Genome evolution

1. Genome evolution Lecture 10: Comparative genomics, non coding sequences

2. Why larger genomes? • 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.

3. Why smaller genomes? • Metabolic cost: maybe cells lose excess DNA for energetic efficiency • 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)

4. 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

5. Repeats: selfish DNA

6. Retrotransposition via RNA Repetitive elements in the human genome

7. Burst of repeats activity Han et al. 2005

8. Age of repeats in the human genome

9. 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

10. The selection hypotheses on the origin of G+C content heterogeneity Bernardi G. PNAS 2007;104:8385-8390

11. Genomic information: Protein coding genes

12. Genome information: RNA genes 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

13. Gene content in the genome M. Lynch

14. Genome information: Introns/Exons

15. 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

16. 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

17. 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: The Waterston estimator for theta is: Definition: Let Dij count the number of differences between two sequences. The average number of pairwise difference in a sample of n individuals is: Theorem: as always, q=4Nu. We have:

18. Tajima’s D Theorem: as always, q=4Nu. We have: Proof: Going backwards. Coalescent is occuring before mutation in a rate of: After one mutation occurred, we again have the same rate so overall: 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:

19. Tajima’s D for classes of drosophila sequence Definition: Tajima’s D is the difference between two estimators of q: High D values: allele multiplicities are spread more evenly than expected – (why?) Low D values: More rare alleles are present (Why?)

20. Adaptive evolution of non-coding DNA in Drosophila (P. Andolfatto) The proportion of divergence driven by positive selection: a = 1–(DSPX/DXPS)

21. 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

22. Phastcons parameters Siepel A. et.al. Genome Res. 2005;15:1034-1050

23. Fixation probabilities and population size: what selection coefficient can drive a 70% decrease in substitution rate (if N_e = 10,000)?

24. ENCODE

25. Ultra-conserved elements 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

26. Ultra-conserved elements Population genetics do suggest ultraconserved elements are under selection Separating mutational effects from selective effect is still a challenge… Katzman et al., Science 2007