High-throughut comparative genomics

1. High-throughut comparative genomics 24th October 2013 Joe Parker, Queen Mary University London

2. Topics • Introduction • Background: why phylogenomics? • Examples • Practice • Case study • On the horizon • Over the horizon

3. Aims • Context of phylogenomics: Next-generation sequencing (NGS) • Why phylogenomics? • Practical analyses • Future developments

4. 1. Our Research

5. Lab Interests • Ecology and evolution of traits • Echolocation, sociality • NGS data for population genetics and phylogenomics

6. Activities • Phylogeny estimation/comparison • Molecular correlates of evolution; • site substitutions, dN/dS, composition • Simulation • Dataset limitations (R-L): Joe Parker; GeorgiaTsagkogeorga; Kalina Davies; Steve Rossiter; Xiuguang Mao; Seb Bailey

7. 2. Background

8. Next-generation sequencing

9. Why phylogenomics, not -genetics? • Causes of discordant signal • Incomplete lineage sorting • Lateral transfer • Recombination • Introgression

10. Quantitative biology • Multiple configurations • Hyperparameters empirically investigated • Determine sensitivity of results

11. Distributions • Genome-scale data provides context • Identify outliers Genes / taxa / trees • Compare values across biological systems

12. Integration with ‘Omics • Multiple databases • Functional data • Bibliographic information

13. 3. Example studies

14. Tsakgogeorgia et al. (in press)

15. Salichos & Rokas (2013)

16. Backström et al. (2013)

17. Lindblad-Toh et al. (2011)

18. 4. Practice

19. Source material • Samples • Storage • Purification • Library prep

20. Sequencing • Genome • Sanger • Illumina • Pyro /454 • SOLiD • PacBio • Transcriptome / RNA-seq • MyBAITS • HiSeq / MiSeq • IonTorrent

21. Infrastructure • Desktop machines • Computing clusters • Grid systems • Cloud-based computation

22. Assembly, Annotation • Assembly • To reference (mapping) • De novo • Annotation • By homology • De novo • SOAPdenovo • MAKER • Velvet • Bowtie / Cufflinks / Tophat • Trinity

23. Alignment • PRANK • MUSCLE • MAFFT • Clustal

24. Phylogeny inference • MrBayes • RAxML • BEAST • MP-EST • STAR

25. Phylogenetic analysis • BEAST • HYPHY • PAML • Pipelines • LRT

26. 5. Case study

27. Parker et al. (2013) • De novo genomes: • four taxa • 2,321 protein-coding loci • 801,301 codons • Published: • 18 genomes • ~69,000 simulated datasets • ~3,500 cluster cores

28. Our pipeline for detecting genome-wide convergence

36. mean = 0.05

37. mean = 0.05 mean = -0.01 mean = -0.08 

38. Development cycle Design Alignment loadSequences() getSubstitutions() Phylogeny trimTaxa() getMRCA() Review, refine & refactor Wireframe & specify tests DataSeries calculateECDF() randomise() Regression getResiduals() predictInterval() Implement

39. Parker et al. (2013)

40. Parker et al. (2013)

41. 6. On the horizon

42. Environmental metagenomics

43. Models of computation • Cloud resources: Unlimited flexibility, finite time • Development trade-off • Off-the-shelf • Bespoke • Exploratory work • Real time genomic transects? • Essential fundamental data missing from nearly every system; • Diversity; structure; substitution rates; dN/dS; recombination; dispersal; lateral transfer

44. Serialisation • Process data remotely • Freeze-dry objects, download to desktop • Implement new methods directly on previously-analysed data

45. 7. Over the horizon • Real-time phylogenetics • Field phylogenetics • Alignment-free analyses

46. Conclusions • Why phylogenomics? • Practice • Comparative approach • Statistical context

47. Thanks Steve Rossiter1, James Cotton2, Elia Stupka3 & Georgia Tsagkogeorga1 1School of Biological and Chemical Sciences, Queen Mary, University of London 2Wellcome Trust Sanger Institute 3Center for Translational Genomics and Bioinformatics, San Raffaele Institute, Milan Chris Walker & Dan Traynor Queen Mary GridPP High-throughput Cluster Chaz Mein & Anna Terry Barts and The London Genome Centre Mahesh Pancholi School of Biological and Chemical Sciences BBSRC (UK); Queen Mary, University of London

48. Resources • My email: Joe Parker (Queen Mary University of London): j.d.parker@qmul.ac.uk • Parker, J., Tsagkogeorga, G., Cotton, J.A., Liu, Y., Provero, P., Stupka, E. & Rossiter, S.J. (2013) Genome-wide signatures of convergent evolution in echolocating mammals. Nature502(7470):228-231 doi:10.1038/nature12511. • Tsagkogeorga, G., Parker, J., Stupka, E., Cotton, J.A., & Rossiter, S.J. (2013) Phylogenomic analyses elucidate evolutionary relationships of the bats (Chiroptera) Curr. Biol. in the press. • Salichos, L. & Rokas, A. (2013) Inferring ancient divergences requires genes with strong phylogenetic signals. Nature437:327-331. doi:10.1038/nature12130 • Backström, N., Zhang, Q. & Edwards, S.V. (2013) Evidence from a House Finch (Haemorhous mexicanus) Spleen Transcriptome for Adaptive Evolution and Biased Gene Conversion in Passerine Birds. MBE30(5):1046-50. doi:10.1093/molbev/mst033 • Lindblad-Toh, K., Garber, M., Zuk, O., Lin, M.F., Parker, B.J., et al. (2011) A high-resolution map of human evolutionary constraint using 29 mammals. Nature478:476–482 doi:10.1038/nature10530 • Degnan, J.H. & Rosenberg, N.A. (2009) Gene tree discordance, phylogenetic inference and the multispecies coalescent. TREE24:(6)332-340 doi:10.1016/j.tree.2009.01.009 • The Tree Of Life: http://phylogenomics.blogspot.co.uk/ • RNA-seq For Everyone: http://rnaseq.uoregon.edu/index.html • Evo-Phylo: http://www.davelunt.net/evophylo/tag/phylogenomics/ • OpenHelix: http://blog.openhelix.eu/ • Our blogs: http://evolve.sbcs.qmul.ac.uk/rossiter/ (lab) and http://www.lonelyjoeparker.com/?cat=11 (Joe)