pRL7 150kb

- - -

7 - - -plasmid B 180kb

plasmid pRL8 150kb

- - -

Replicon color key for HTML tables


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


Polymorphism detection

• inDels, SNPs• pseudogenes


I

II

Figure 4.

65

Pseudogenes

• Nonfunctional protein coding genes• Mutations introduce “sequence problems”

(frameshifts, stop in frame, absence of stop)• Natural mutation or sequencing error?

66

Pseudogene cases

67

• “Normal” bacterial genomes have 1-5% of pseudogenes [Liu et al]

• Pseudogenes can give interesting clues to evolutionary pathways

Why study pseudogenes?

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Why study pseudogenes? Cont’d

• High fractions of pseudogenes suggest a “genome degradation” process

• May be cause or effect of niche restriction• Examples

– Mycobacterium leprae: 36% (~1,100 genes)– Leifsonia xyli subsp. xyli: 13% (~300 genes)

• Pseudogenes do not show up in BLAST searches– Ortholog computations will in general not include them!

69

BLASTN

Annotated Pseudogenes vs. Genome Sequences

Previously Known PseudogeneKnown Gene (Homologous to Pseudogene)Newly Identified Pseudogene

Pseudogene Identification by Sequence SimilarityStudy of 8 Brucella Genomes

Brucella Pseudogene Analysis

Identification of New Pseudogenes by Homology

0

100

200

300

400

500

600

Bab9941 BabS19 Bcan23365 Bmel16M Bab2308 Bovi25840 Bsui1330 Bsui23445

PG Count: Initial

Tot. A lignments

Know n Genes

PG Count: Final

Total Alignments 4120 0.98

Gene hits 2627 0.62

pseudogenes 1493 0.35

Genomics is just the beginning

Genomics/proteomics

Interactions between molecules

Cell processes

complexity

Whole organisms


populations

21st century Biology: integration


Acks

• Nalvo Almeida• Chris Lasher• Brett Tyler• Rebecca Wattam


comparative bacterial genomics joão carlos setubal vbi/virginia tech for embo course florianopolis,...

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