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A Separate Analysis Approach to the Reconstruction of Phylogenetic Networks. Luay Nakhleh Department of Computer Sciences UT Austin. Who’s Involved. UT CS : Tandy Warnow, Luay Nakhleh UT BIO : Randy Linder UNM CS : Bernard Moret. Why Networks?. Lateral gene transfer (LGT)

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a separate analysis approach to the reconstruction of phylogenetic networks

A Separate Analysis Approach to the Reconstruction of Phylogenetic Networks

Luay Nakhleh

Department of Computer Sciences

UT Austin

who s involved
Who’s Involved
  • UT CS: Tandy Warnow, Luay Nakhleh
  • UT BIO: Randy Linder
  • UNM CS: Bernard Moret
why networks
Why Networks?
  • Lateral gene transfer (LGT)
    • Ochman estimated that 755 of 4,288 ORF’s in E.coli were from at least 234 LGT events
  • Hybridization
    • Estimates that as many as 30% of all plant lineages are the products of hybridization
    • Fish
    • Some frogs
phylogenetic networks
Phylogenetic Networks
  • Rooted, directed, acyclic graphs that actually model the evolutionary process
  • “tree” nodes and “network” nodes
  • Time constraints
separate analysis
Separate Analysis
  • Analyze individual genes separately
  • Reconcile the resulting phylogenies
  • As opposed to combined analysis in which the datasets are combined (via concatenation) and the combined dataset is then analyzed
spr distances among gene trees
SPR Distances Among Gene Trees

A

B

C

D

E

SPR Distance 1

A

B

C

D

E

A

B

C

D

E

maddison s method
Maddison’s Method

Given two gene datasets

  • Construct two gene trees T1 and T2
  • If SPR(T1,T2)=0
    • Return a tree
  • If SPR(T1,T2)=1
    • Return a network with one reticulation event

Open problem: extend to reconstructing a network with m reticulation events

challenges
Challenges

(1) Computational

  • Computing SPR distances is of unknown computational complexity (probably hard)
solving the computational challenge
Solving the Computational Challenge
  • Galled-networks: reticulation events are independent
  • For two gene trees T1 and T2 on n leaves we can
    • Decide whether SPR(T1,T2)=m in O(mn) time, and
    • Construct network N from T1 and T2 in O(mn) time
challenges1
Challenges

(2) Systematic

  • Obtaining the correct gene trees in practice is very hard (due to missing data, inaccuracy of tree reconstruction methods, wrong assumptions, etc.)
solving the systematic challenge our method spnet
Solving the Systematic Challenge: Our MethodSpNet

Given the sequences of two genes I & II on a set of species

  • Run MP or ML on gene I and obtain a set U1 of trees, represented by its consensus tree t1
  • Run MP or ML on gene II and obtain a set U2 of trees, represented by its consensus tree t2
  • Find binary trees T1 and T2, that refine t1 and t2, respectively, and such that SPR(T1,T2)=1
  • Build network N from T1 and T2
spnet running time
SpNet: Running Time
  • We have a linear-time algorithm for the single hybrid case (implementation and experimental results are available as well)
  • We are working on the general case of arbitrary number of reticulation events
experimental study
Experimental Study
  • Generated random networks on 10 and 20 taxa, with 0, 1, and 2 hybrids
  • Evolved sequences under the GTR+Gamma model of evolution with invariant sites
  • We studies the topological accuracy based on the splits defined by the model and inferred network
evaluation criteria
Evaluation Criteria
  • Detection Quality
    • How often did the method infer the correct number of hybrids in the model phylogeny?
  • Reconstruction Quality
    • What is the topological accuracy of the inferred phylogeny?
methods
Methods
  • SpNet(i): Our method where we contract i edges
  • NNet: The method of Bryant and Moulton
  • NJ
conclusions
Conclusions
  • Considering a set of “good” trees rather than a single optimal tree is advantageous in network reconstruction
  • Separate analysis approaches outperform combined analysis approaches
ongoing research
Ongoing research
  • Using other techniques for obtaining unresolved trees (e.g., Bayesian analyses, bootstrapping, etc.)
  • Detection vs. reconstruction – visualization and clustering techniques may also be useful (collaboration with St John)
  • Refining unresolved networks
  • DCM-like network reconstruction