Christian ArnoldBioinformatics Group, University of Leipzig

Bioinformatics HerbstseminarOctober 23th, 2009

Three Weeks of Experience at the formatics Institute

Content

1. The 10kTrees Project

2. Phylogenetic Targeting

3. Acknowledgements

1. The 10kTrees Project

Goals

• Updated primate phylogeny that includes phylogenetic uncertainty– Use newest available sequence data, include as

much primate species as possible, and update regularly

– Produce a set of >=10,000 primate-wide trees (with branch lengths) that are appropriate for taxonomically broad comparative research on primate behavior, ecology and morphology using Bayesian methods

• Make it accessible to other researchers

Methodology

1. Download sequences from

GenBank

2. Select the longest available sequence

for each gene in each species

3. Create individual fasta file with all

available sequences for each gene

7. Improve alignment quality using GBLOCKS

6. Create MSA using Muscle

4. Create availability matrix

9. Concatenate sequences and create partitioned dataset in

MrBayes format

5. Identify species with non-overlapping

genes

8. Identify best substitution model for

each gene

10. Run MrBayes 12. Update website11. Evaluate MrBayes analysis and calculate

consensus tree

Version 1 vs. Version 2Version 1 Version 2

Species 187 231

Genes

4 mitochondrial (COI, COII, CYTB and ND1) and 1 autosomal gene (SRY)

6 mitochondrial (12S rRNA, 16S rRNA, COI, COII, CYTB, cluster of other mitochondrial genes) and 3 autosomal genes (SRY, CCR5, MC1R)

Genetic loci 2 4

Total No. of Sites 5134 ~9000

Collected sequences413 out of 935 total(55.8% missing data)

1007 out of 2079 total(51.6% missing data)

No. of constraints 29 1

Generations 8 millions 60 millions

Computing time~ 48 days (16 processors in parallel, ~ 3 days each)

~ 2 years (32 processors in parallel, ~ 3 weeks each)

Preliminary consensus treeGreen: Cercopithecines

Blue: Hominoids

Red: Platyrrhines

Yellow: Tarsiers

Brown: Strepsirrhines

Rooted with Galeopterus variegatus

The 10kTrees Websitehttp://10ktrees.fas.harvard.edu/

Current Progress

• Submitted to Evolutionary Anthropology, in press.

• Will be presented at the AAPA conference (April 2010) in Albuquerque, New Mexico

• Version 2 is almost finished

• Available at http://10kTrees.fas.harvard.edu

Summary

• Bayesian approach is time-consuming, but works well, even though data matrix is very sparse

• Increased number of sequences in Version 2 dramatically reduces need for constraints and improves quality of tree and branch lengths estimates

• Ongoing project

• Total number of downloaded trees since June 2009: 95800

2. Phylogenetic Targeting

Which species should we study?

For which species should we collect data in order to increase the size of comparative data sets ?

Goals

?

Example 1/2

• Hypothesis: Two characters (x and y) show correlated evolution

• Goal: Test this hypothesis comparatively (e.g. by using phylogenetically independent contrasts and correlation tests)

• Problem 1: Data has been only collected for x, but not for y

• Solution 1: Collect data for y and test hypothesis

• Problem 2: From which species should we collect data for y?

• Solution 2: Phylogenetic targeting!?

Example 2/2

Brain size

Cognitive data

4 ?

9 7

10 ?

3 ?

2 ?

s3

s1

s4

s5

s2

Collecting new data is time-consuming and expensive…

Methods• Systematically generate all possible pairwise comparisons

• For every pairwise comparison, calculate character differences for the two species that form the pair and assign a score

• Determine set of phylogenetically independent pairs that maximizes the sum of all selected pair scores (maximal pairing)

s3

s4

s2

s6

s1

s7

s5

Maximal pairing: Example

Time complexity: , for balanced trees:)( 3nO

Decomposition of the maximal pairing

)log( 22 nnO

)(maxmax

)(

)(

RsubtreesRR

Tdesc

TSS

SS

Simulation results 1/2

• Random (Rnd) selection of species– Type 1 errors close to nominal level– Power: ~40%, independent of number of taxa– Uses 67% of available variation

• Phylogenetic targeting (PT) induced selection of species– Type 1 errors close to nominal level– Power: 67-81%, increases with number of taxa– Uses 89% of available variation

Detecting correlated character evolution, based on selection of 12 species

Simulation results 2/2

PT Rnd 12 18 24

Number of selected species

Fra

cti

on

of

availab

le v

ari

ati

on

aft

er

sam

plin

g

PT Rnd PT Rnd PT Rnd

Current Progress

• A revised version will be resubmitted to American Naturalist in the not too distant future

• TODO: Extend simulations and clarify some issues

• Available at http://phylotargeting.fas.harvard.edu

Summary

• A focused selection of species can save valuable time and money

• Phylogenetic targeting provides a very flexible approach and can address different questions in the context of limited resources

• Dynamic programming algorithms are everywhere

3. Acknowledgements

• Harvard University

• Max-Planck Institute for Evolutionary Anthropology

• University of Leipzig

• Charlie Nunn

• Luke Matthews

• Peter F. Stadler

Thanks!

Thank you for your attention!

Questions?

If not: Cheers (it’s early, but not too early…)

Any Questions?