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

2. Topics 1.Introduction 2.Background: why phylogenomics? 3.Examples 4.Practice 5.Case study 6.On the horizon 7.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 = 0.05mean = -0.01mean = -0.08

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

39. 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 Rossiter 1, James Cotton 2, Elia Stupka 3 & Georgia Tsagkogeorga 1 1 School of Biological and Chemical Sciences, Queen Mary, University of London 2 Wellcome Trust Sanger Institute 3 Center 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.ukj.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. Nature 502(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. Nature 437:327- 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. MBE 30(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. Nature 478:476–482 doi:10.1038/nature10530 Degnan, J.H. & Rosenberg, N.A. (2009) Gene tree discordance, phylogenetic inference and the multispecies coalescent. TREE 24:(6)332-340 doi:10.1016/j.tree.2009.01.009 The Tree Of Life: http://phylogenomics.blogspot.co.uk/http://phylogenomics.blogspot.co.uk/ RNA-seq For Everyone: http://rnaseq.uoregon.edu/index.htmlhttp://rnaseq.uoregon.edu/index.html Evo-Phylo: http://www.davelunt.net/evophylo/tag/phylogenomics/http://www.davelunt.net/evophylo/tag/phylogenomics/ OpenHelix: http://blog.openhelix.eu/http://blog.openhelix.eu/ Our blogs: http://evolve.sbcs.qmul.ac.uk/rossiter/ (lab) and http://www.lonelyjoeparker.com/?cat=11 (Joe)http://evolve.sbcs.qmul.ac.uk/rossiter/http://www.lonelyjoeparker.com/?cat=11