Grenoble 2011 galtier

Transcriptomique haut-débit pour l'évolution moléculaire et la génétique des populations

CBGP, mars 2011

Nicolas Galtier

UMR 5554 - Institut des Sciences de l'Evolution - Montpellier

[email protected]

Molecular evolution in the 21st century

- an enormous amount of data (genomics)

- a robust theoretical framework (population genetics)

⇒ we should understand molecular variation patterns

We have:

Yet we do not really know:

- why some species evolve (much) faster than other, proteome-wise

- why GC-content varies between and across genomes

- by how much population size determines genetic diversity

- etc…

Molecular evolution in the 21st century

Why so many unsolved, basic questions?

- lacking theory

- biased sampling

species

genes

PopPhyl goals

Injecting species biology/ecology into comparative genomics

Exploring the molecular diversity of nonmodel taxa

Testing predictions of the population genetic theory genome-wide

life history traits population genetic parameters

genomic variation data

body mass generation time abundance mating system

mutation rate population size selection recombination

within-species between species

PopPhyl goals

Injecting species biology/ecology into comparative genomics

Exploring the molecular diversity of nonmodel taxa

Testing predictions of the population genetic theory genome-wide

Some specific questions we want to address:

- Why are fast-evolving taxa fast? (mutation, selection) - Are abundant species more polymorphic than scarce ones? - Is selection less efficient in selfers than outcrossers? - How does longevity influence mito vs nuclear DNA evolution? - Who optimizes codon usage, who does gBGC, and why? - Is the rate of selective sweeps higher in large populations?

How?

- Target = transcriptome coding sequences

expression data

- Sampling scheme:

focal species (10 individuals)

outgroups (1 or 2 individuals)

X 30

- Next-Generation Sequencing technology

For each taxon: 5.105 400 bp reads (454, pooled individuals) 5.107 100 bp reads (illumina, tagged individuals)

Species sampling

Demosponges Eponges

Cnidaires Cténophores Rotifères Acanthocéphales Entoproctes

Plathelminthes Némertes

Annélides Mollusques Ectoproctes Brachiopodes Chaetognathes Tardigrades Onychophores Arthropodes Loricifères Kinorhynches Priapulides Nématodes Hémichordés Echinodermes Céphalochordés Urochordés Vertébrés

Why are tunicates fast-evolving, proteome-wise?

T

V

- higher mutation rate? - more prevalent adaptive evolution ? - relaxed selective constraint on housekeeping genes ?

E

C

Data analysis pipeline

454

Solexa

reference transcriptome assembling

transcriptome reads

mapping

SNPs and genotypes

SNP calling

πN, πS, dN, dS

allele frequencies

coding annot.

Assembling transcriptomes from NGS data: a benchmark in Ciona

454

Solexa


D

454 reads

A c s

Celera

454 reads

B c s

Mira

454 reads

C c s

Cap3

Illumina reads

Abyss

c

s

Cap3 Cap3

c+s c+s

F E

C

454 reads Illumina reads

Abyss

Cap3

Cap3

merge reads

454 reads Illumina reads

Abyss

Cap3

Cap3

Cap3

merge contigs

F' - refine

c s

c+s

c+s

c+s

c+s c

c s

s

de novo transcriptome assembly: quantitative assessment

data set method contigs mean lg median lg N50 assembly

lg (Mb) touched

genes

A Ciona_454 Celera 25,669 491 438 491 12.6 7616

B Ciona_454 Mira 33,196 635 526 650 21.1 7951

C Ciona_454 Cap3 24,515 671 540 713 16.5 7945

D Ciona_illu Abyss+Cap3 27,426 574 380 769 15.8 7704

E Ciona_mix merge reads 29,097 571 399 721 16.6 7982

F Ciona_mix merge contigs 27,956 726 529 891 20.3 8207

0

500

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454_Con0gs

Illumina_con0gs

Mix_con0gs

Illumina contigs

454 contigs

Mix contigs

Illumina_contigs

454_contigs

Mix_contigs

0

20

40

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140 120

80

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1000 2000 1500

Assembling transcriptomes from NGS data: a benchmark using Ciona intestinalis

no hit

1→1

m→1

1→n

m→n

predicted contigs

reference transcriptome

BLAST

1→1 : full

partial m→1 :

fragments

alleles

1→n : chimera

multi m→n :

full or partial

multi

no hit 1→1

m→1

1→n

m→n

de novo transcriptome assembly: qualitative assessment

Average contig length varies between categories

4000 8000 12000

60%

80%

Improving assemblies by filtering according to length + coverage

number of contigs

correct

de novo transcriptome assembly from NGS data: conclusions

- illumina > 454 (454 useful yet)

- correct cDNA predictions are minoritary in typical assemblies

- existing programs differ substantially in performance (in PopPhyl we retain Cap3 and Abyss)

- contig length + coverage is a reasonable quality criterion

- somewhat variable across species

Data analysis pipeline

454

Solexa


transcriptome reads

mapping

SNPs and genotypes

SNP calling

πN, πS, dN, dS

allele frequencies

coding annot.

Calling SNPs and genotypes from transcriptome reads

>contig1 pos ind1 ind2 ind3 1 5/0/9/0 0/0/8/0 10/0/0/0 2 0/4/0/0 0/7/0/0 0/17/0/0 3 1/0/0/17 0/0/0/6 0/0/0/22 … >contig2 pos ind1 ind2 ind3 1 0/0/0/4 0/0/0/8 0/2/0/11 2 34/1/13/0 52/0/45/0 4/0/8/0 …

reads


>contig1 pos ind1 ind2 ind3 1 5/0/9/0 AG 0/0/8/0 GG 6/0/0/0 AA 2 0/4/0/0 CC 0/7/0/0 CC 0/17/0/0 CC 3 1/0/0/17 TT 0/0/0/6 TT 0/0/0/5 TT … >contig2 pos ind1 ind2 ind3 1 0/0/0/1 TT 0/0/0/8 TT 0/2/0/11 CT(90%) 2 14/1/9/0 AG 8/0/15/0 AG 12/0/0/0 AA …

genotypes


Model M1 : sequencing error ε

A:1 C:0 G:6 T:0

reads genotype

[AG]

[GG] 7 ε/3 (1-ε)6

7 (1/2-ε/3)7


Model M2: sequencing error ε and allelic bias α

A:1 C:0 G:6 T:0

reads genotype

[AG]

[GG] 7 ε (1-3ε)6

7 [q' q''6/2 + q'' q'6/2]

A:0 C:3 G:0 T:16

A:4 C:0 G:1 T:0

A:0 C:19 G:2 T:0

A:8 C:0 G:2 T:1

A:0 C:3 G:12 T:0

Population genomics of a fast-evolver

M1 M2

SNPs

error rate

allelic bias

30020

0.021 [0.012-0.038]

0

nb best model 70 (4.6%) 1532 (95.4%)

29544

0.020 [0.011-0.035]

[0.08-0.5]

stop codons 77 (0.26%) 117 (0.39%)

FIT -0.017 -0.054

focal species: Ciona intestinalis B (8 individuals) outgroup: Ciona intestinalis A (reference sequence)

1602 contigs (>10X in >5 individuals), of average length 138 codons

focal species: Ciona intestinalis B (8 individuals) outgroup: Ciona intestinalis A (reference sequence)

Population genomics of a fast-evolver

1602 contigs (>10X in >5 individuals), of average length 138 codons

average πS: 0.057 per site (a highly polymorphic species)

average πN: 0.0026 per site

πN/πS : 0.046 (strong level of purifying selection)

dN/dS : 0.103 (high impact of adaptive evolution)

estimated proportion of adaptive non-synonymous substitutions: 54%

Why are tunicates fast-evolving, proteome-wise?

T

V

- higher mutation rate? YES - more prevalent adaptive evolution ? YES - relaxed selective constraint on housekeeping genes ? NO

E

C

adaptive

neutral

deleterious

→ large Ne, large µ (per year)

Conclusions

- de novo population genomics from NGS transcriptome data is doable

- transcriptome assembly is probably the most tricky step

- major population genomic descriptors are robust to error models

- life history traits apparently impact molecular evolution to some extant

- long-lived, small population-sized species are the best choice for phylogenomics

VERTEBRES INSECTES

MOLLUSQUES

UROCHORDES CNID.

NEM. NEMATODES

ANNELIDES CRUSTACES SPONG.

- selfers vs outcrossers in snails and nematodes

- long-lived vs short-lived in insects

- big vs small in amniotes phylogeny of turtles

- fast proteic evolution in tunicates and nematodes

- extreme longevity

Subprojects we have started

Thanks to:

Philippe Gayral Vincent Cahais Georgia Tsagkogeorga Marion Ballenghien Zef Melo Ferreira Ylenia Chiari Lucy Weinert

Sylvain Glémin Nico Bierne Khalid Belkhir Fred Delsuc Vincent Ranwez

Guillaume Dugas Sébastien Harispe Caroline Benoist

CNRS

ISEM

ERC

Grenoble 2011 galtier

Technology

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