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M2 BIM - Génomes, Génétique et Evolution

M2 BIM

Génétique Génome et Evolution

13 octobre 2010

Anatomie et annotations des génomes

Definitions:

• 1) Genome: the genome is the entire DNA content of a cell- chromosomes- plasmids- mitochondrial DNA- chloroplastic DNA

• 2) Gene: A gene is an informative DNA sequence composed of a transcribed regionand a regulatory sequence

• 3) ORF (open reading frame): a DNA sequence betweeen two STOP codons. It ispresumed to be the sequence of a protein coding gene

• 4) CDS (coding sequence): a DNA sequence betweeen a START and a STOPcodon

• 5) Intron: a RNA sequence spliced from the pre-mature RNAExon: the coding part of the protein encoded genes

Genome sizes: The C-value paradox

2

0 500 1000 1500 2000 2500 3000

Escherichia coli

Saccharomyces cerevisiae

Caenorhabditis elegans

Arabidopsis thaliana

Drosophila melanogaster

Homo sapiens

(Mbp)

Estimated gene number:

~ 20,000

~ 25,000

~ 13,000

~ 19,000

~ 40,000

~ 6,000

Gene content: The G-value paradox

Paramecium tetraurelia

S. pombe n = 3Arabidopsis : n = 5S. cerevisiae : n = 16Human : n = 23Tobacco : n = 36Kiwi : n = 98Fern: n > 500

Number of chromosomes/haploid genome:

⇒no correlation between complexity:⇒genome size⇒number of genes⇒number of chromosomes

Genome content :

I) Unique sequences

- protein encoding genes

- RNA genes (RNAseP, TelC1,…)

II) Repeated sequences

- transposable elements

- ADN satellite

- protein encoding genes

- RNA genes (tDNA, rDNA, etc)

25-60% du génome des vertebrés environ 50% du génome humainJusqu’à 80% du génome des plantesou des amphibiens.

Most eukaryotic genomes contain high proportion of duplicated sequences

Duplicated Genes 43% 65% 49% 40% 50%

S. c. A. t. C. e. D. m. H. s. s.

2 - Gene duplications

duplication

Gene dosage increaseGenetic robustness

Gain of a newfunction

Specialization ofthe 2 copies

Most frequent fate:278 in yeast(Lafontaine et al. 2004)

Pseudogenization Neofunctionalization ConservationDegenerationComplementation

3

Homologs, orthologs (co-orthologs), paralogs (in-paralogs, out-paralogs)

Speciation

A1 A2

A

species 1 species 2

orthologs

ancestor

Speciation

A1 B1 B2

A B

species 1 species 2

A2

Duplication

ancestor

orthologs

out-paralogs out-paralogs

Speciation

A B C

A B

species 1 species 2

ancestor

orthologs

in-paralogs

Duplication

LOSSDUPLICATION

A1 C1 B2.2

Loss of C2

Loss of A2Loss of B1

A B C

A1 B1 C1A2 C2

species 1 species 2

B2.1 B2.2

B2.1

Speciation

Duplication

Duplication

Duplication

ancestor Organisation et structure des gènes« protéiques » chez les eucaryotes

4

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Organisation et structure des gènes« protéiques » chez les eucaryotes

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Organisation et structure des gènes« protéiques » chez les eucaryotes

Les amoureux fervents et les savants austères Aiment également, dans leur mûre saison, Les chats puissants et doux, orgueil de la maison, Qui comme eux sont frileux et comme eux sédentaires.Amis de la science et de la volupté, …/…

Ch. Beaudelaire, « Les chats »

multiplepromoters

multipleterminatorsalternatively spliced introns

alternative promoters

alternative terminators

alternative splicing

Structure of Eukaryotic coding genes:

Eukaryotic mRNAs are modified at their 5ʼ and 3ʼ ends-5ʼ cap-poly-A tail at 3ʼ end

Eukaryotic genes give rise to multiple protein products-alternative splicing-alternative promoters-alternative terminators

correlation betweencomplexity andprotein diversity?

About 100 000 prot in human

5

Chaque chromosome humain contient desdizaines de millions de paires de bases

TCGCGCGTTTCGGTGATGACGGTGAAAACCTCTGACACATGCAGCTCCCGGAGACGGTCACAGCTTGTCTGTAAGCGGATGCCGGGAGCAGACAAGCCCGTCAGGGCGCGTCAGCGGGTGTTGGCGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCCATTCGCCATTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGGGGATGTGCTGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCGAGCTCGGTACCCGGGGATCCTCTAGAGTCGACCTGCAGGCATGCAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGGAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCTTTCCAGTCGGGAAACCTGTCGTGCCAGCAGATCTGAATTAATTCGGTCGAAAAAAGAAAAGGAGAGGGCCAAGAGGGAGGGCATTGGTGACTATTGAGCACGTGAGTATATATACCGTGATTAAGCACACAAAGGCAGCTTGGAGTATGTCTGTTATTAATTTCACAGGTAGTTCTGGTCCATTGGTGAAAGTTTGCGGCTTGCAGAGCACAGAGGCCGCAGAATGTGCTCTAGATTCCGATGCTGACTTGCTGGGTATTATATGTGTGCCCAATAGAAAGAGAACAATTGACCCGGTTATTGCAAGGAAAATTTCAAGTCTTGTAAAAGCATATAAAAATAGTTCAGGCACTCCGAAATACTTGGTTGGCGTGTTTCGTAATCAACCTAAGGAGGATGTTTTGGCTCTGGTCAATGATTACGGCATTGATATCGTCCAACTGCATGGAGATGAGTCGTGGCAAGAATACCAAGAGTTCCTCGGTTTGCCAGTTATTAAAAGACTCGTATTTCCAAAAGACTGCAACATACTACTCAGTGCAGCTTCACAGAAACCTCATTCGTTTATTCCCTTGTTTGATTCAGAAGCAGGTGGGACAGGTGAACTTTTGGATTGGAACTCGATTTCTGACTGGGTTGGAAGGCAAGAGAGCCCCGAAAGTTTACATTTTATGTTAGCTGGTGGACTGACGCCAGAAAATGTTGGTGATGCGCTTAGATTAAATGGCGTTATTGGTGTTGATGTAAGCGGAGGTGTGGAGACAAATGGTGTAAAAGACTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATAGGTTATTACTGAGTAGTATTTATTTAAGTATTGTTTGTGCACTTGCCCAGATCTGCTGCATTAATGAATCGGCCAACGCGCGGGGAGAGGCGGTTTGCGTATTGGGCGCTCTTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCATCGATGCTCACTCAAAGGTCGGTAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCATAGCTCACGCTGTAGGTATCTCAGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGACCTTTGATCTTTTCTACGGGGTCTGACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTAAATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGAGGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGGAGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATAAACCAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGAAGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTACAGGATCGTGGTGTCACGCTCGTCGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCGGTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCATCGCGCGTTTCGGTGATGACGGTGAAAACCTCTGACACATGCAGCTCCCGGAGACGGTCACAGCTTGTCTGTAAGCGGATGCCGGGAGCAGACAAGCCCGTCAGGGCGCGTCAGCGGGTGTTGGCGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCCATTCGCCATTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGGGGATGTGCTGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCGAGCTCGGTACCCGGGGATCCTCTAGAGTCGACCTGCAGGCATGCAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGGAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCTTTCCAGTCGGGAAACCTGTCGTGCCAGCAGATCTGAATTAATTCGGTCGAAAAAAGAAAAGGAGAGGGCCAAGAGGGAGGGCATTGGTGACTATTGAGCACGTGAGTATATATACCGTGATTAAGCACACAAAGGCAGCTTGGAGTATGTCTGTTATTAATTTCACAGGTAGTTCTGGTCCATTGGTGAAAGTTTGCGGCTTGCAGAGCACAGAGGCCGCAGAATGTGCTCTAGATTCCGATGCTGACTTGCTGGGTATTATATGTGTGCCCAATAGAAAGAGAACAATTGACCCGGTTATTGCAAGGAAAATTTCAAGTCTTGTAAAAGCATATAAAAATAGTTCAGGCACTCCGAAATACTTGGTTGGCGTGTTTCGTAATCAACCTAAGGAGGATGTTTTGGCTCTGGTCAATGATTACGGCATTGATATCGTCCAACTGCATGGAGATGAGTCGTGGCAAGAATACCAAGAGTTCCTCGGTTTGCCAGTTATTAAAAGACTCGTATTTCCAAAAGACTGCAACATACTACTCAGTGCAGCTTCACAGAAACCTCATTCGTTTATTCCCTTGTTTGATTCAGAAGCAGGTGGGACAGGTGAACTTTTGGATTGGAACTCGATTTCTGACTGGGTTGGAAGGCAAGAGAGCCCCGAAAGTTTACATTTTATGTTAGCTGGTGGACTGACGCCAGAAAATGTTGGGAAGGCAAGAGAGCCCCGAAAGTTTACATTTTATGTTAGCTGGTGGACTGACGCCAGAAAATGTTGGGAAGGC

Chaque chromosome humain contient desdizaines de millions de paires de bases

centromere

télomère(TTAGGG)n

télomère(TTAGGG)n

subtélomère subtélomère

Protein/RNA complex -> RNA is template, protein is reverse transcriptase

1) RNA anneals to leading strand

2) Forms template to make more leading strand

3) Translocates 6 bp & repeats

4) Once have enough unpaired leading strand lagging strand is replicated in usual way.

-> add back piece that got left off

Prix Nobel de médecine 2009: Blackburn, Greider et Szostak

Comment séquencer l’ADN ?

6

Sequençage méthode didéoxy (Fred Sanger, Nobel 1980) Sequençage méthode didéoxy (Fred Sanger, Nobel 1980)

55% ~ 100 europeen laboratories17% Sanger centre, Cambridge15% Washington University, Saint Louis7% Stanford University4% Mc Gill University, Montréal2% Institut RIKEN, Japon

Library construction => DNA extraction => manual sequencing

8 years, 120 labs, 633 people The S. cerevisiae genome sequence

Life with 6000 genes; Goffeau et al., Science, 1996

Sanger method

Génolevures I 2000

Exploration of 13 species

Génolevures II 2004

Complete genome 4 species

Génolevures III 2009

Complete genome 3 species

Comparative genomics 6 French laboratories, GenoscopeGenopole Institut Pasteur

Library construction

automatic sequencing

DNA extraction

Yarrowia lipolytica

Saccharomyces cerevisiae

Candidaglabrata

Lachanceakluyveri(WashU seq centerM. Jonhston)

Debaryomyceshansenii

Kluyveromyces lactis

Lachanceathermotolerans

Zygosaccharomyces rouxii

7

Applied BiosystemsABI 3730XL

Applied BiosystemsSOLiD

Ce qui change :

– La quantité et le type des données générées- Le coût– La qualité des données (erreurs)

Illumina / SolexaGenetic Analyzer

Roche / 454Genome Sequencer FLX

New sequencing technologies 454 / Roche – Genome Sequence FLX

454 / Roche – Genome Sequence FLX 454 / Roche – Genome Sequence FLX

8

454 / Roche – Genome Sequence FLX

1 fragment -> 1 bille1 bille -> 1 lecture

Illumina / SolexaGenetic Analyzer 1G

Illumina / Solexa – Genetic Analyzer 1G

Illumina / Solexa – Genetic Analyzer 1G Illumina / Solexa – Genetic Analyzer 1G

9

Illumina / Solexa – Genetic Analyzer 1G Illumina / Solexa – Genetic Analyzer 1G

Applied BiosystemsSOLiD

Applied Biosystems – SOLiD (Sequencing by Oligo Ligation and Detection) Applied Biosystems – SOLiD (Sequencing by Oligo Ligation and Detection)

10

1980-2000 : Séquençage manuel => 1 Kb / réaction

2000-2008 : Séquençage automatique => 100 Kb /réaction

2008-2010 : Nouvelles technologies => 1 Gb / réaction

Résumé

X 100

X 10 000

?

Comment trouver les gènes ?TCGCGCGTTTCGGTGATGACGGTGAAAACCTCTGACACATGCAGCTCCCGGAGACGGTCACAGCTTGTCTGTAAGCGGATGCCGGGAGCAGACAAGCCCGTCAGGGCGCGTCAGCGGGTGTTGGCGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCCATTCGCCATTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGGGGATGTGCTGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCGAGCTCGGTACCCGGGGATCCTCTAGAGTCGACCTGCAGGCATGCAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGGAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCTTTCCAGTCGGGAAACCTGTCGTGCCAGCAGATCTGAATTAATTCGGTCGAAAAAAGAAAAGGAGAGGGCCAAGAGGGAGGGCATTGGTGACTATTGAGCACGTGAGTATATATACCGTGATTAAGCACACAAAGGCAGCTTGGAGTATGTCTGTTATTAATTTCACAGGTAGTTCTGGTCCATTGGTGAAAGTTTGCGGCTTGCAGAGCACAGAGGCCGCAGAATGTGCTCTAGATTCCGATGCTGACTTGCTGGGTATTATATGTGTGCCCAATAGAAAGAGAACAATTGACCCGGTTATTGCAAGGAAAATTTCAAGTCTTGTAAAAGCATATAAAAATAGTTCAGGCACTCCGAAATACTTGGTTGGCGTGTTTCGTAATCAACCTAAGGAGGATGTTTTGGCTCTGGTCAATGATTACGGCATTGATATCGTCCAACTGCATGGAGATGAGTCGTGGCAAGAATACCAAGAGTTCCTCGGTTTGCCAGTTATTAAAAGACTCGTATTTCCAAAAGACTGCAACATACTACTCAGTGCAGCTTCACAGAAACCTCATTCGTTTATTCCCTTGTTTGATTCAGAAGCAGGTGGGACAGGTGAACTTTTGGATTGGAACTCGATTTCTGACTGGGTTGGAAGGCAAGAGAGCCCCGAAAGTTTACATTTTATGTTAGCTGGTGGACTGACGCCAGAAAATGTTGGTGATGCGCTTAGATTAAATGGCGTTATTGGTGTTGATGTAAGCGGAGGTGTGGAGACAAATGGTGTAAAAGACTCTAACAAAATAGCAAATTTCGTCAAAAATGCTAAGAAATAGGTTATTACTGAGTAGTATTTATTTAAGTATTGTTTGTGCACTTGCCCAGATCTGCTGCATTAATGAATCGGCCAACGCGCGGGGAGAGGCGGTTTGCGTATTGGGCGCTCTTCCGCTTCCTCGCTCACTGACTCGCTGCGCTCGGTCGTTCGGCTGCGGCGAGCGGTATCAGCATCGATGCTCACTCAAAGGTCGGTAATACGGTTATCCACAGAATCAGGGGATAACGCAGGAAAGAACATGTGAGCAAAAGGCCAGCAAAAGGCCAGGAACCGTAAAAAGGCCGCGTTGCTGGCGTTTTTCCATAGGCTCCGCCCCCCTGACGAGCATCACAAAAATCGACGCTCAAGTCAGAGGTGGCGAAACCCGACAGGACTATAAAGATACCAGGCGTTTCCCCCTGGAAGCTCCCTCGTGCGCTCTCCTGTTCCGACCCTGCCGCTTACCGGATACCTGTCCGCCTTTCTCCCTTCGGGAAGCGTGGCGCTTTCTCATAGCTCACGCTGTAGGTATCTCAGTTCGGTGTAGGTCGTTCGCTCCAAGCTGGGCTGTGTGCACGAACCCCCCGTTCAGCCCGACCGCTGCGCCTTATCCGGTAACTATCGTCTTGAGTCCAACCCGGTAAGACACGACTTATCGCCACTGGCAGCAGCCACTGGTAACAGGATTAGCAGAGCGAGGTATGTAGGCGGTGCTACAGAGTTCTTGAAGTGGTGGCCTAACTACGGCTACACTAGAAGGACAGTATTTGGTATCTGCGCTCTGCTGAAGCCAGTTACCTTCGGAAAAAGAGTTGGTAGCTCTTGATCCGGCAAACAAACCACCGCTGGTAGCGGTGGTTTTTTTGTTTGCAAGCAGCAGATTACGCGCAGAAAAAAAGGATCTCAAGAAGACCTTTGATCTTTTCTACGGGGTCTGACGCTCAGTGGAACGAAAACTCACGTTAAGGGATTTTGGTCATGAGATTATCAAAAAGGATCTTCACCTAGATCCTTTTAAATTAAAAATGAAGTTTTAAATCAATCTAAAGTATATATGAGTAAACTTGGTCTGACAGTTACCAATGCTTAATCAGTGAGGCACCTATCTCAGCGATCTGTCTATTTCGTTCATCCATAGTTGCCTGACTCCCCGTCGTGTAGATAACTACGATACGGGAGGGCTTACCATCTGGCCCCAGTGCTGCAATGATACCGCGAGACCCACGCTCACCGGCTCCAGATTTATCAGCAATAAACCAGCCAGCCGGAAGGGCCGAGCGCAGAAGTGGTCCTGCAACTTTATCCGCCTCCATCCAGTCTATTAATTGTTGCCGGGAAGCTAGAGTAAGTAGTTCGCCAGTTAATAGTTTGCGCAACGTTGTTGCCATTGCTACAGGATCGTGGTGTCACGCTCGTCGTTTGGTATGGCTTCATTCAGCTCCGGTTCCCAACGATCAAGGCGAGTTACATGATCCCCCATGTTGTGCAAAAAAGCGGTTAGCTCCTTCGGTCCTCCGATCGTTGTCAGAAGTAAGTTGGCCGCAGTGTTATCATCGCGCGTTTCGGTGATGACGGTGAAAACCTCTGACACATGCAGCTCCCGGAGACGGTCACAGCTTGTCTGTAAGCGGATGCCGGGAGCAGACAAGCCCGTCAGGGCGCGTCAGCGGGTGTTGGCGGGTGTCGGGGCTGGCTTAACTATGCGGCATCAGAGCAGATTGTACTGAGAGTGCACCATATGCGGTGTGAAATACCGCACAGATGCGTAAGGAGAAAATACCGCATCAGGCGCCATTCGCCATTCAGGCTGCGCAACTGTTGGGAAGGGCGATCGGTGCGGGCCTCTTCGCTATTACGCCAGCTGGCGAAAGGGGGATGTGCTGCAAGGCGATTAAGTTGGGTAACGCCAGGGTTTTCCCAGTCACGACGTTGTAAAACGACGGCCAGTGAATTCGAGCTCGGTACCCGGGGATCCTCTAGAGTCGACCTGCAGGCATGCAAGCTTGGCGTAATCATGGTCATAGCTGTTTCCTGTGTGAAATTGTTATCCGCTCACAATTCCACACAACATACGAGCCGGAAGCATAAAGTGTAAAGCCTGGGGTGCCTAATGAGTGAGCTAACTCACATTAATTGCGTTGCGCTCACTGCCCGCTTTCCAGTCGGGAAACCTGTCGTGCCAGCAGATCTGAATTAATTCGGTCGAAAAAAGAAAAGGAGAGGGCCAAGAGGGAGGGCATTGGTGACTATTGAGCACGTGAGTATATATACCGTGATTAAGCACACAAAGGCAGCTTGGAGTATGTCTGTTATTAATTTCACAGGTAGTTCTGGTCCATTGGTGAAAGTTTGCGGCTTGCAGAGCACAGAGGCCGCAGAATGTGCTCTAGATTCCGATGCTGACTTGCTGGGTATTATATGTGTGCCCAATAGAAAGAGAACAATTGACCCGGTTATTGCAAGGAAAATTTCAAGTCTTGTAAAAGCATATAAAAATAGTTCAGGCACTCCGAAATACTTGGTTGGCGTGTTTCGTAATCAACCTAAGGAGGATGTTTTGGCTCTGGTCAATGATTACGGCATTGATATCGTCCAACTGCATGGAGATGAGTCGTGGCAAGAATACCAAGAGTTCCTCGGTTTGCCAGTTATTAAAAGACTCGTATTTCCAAAAGACTGCAACATACTACTCAGTGCAGCTTCACAGAAACCTCATTCGTTTATTCCCTTGTTTGAT

11

➥ Predictive methods

➥ Comparative methods

➥ Experimental methods

Interpretation of the DNA sequence into genes according to rules

Interpretation of the DNA sequence into genes according tosimilarities with other sequences

Interpretation of the DNA sequence into genes accordingto experimental results

Genetics, mutations, mappingcDNA librariesExpression data on microarraysRNA seq…

Strategies to find genes:

1000

1000

2000

2000

3000

3000

4000

4000

5000

5000

6000

6000

7000

7000

8000

8000

9000

9000

10000

10000

11000

11000

12000

12000

3> 3>

2> 2>

1> 1>

<1 <1

<2 <2

<3 <3

Stop codons (in the appropriate genetic code)*

AUG codons (translation initiator)

Watsonstrand

Crickstrand

frames

ORF (open reading frame):

a DNA sequence betweeen two STOP codons. It is presumed to be the sequence of a protein coding gene

CDS (coding sequence):

a DNA sequence betweeen a START and a STOP codon

➥ Predictive methods:

TTT phe F 2.7 TCT ser S 2.3 TAT tyr Y 1.9 TGT cys C 0.8

TTC phe F 1.8 TCC ser S 1.4 TAC tyr Y 1.4 TGC cys C 0.5

TTA leu L 2.7 TCA ser S 1.9 TAA OCH * TGA OPA *

TTG leu L 2.7 TCG ser S 0.9 TAG AMB * TGG trp W 1.0

CTT leu L 1.2 CCT pro P 1.3 CAT his H 1.4 CGT arg R 0.6

CTC leu L 0.5 CCC pro P 0.7 CAC his H 0.8 CGC arg R 0.3

CTA leu L 1.4 CCA pro P 1.8 CAA gln Q 2.7 CGA arg R 0.3

CTG leu L 1.1 CCG pro P 0.5 CAG gln Q 1.2 CGG arg R 0.2

ATT ile I 3.0 ACT thr T 2.0 AAT asn N 3.6 AGT ser S 1.5

ATC ile I 1.7 ACC thr T 1.2 AAC asn N 2.5 AGC ser S 1.0

ATA ile I 1.8 ACA thr T 1.8 AAA lys K 4.3 AGA arg R 2.1

ATG met M 2.1 ACG thr T 0.8 AAG lys K 3.1 AGG arg R 1.0

GTT val V 2.2 GCT ala A 2.0 GAT asp D 3.8 GGT gly G 2.3

GTC val V 1.1 GCC ala A 1.2 GAC asp D 2.0 GGC gly G 1.0

GTA val V 1.2 GCA ala A 1.6 GAA glu E 4.6 GGA gly G 1.1

GTG val V 1.1 GCG ala A 0.6 GAG glu E 2.0 GGG gly G 0.6

➥ Predictive methods: CAI = mesurement of the bias in codon usage(Sharp and Li, 1987)

• Discrepency of the genetic code > synonymous codons• Bias due to the different translational efficiencies of codons• Reference table of relative synonymous codon usage values (RSCU) from

highly expressed genes:

RSCUPhe UUU 0.203 UUC 1.797

Ile AUU 1.352 AUC 1.643 AUA 0.005- - - - - - - - -

freq obsRSCU = freq exp

12

CAI = mesurement of the bias in codon usage

CAI = CAIobs / CAImax

with CAIobs = ( II RSCUk)1/L

CAImax = (II RSCUkmax)1/L

and RSCU = relative synonymous codon usage

K=1

L

K=1

L

500

500

1000

1000

1500

1500

2000

2000

3> 3>

2> 2>

1> 1>

<1 <1

<2 <2

<3 <3

Mirror effects

YPR080w (TEF1)translation elongation factor EF-1 alpha

disregarded ORF

500

500

1000

1000

1500

1500

2000

2000

3> 3>

2> 2>

1> 1>

<1 <1

<2 <2

<3 <3

100

100

200

200

1.0 1.0

0.5 0.5

0.1 0.1

.01 .01

100

100

200

200

1.0 1.0

0.5 0.5

0.1 0.1

.01 .01

YKR035c

YKR035wa

507576 - 508190

507557 - 508198

One homolog (Pichia sorbitophila)

No homolog

FTI1: RAD52 inhibitor

➥ Comparative methods:

215 217 221 223

3

2

1

-1-2-3

YKL120w

PMT1

YKL121w YKL119c

VPH2

YKL117w YKL116c YKL114c

APN1

tRNA Ala1delta

Fra

me

s

A segment of the S. cerevisiae genome

Entirely included ORFs were disregarded

Partially overlapping ORFs were considered

Proportion gènes/génome : • 75% du genome de S. cerevisiae

• 1,5% du génome humain (40% including introns)

• 0,05% du génome de certaines plantes

13

S. cerevisiae genome:

- 12 Megabases

- 5807 protein-coding genes 140 rRNA genes 274 tRNA genes

- only 4% of intron-containing genes

- 40% of the genes belong to families

-ORFs occupy 72% of the genome!

200 400 600 800 1000 1200 1400 1600I

II

VVI

VII

XV

X

XIIXIIIXIV

XVI

IIIIV

VIIIIX

XI

mt- 16 chromosomes

260 000 ORFs < 100 codons

Basrai et al. (1997) Genome Research, 7, 768-771

« Small ORFs: Beautiful needles in the haystack »

small ORFs

⇒299 sORFs (Functional genomics of geneswith small open reading frames (sORFs) in S. cerevisiaeKastenmayer et al, Genome Research, 2006)

HRA1 antisens to DRS2:

DRS2

HRA1

GOLGI-membrane located transport proteinphenotype involved in maturation of 18S rRNA?

Responsible for the rRNA processing phenotype

Samanta et al., PNAS (2006); Global identification of noncoding RNAs in S. cerevisiae Rrp4

Ski2 Ski8

Ski3

Rrp47Rrp6

Two forms of the exosome in S. cerevisiae

Cytoplasm

Nucleus

Degradation ofmRNAs

Maturation/degradation of rRNAs, snRNAs, snoRNAs and tRNAs

Rrp41Rrp44

Rrp4Rrp41

Rrp44 Exosome = 3'→5' exonucleases

AAAAAAAAAA5'UTR 3'UTR

mRNA

ncRNA

A novel type of genetic elements: CUTs (cryptic unstable transcripts)

Wyers et al. Cell, 2005 -

14

A novel type of genetic elements: CUTs (cryptic unstable transcripts)

Neil et al., Nature 2009

A novel type of genetic elements: CUTs (cryptic unstable transcripts)

Neil et al., Nature 2009

Repression of serine biosynthesis

SER3

SRG1

Activation of serine catabolism

CAT1

Eukaryotic promoters are intrinsically bidirectional !!!

15

CONCLUSIONS :

-séquencer des génomes n’est plus vraiment limitant

- Annotation aisée des gènes codant pour des protéineset pour quelques types de gènes d’ARN

- La fraction ARN informative des génomes estvraisemblablement très sous-estimée et très difficile à annoter

- Trouver TOUS les gènes d’un organisme reste un véritablechalenge