Transcript Genome evolution: a sequence

Genome Evolution. Amos Tanay 2009
Genome evolution
Lecture 10: Comparative genomics,
non coding sequences
Genome Evolution. Amos Tanay 2009
Why larger genomes?
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Ameobe dubia – 670Gb!
S. cerevisae is 0.3% of human, D. melanogaster is 3%
Selflish DNA –
– larger genomes are a result of the proliferation of selfish DNA
– Proliferation stops only when it is becoming too deleterious
•
Bulk DNA
– Genome content is a consequence of natural selection
– Larger genome is needed to allow larger cell size, larger nuclear membrane etc.
Genome Evolution. Amos Tanay 2009
Why smaller genomes?
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Metabolic cost: maybe cells lose excess DNA for energetic efficiency
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But DNA is only 2-5% of the dry mass
No genome size – replication time correlation in prokaryotes
Replication is much faster than transcription (10-20 times in E. coli)
Genome Evolution. Amos Tanay 2009
Mutational balance
• Balance between deletions and insertions
– May be different between species
– Different balances may have been evolved
• In flies, yeast laboratory evolution
– 4-fold more 4kb spontaneous insertions
• In mammals
– More small deletions than insertions
Mutational hazard
• No loss of function for inert DNA
– But is it truly not functional?
• Gain of function mutations are still possible:
– Transcription
– Regulation
Differences in population size may make DNA purging more effective for
prokaryotes, small eukaryotes
Differences in regulatory sophistication may make DNA mutational hazard less
of a problem for metazoan
Genome Evolution. Amos Tanay 2009
Repeats: selfish DNA
Genome Evolution. Amos Tanay 2009
Retrotransposition via RNA
Repetitive elements in the human
genome
Class
Copies
Genome
Fraction
LINEs
868,000
20.4%
(only ~100
active!!)
SINEs
1,558,000
(70% Alu)
13.1%
LTR
elements
443,000
8.3%
Transposons
294,000
2.8%
Genome Evolution. Amos Tanay 2009
Burst of repeats activity
Han et al. 2005
Genome Evolution. Amos Tanay 2009
Age of repeats in the human genome
Genome Evolution. Amos Tanay 2009
DNA and gene distribution in the isochore families of the human genome
These trends are quite clear. But the existence of
distinct isochore classes can be questioned
Bernardi G. PNAS 2007;104:8385-8390
Genome Evolution. Amos Tanay 2009
The selection hypotheses on the origin of G+C content heterogeneity
Bernardi G. PNAS 2007;104:8385-8390
Genome Evolution. Amos Tanay 2009
Genomic information: Protein coding genes
Genome information: RNA genes
Genome Evolution. Amos Tanay 2009
mRNA – messenger RNA. Mature gene transcripts after introns have been
processed out of the mRNA precursor
miRNA – micro-RNA. 20-30bp in length, processed from transcribed “hair-pin”
precursors RNAs. Regulate gene expression by binding nearly perfect
matches in the 3’ UTR of transcripts
siRNA – small interfering RNAs. 20-30bp in length, processed from double
stranded RNA by the RNAi machinary. Used for posttranscriptional silencing
rRNA – ribosomal RNA, part of the ribosome machine (with proteins)
snRNA – small nuclear RNAs. Heterogeneous set with function confined to
the nucleus. Including RNAs involved in the Splicesome machinery.
snoRNA – small nucleolar RNA. Involved in the chemical modifications made
in the construction of ribosomes. Often encode within the introns of ribosomal
proteins genes
tRNA – transfer RNA. Delivering amino-acid to the ribosome.
piRNA – silencing repeats in the germline
Genome Evolution. Amos Tanay 2009
Gene content in the genome
M. Lynch
Genome Evolution. Amos Tanay 2009
Genome information:
Introns/Exons
Genome Evolution. Amos Tanay 2009
Pseudogenes
Genes that are becoming
inactive due to mutations are
called pseudogenes
mRNAs that jump back into
the genome are called
processed pseudogenes
(they therefore lack introns)
M. Lynch
Genome Evolution. Amos Tanay 2009
Adaptive evolution of non-coding DNA in Drosophila
(P. Andolfatto, 2005)
12 D. melanogaster collected in Zimbabwe
188 regions of ~800bp, surveyed for polymorphisms
compared to sequences of D. simulans to measure divergence
Classified loci according to genomic context
Genome Evolution. Amos Tanay 2009
Estimating q
Theorem: Let u be the mutation rate for a locus under consideration, and set q=4Nu. Under
the infinite sites model, the expected number of segregating sites is:
n 1
1
i 1 i
E (S )  q 
The Waterston estimator for theta is:
 n 1 1 
qW  S /   
 i 1 i 
Definition: Let Dij count the number of differences between two sequences. The average
number of pairwise difference in a sample of n individuals is:
 n
D n   
 2
1
D
i, j
Theorem: as always, q=4Nu. We have:
ED n  q
ij
 q
Genome Evolution. Amos Tanay 2009
Tajima’s D
Theorem: as always, q=4Nu. We have:
ED n  q
Proof:
Going backwards. Coalescent is occuring before mutation in a rate of:
1 /(1  4 Nu )  1 /(q  1)
After one mutation occurred, we again have the same rate so overall:
 q  1
P(D 2  k )  

q

1

 q 1
k
The expected value of this geometric series is q, and so is the average of all pairs.
Definition: Tajima’s D is the difference between two estimators of q:
D  q  qW
Genome Evolution. Amos Tanay 2009
Tajima’s D for classes of drosophila sequence
Definition: Tajima’s D is the difference between two estimators of q:
D  q  qW
High D values: allele
multiplicities are spread more
evenly than expected – (why?)
Low D values: More rare alleles
are present (Why?)
Genome Evolution. Amos Tanay 2009
Adaptive evolution of non-coding DNA in Drosophila
(P. Andolfatto)
The proportion of divergence driven by positive selection:
a = 1–(DSPX/DXPS)
Genome Evolution. Amos Tanay 2009
Phastcons (A. Siepel)
Each model is context-less
Transition parameters are
kept fixed – this determine
the fraction of conserved
sequence
Inference on the phyloHMM
-> inferred conserved model
posteriors
Use threshold to detect
contiguous regions of high
conservation posterior
Learning the branch lengths
Siepel A. et.al. Genome Res. 2005;15:1034-1050
Genome Evolution. Amos Tanay 2009
Phastcons parameters
Siepel A. et.al. Genome Res. 2005;15:1034-1050
Genome Evolution. Amos Tanay 2009
Fixation probabilities and population size: what selection coefficient can
drive a 70% decrease in substitution rate (if N_e = 10,000)?
P2 Np (T2 N
1  e 4 Nsp
2s
 T0 ) 

1  e 4 Ns 1  e 4 Ns
0.02
0.01
0.0001
0.000001
0.00000001
0.015
1E-10
1E-12
1E-14
1E-16
0.01
1E-18
Ne=100
Ne=1000
Ne=10000
Ne=100000
Ne=100
Ne=1000
Ne=10000
Ne=100000
1E-20
1E-22
1E-24
0.005
1E-26
1E-28
1E-30
1E-32
0
-0.005
-0.003
-0.001
0.001
0.003
0.005
0.007
1E-34
0.009
1E-36
1E-38
-0.005
-0.005
-0.003
1E-40
-0.001
0.001
0.003
0.005
0.007
0.009
Genome Evolution. Amos Tanay 2009
ENCODE
Ultra-conserved elements
Genome Evolution. Amos Tanay 2009
481 segment longer than 200bp that are absolutely conserved between human, mouse and rat (Bejerano
et al 2005)
What are these elements doing? Why they are completely conserved? 4 Knockouts are not revealing
significant phenotypes..
Ahituv et al. PloS Biolg 2007
Ultra-conserved elements
Genome Evolution. Amos Tanay 2009
Population genetics do suggest ultraconserved elements are under selection
Separating mutational effects from
selective effect is still a challenge…
Katzman et al., Science 2007