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LBBE, CNRS, Université de Lyon Evolutionary genomics Bastien Boussau [email protected] @bastounette

Evolutionary genomics

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A presentation about evolutionary genomics, showing a selection of examples of what information can be extracted from genomes.

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Page 1: Evolutionary genomics

LBBE, CNRS, Université de Lyon

Evolutionary genomics

Bastien Boussau

[email protected]

@bastounette

Page 2: Evolutionary genomics

Chance and necessity

…ATCGACATCAGCATCAGCACTAC…

Page 3: Evolutionary genomics

Chance and necessity

… …Evolution

…ATCGACATCAGCATCAGCACTAC…

Page 4: Evolutionary genomics

Chance and necessity

… …Evolution

Function

…ATCGACATCAGCATCAGCACTAC…

Page 5: Evolutionary genomics

3

Evolution in our genomes

3

Brown, Sanger, Kitai. Biochem. J. 1955.

Page 6: Evolutionary genomics

3

Evolution in our genomes

3

Brown, Sanger, Kitai. Biochem. J. 1955.

Page 7: Evolutionary genomics

3

Evolution in our genomes

3

Brown, Sanger, Kitai. Biochem. J. 1955.

Page 8: Evolutionary genomics

Genomes as Documents of Evolutionary History

4

Page 9: Evolutionary genomics

What information can we extract from genome sequences?

5

1. Species phylogeny!

2. Phylogeography!

3. Diversification history!

4. Ancestral lifestyles!

5. Selective pressures in extant species!

6. Application to cell lineages

Page 10: Evolutionary genomics

Genome evolution

6

Genome sequence

Processes

…ACTCGATCGCATCGACTCCTCCAGC…

Page 11: Evolutionary genomics

Genome evolution

6

Genome sequence

point mutations

Processes

…ACTCGTTCGCATCGACTCCTCCAGC…

Page 12: Evolutionary genomics

Genome evolution

7

Genome sequence

point mutations

Processes

…ACTCGATCGCATCGACTCCTCCAGC…

Page 13: Evolutionary genomics

Genome evolution

7

Genome sequence

point mutations insertions/deletions

Processes

…ACTCGATCGCATCGAAAACTCCTCCAGC…

Page 14: Evolutionary genomics

Genome evolution

8

Genome sequence

point mutations insertions/deletions

Processes

…ACTCGATCGCATCGACTCCTCCAGC…

Page 15: Evolutionary genomics

Genome evolution

8

Genome sequence

point mutations insertions/deletions duplications/losses

Processes

…ACTCGATCGCATCGACTCCTTCCTCCAGC…

Page 16: Evolutionary genomics

Genome evolution

9

Genome sequence

point mutations insertions/deletions duplications/losses

Processes

…ACTCGATCGCATCGACTCCTCCAGC…

Page 17: Evolutionary genomics

Genome evolution

9

Genome sequence

point mutations insertions/deletions duplications/losses rearrangements

Processes

…ACTCGATCGCAAGCTCTCCTCCAGC…

Page 18: Evolutionary genomics

Genome evolution

10

Genome sequence

point mutations insertions/deletions duplications/losses rearrangements

Processes

population genetics molecular machinery species phylogeny environment

…ACTCGATCGCATCGACTCCTCCAGC…

Page 19: Evolutionary genomics

Using genomes for statistical inference

11

Genome sequence

point mutations insertions/deletions duplications/losses rearrangements

Processes

population genetics molecular machinery species phylogeny environment

…ACTCGATCGCATCGACTCCTCCAGC…

Page 20: Evolutionary genomics

Inferential statistics

12

Boussau and Daubin, Tree 2010

Page 21: Evolutionary genomics

Inferential statistics

12

Boussau and Daubin, Tree 2010

• Using computers!• Probabilistic models (e.g. models of sequence evolution)!• “What I cannot create, I do not understand.” (Feynman, 1988)!• What I cannot simulate, I do not understand.”

Page 22: Evolutionary genomics

What information can we extract from genome sequences?

13

1. Species phylogeny!

2. Phylogeography!

3. Diversification history!

4. Ancestral lifestyles!

5. Selective pressures in extant species!

6. Application to cell lineages

Page 23: Evolutionary genomics

1 Inferring the phylogeny

Models:!

• Modelling events of substitution!

• In some cases, modelling insertions and deletions!

• In some cases, modelling allele sorting!

• In some cases, modelling gene duplications, losses and transfers!

• In some cases, modelling hybridization!

• Dates of speciation can also be inferred with a model of rate evolution

Page 24: Evolutionary genomics

1 The phylogeny of life

Williams et al., Nature 2013

Page 25: Evolutionary genomics

1 The phylogeny of life

Williams et al., Nature 2013

Improvements in:!• probabilistic models!• data available

Page 26: Evolutionary genomics

1 The origin of viral strains

Boussau, Guéguen, Gouy, Evolutionary Bioinformatics 2009.

1353 first sites ~1200 remaining sites

Page 27: Evolutionary genomics

1 The origin of viral strains

Boussau, Guéguen, Gouy, Evolutionary Bioinformatics 2009.

The N HIV strain originated through a recombination between a Human and a Chimp virus

1353 first sites ~1200 remaining sites

Page 28: Evolutionary genomics

Contagion, Steven Soderbergh, 2011

Page 29: Evolutionary genomics

Contagion, Steven Soderbergh, 2011

Page 30: Evolutionary genomics

1 Forensic analyses

Scaduto et al., PNAS 2010

The problem:!

• CC01 is a HIV-positive male, accused by several partners of hiding

his seropositivity and infecting them in the process ==> trial!

• 1 accused male, 6 partners, all seropositive!

• HIV sequences available from each of them!

• How can we tell whether CC01 likely contaminated his partners?

Page 31: Evolutionary genomics

1 Forensic analyses

Scaduto et al., PNAS 2010

The problem:!

• CC01 is a HIV-positive male, accused by several partners of hiding

his seropositivity and infecting them in the process ==> trial!

• 1 accused male, 6 partners, all seropositive!

• HIV sequences available from each of them!

• How can we tell whether CC01 likely contaminated his partners?

Use the HIV sequences to build a phylogenetic tree!

Page 32: Evolutionary genomics

1 Forensic analyses

Scaduto et al., PNAS 2010

Page 33: Evolutionary genomics

1 Forensic analyses

Scaduto et al., PNAS 2010

Evidence used to establish that CC01 had infected his partners

Page 34: Evolutionary genomics

1 Inferring the phylogeny

Purposes:!

• Inferring the species phylogeny!

• Reconstructing the evolutionary history of infectious agents!

• Reconstructing transmission histories (e.g. forensic analyses)!

Page 35: Evolutionary genomics

2 Phylogeography

Question:!• How did these organisms get to be where they are?!

Page 36: Evolutionary genomics

2 Phylogeography

Question:!• How did these organisms get to be where they are?!

Mus musculus, GBIF database

Page 37: Evolutionary genomics

2 Phylogeography

Faria et al., Science 2014

Models:!• Add spatial information at the leaves!• Use Discrete models or continuous models to reconstruct

ancestral ranges!

Page 38: Evolutionary genomics

2 Phylogeography

Landis et al., Syst. Biol. 2014

Page 39: Evolutionary genomics

Landis et al., Syst. Biol. 2014

2 Phylogeography

Page 40: Evolutionary genomics

2 HIV phylogeography

Faria et al., Science 2014

Page 41: Evolutionary genomics

2 Phylogeography

Purposes:!

• Inferring the species geographical range through time!

• Reconstructing the evolutionary spread of infectious agents!

• Investigating plate tectonics!

Page 42: Evolutionary genomics

3 Diversification history

How did species diversify? Were there bursts of speciation, or

mass extinctions? !

How many species/individuals through time?!

Models:!

• Modelling events of speciation, and events of extinction!

• In some cases, can be dependent on other parameters

Page 43: Evolutionary genomics

28

3 Species of birds through time

Jetz et al., Nature 2012

Page 44: Evolutionary genomics

29

3 Speciations of birds across the globe

Jetz et al., Nature 2012

Page 45: Evolutionary genomics

30

3 Phylodynamics of HCV in Egypt

Drummond et al., MBE 2005

Page 46: Evolutionary genomics

30

3 Phylodynamics of HCV in Egypt

Drummond et al., MBE 2005

Huge increase in number of viruses coincides with the extensive use of an antischistosomiasis treatment from 1920 to 1980

Page 47: Evolutionary genomics

3 Diversification history

Purposes:!

• Inferring the number of species/individuals through time!

• Finding major radiation/extinction events!

• Reconstructing past epidemics!

Page 48: Evolutionary genomics

4 Ancestral lifestyles

How did ancestral species live? What temperature did they

like most? How long did they live?!

Models:!

• Correlating molecular evolution with phenotypic traits!

Page 49: Evolutionary genomics

4 Inferring growth temperature across the tree of life

Page 50: Evolutionary genomics

4 Inferring growth temperature across the tree of life

Idea: reconstruct ancestral sequences in silico, and predict ancestral growth temperatures

Page 51: Evolutionary genomics

4 Inferring growth temperature across the tree of life

Usual model: !all branches evolve according to the same model

Better model: !different models for different branches

Page 52: Evolutionary genomics

Boussau et al., Nature 2008

4 Inferring growth temperature across the

tree of life

Page 53: Evolutionary genomics

Boussau et al., Nature 2008

4 Inferring growth temperature across the tree of life

Page 54: Evolutionary genomics

Boussau et al., Nature 2008

4 Inferring growth temperature across the tree of life

Late Heavy Bombardment 3.8 Bya?

Page 55: Evolutionary genomics

Lartillot and Delsuc, Evolution 2012

4 Joint inference of rates, dates, and traits

Page 56: Evolutionary genomics

4 Ancestral lifestyles

Purposes:!

• Inferring characteristics of ancient organisms!

• Inferring characteristics of ancient environments!

• Finding/using correlations between phenotype evolution and

genotype evolution

Page 57: Evolutionary genomics

5 Selective pressures in extant species

What sites in the genome of species X are important?!

Models:!

• Usual models of sequence evolution!

• Models of insertion-deletion!

• Hidden Markov Models that run along the genome!

Page 58: Evolutionary genomics

40

5 Conservation across species indicates function

40

Brown, Sanger, Kitai. Biochem. J. 1955.

Page 59: Evolutionary genomics

41

5 Conservation across species indicates function

41

The UCSC genome browser uses conservation across 100 vertebrates to detect functional regions

Page 60: Evolutionary genomics

Gnad et al., BMC Genomics 2013

5 How to best predict cancer-

causing mutations?

Comparison across 12 methods:

Page 61: Evolutionary genomics

5 How to best predict cancer-causing mutations?

Gnad et al., BMC Genomics 2013

Page 62: Evolutionary genomics

5 Selective pressures in extant species

Purposes:!

• Screening a genome for new functional elements!

• Evaluating the severity of candidate mutations (e.g. genetic

disease, cancer)!

• Finding sites to target in a pest that needs controlling

Page 63: Evolutionary genomics

6 Application to cell lineages

As cells divide by mitosis, mutations accumulate. !—> phylogenetic approaches can be used to address developmental questions:!• How similar is development across individuals?!• Do the first cells produced during development contribute equally to

the adult organism?!• …!Models:!• Usual models of sequence evolution!• Models of microsatellite evolution

Page 64: Evolutionary genomics

6 Application to cell lineages

Behjati et al., Nature 2014

Page 65: Evolutionary genomics

6 Application to cell lineages

Behjati et al., Nature 2014

Contributions of early embryonic cells to adult tail cell populations

Page 66: Evolutionary genomics

6 Application to cell lineages

Page 67: Evolutionary genomics

6 Application to cell lineages

Purposes:!

• Learning about development!

• Learning about cancer evolution (e.g. using phylogeography to

understand cancer spread)

Page 68: Evolutionary genomics

Conclusions

• Genomes contain a lot of information about their history and about how they work!

• The comparative approach is a powerful way to learn about the function of a stretch of sequence!

• Thanks to probabilistic models, one can exploit the huge amount of information in genomes to ask a large number of interesting questions

Slides available on SlideShare: http://www.slideshare.net/boussau