High-throughut comparative genomics

24th October 2013Joe Parker,

Queen Mary University London

Topics

1. Introduction2. Background: why phylogenomics?3. Examples4. Practice5. Case study6. On the horizon7. Over the horizon

Aims

• Context of phylogenomics: Next-generation sequencing (NGS)

• Why phylogenomics?• Practical analyses• Future developments

1. Our Research

Lab Interests

• Ecology and evolution of traits• Echolocation, sociality• NGS data for population genetics and phylogenomics

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

2. Background

Next-generation sequencing

Why phylogenomics, not -genetics?

• Causes of discordant signal– Incomplete lineage sorting– Lateral transfer– Recombination – Introgression

Quantitative biology

• Multiple configurations

• Hyperparameters empirically investigated

• Determine sensitivity of results

Distributions

• Genome-scale data provides context

• Identify outliersGenes / taxa / trees

• Compare values across biological systems

Integration with ‘Omics

• Multiple databases

• Functional data

• Bibliographic information

3. Example studies

Tsakgogeorgia et al. (in press)

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Salichos & Rokas (2013)

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Backström et al. (2013)

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Lindblad-Toh et al. (2011)

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4. Practice

Source material

• Samples• Storage• Purification• Library prep

Sequencing

• Genome– Sanger– Illumina – Pyro /454– SOLiD– PacBio

• Transcriptome / RNA-seq– MyBAITS

• HiSeq / MiSeq• IonTorrent

Infrastructure

• Desktop machines• Computing clusters• Grid systems• Cloud-based computation

Assembly, Annotation

• Assembly– To reference (mapping)– De novo

• Annotation– By homology– De novo

•SOAPdenovo•MAKER•Velvet•Bowtie / Cufflinks / Tophat•Trinity

Alignment

• PRANK• MUSCLE• MAFFT• Clustal

Phylogeny inference

• MrBayes• RAxML• BEAST• MP-EST• STAR

Phylogenetic analysis

• BEAST• HYPHY• PAML• Pipelines• LRT

5. Case study

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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

Our pipeline for detecting genome-wide convergence

mean = 0.05

mean = 0.05 mean = -0.01 mean = -0.08

Development cycle

Design

Wireframe & specify

tests

Implement

AlignmentloadSequences()

getSubstitutions()

PhylogenytrimTaxa()getMRCA()

DataSeriescalculateECDF()randomise()

RegressiongetResiduals()

predictInterval()

Review, refine & refactor

Parker et al. (2013)

Parker et al. (2013)

6. On the horizon

Environmental metagenomics

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

Serialisation

• Process data remotely

• Freeze-dry objects, download to desktop

• Implement new methods directly on previously-analysed data

7. Over the horizon

• Real-time phylogenetics• Field phylogenetics• Alignment-free analyses

Conclusions

• Why phylogenomics?• Practice• Comparative approach• Statistical context

ThanksSteve Rossiter1, James Cotton2, Elia Stupka3 & Georgia Tsagkogeorga1

1School of Biological and Chemical Sciences, Queen Mary, University of London2Wellcome Trust Sanger Institute

3Center for Translational Genomics and Bioinformatics, San Raffaele Institute, Milan

Chris Walker & Dan TraynorQueen Mary GridPP High-throughput Cluster

Chaz Mein & Anna TerryBarts and The London Genome Centre

Mahesh PancholiSchool of Biological and Chemical Sciences

BBSRC (UK); Queen Mary, University of London

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. 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/

• 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)