Practical use of combinatorial methods for phylogenetic network reconstruction
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The document summarizes Philippe Gambette's presentation on the practical use of combinatorial methods for phylogenetic network reconstruction. It discusses phylogenetic networks as a way to model evolution when genetic material transfers occur between coexisting species. It motivates the combinatorial reconstruction approach of using clusters and triplets instead of whole trees as input data to make reconstruction faster. Combinatorial reconstruction methods are described that reconstruct networks from softwired clusters or triplets. The document illustrates applications on biological data and discusses challenges like solution ambiguity.
Practical use of combinatorial methods for phylogenetic network reconstruction
- 1. 09/02/2011 - Rencontres Alphy, Lyon Practical use of combinatorial methods for phylogenetic network reconstruction Philippe Gambette
- 2. Outline • Phylogenetic networks • Motivations for the combinatorial reconstruction approach • Combinatorial reconstruction methods • Practical use • Illustrations • Perspectives
- 3. Outline • Phylogenetic networks • Motivations for the combinatorial reconstruction approach • Combinatorial reconstruction methods • Practical use • Illustrations • Perspectives
- 4. Phylogenetic trees Phylogenetic tree of a species set species “tokogeny” tree S A B C A B C
- 5. Genetic material transfer Genetic material transfers between coexisting species: • horizontal gene transfer • hybridization S A B C A B C
- 6. Genetic material transfer Genetic material transfers between coexisting species: • horizontal gene transfer • hybridization species tree S A B C gene G1 A B C A B C network N A B C
- 7. Genetic material transfer Genetic material transfers between coexisting species: • horizontal gene transfer • hybridization gene G1 species tree S A B C A B C incompatible gene trees gene G2 A B C network N A B C A B C
- 8. Phylogenetic networks Phylogenetic network: network representing evolution data • explicit phylogenetic networks to model evolution Simplistic synthesis diagram galled network level-2 network Dendroscope HorizStory • abstract phylogenetic network to classify, visualize data minimum covering network split network median network SplitsTree Network TCS
- 9. Phylogenetic network software Who is Who in Phylogenetic Networks, Articles, Authors & Programs Based on BibAdmin by Sergiu Chelcea + tag clouds, date histogram, journal lists, co-author graphs, keyword definitions. http://www.atgc-montpellier.fr/phylnet
- 10. Explicit phylogenetic networks Phylogenetic network: network representing evolution data • explicit phylogenetic networks evolution model Simplistic galled network level-2 network Dendroscope synthesis diagram HorizStory T-Rex reticulogram
- 11. Explicit phylogenetic networks Rooted explicit phylogenetic network: tree-like parts + blobs. vertices with more than one parent: reticulations h1 h3 h2 a b c d e f g h i j k
- 12. Plan • Phylogenetic networks • Motivations for the combinatorial reconstruction approach • Combinatorial reconstruction methods • Practical use • Illustrations • Perspectives
- 13. Phylogenetic network reconstruction espèce 1 : AATTGCAG TAGCCCAAAAT espèce 2 : ACCTGCAG TAGACCAAT espèce espèce 3 4 : : GCTTGCCG ATTTGCAG TAGACAAGAAT AAGACCAAAT {gene sequences} espèce 5 : TAGACAAGAAT espèce 6 : ACTTGCAG TAGCACAAAAT espèce 7 : ACCTGGTG TAAAAT distance methods G1 G2 Bandelt & Dress 1992 - Legendre & Makarenkov 2000 - Bryant & Moulton 2002 parsimony methods Hein 1990 - Kececioglu & Gusfield 1994 - Jin, Nakhleh, Snir, Tuller 2009 likelihood methods Snir & Tuller 2009 - Jin, Nakhleh, Snir, Tuller 2009 - Velasco & Sober 2009 network N
- 14. Phylogenetic network reconstruction Problem: usually slow, lots of sequences available. espèce 1 : AATTGCAG TAGCCCAAAAT espèce 2 : ACCTGCAG TAGACCAAT espèce espèce 3 4 : : GCTTGCCG ATTTGCAG TAGACAAGAAT AAGACCAAAT {gene sequences} espèce 5 : TAGACAAGAAT espèce 6 : ACTTGCAG TAGCACAAAAT espèce 7 : ACCTGGTG TAAAAT distance methods G1 G2 Bandelt & Dress 1992 - Legendre & Makarenkov 2000 - Bryant & Moulton 2002 parsimony methods Hein 1990 - Kececioglu & Gusfield 1994 - Jin, Nakhleh, Snir, Tuller 2009 likelihood methods Snir & Tuller 2009 - Jin, Nakhleh, Snir, Tuller 2009 - Velasco & Sober 2009 network N
- 15. Phylogenetic network reconstruction espèce 1 : AATTGCAG TAGCCCAAAAT espèce 2 : ACCTGCAG TAGACCAAT espèce espèce 3 4 : : GCTTGCCG ATTTGCAG TAGACAAGAAT AAGACCAAAT {gene sequences} espèce 5 : TAGACAAGAAT espèce 6 : ACTTGCAG TAGCACAAAAT espèce 7 : ACCTGGTG TAAAAT G1 G2 Reconstruction of one tree for each gene present in several species Guindon & Gascuel, SB, 2003 T1 {trees} HOGENOM database Dufayard, Duret, Penel, Gouy, Rechenmann & Perrière, BioInf, 2005 T2 Tree consensus or reconciliation rooted explicit network optimal network N
- 16. Phylogenetic network reconstruction espèce 1 : AATTGCAG TAGCCCAAAAT espèce 2 : ACCTGCAG TAGACCAAT espèce espèce 3 4 : : GCTTGCCG ATTTGCAG TAGACAAGAAT AAGACCAAAT {gene sequences} espèce 5 : TAGACAAGAAT espèce 6 : ACTTGCAG TAGCACAAAAT espèce 7 : ACCTGGTG TAAAAT G1 G2 Reconstruction of one tree for each gene present in several species Guindon & Gascuel, SB, 2003 T1 {trees} HOGENOM database Dufayard, Duret, Penel, Gouy, Rechenmann & Perrière, BioInf, 2005 T2 > 500 species, >70 000 trees Tree consensus or reconciliation rooted explicit network optimal network N Problem: tree reconciliation is difficult even for 2 trees (NP-complete for 2 trees with minimum reticulation number) Bordewich & Semple, DAM, 2007
- 17. Triplets and clusters Problem: Reconstructing the supernetwork of a set of trees is hard. Idea: reconstruct a network containing all: triplets a|ce a b c d e of the input trees ?
- 18. Triplets and clusters Problem: Reconstructing the supernetwork of a set of trees is hard. Idea: reconstruct a network containing all: triplets clusters a|ce {c,d,e} a b c d e a b c d e of the input trees ?
- 19. Softwired clusters “softwired” cluster : cluster of a tree contained in the network Tree-like model of gene transmission: each gene comes from a single parent abc ab cd bc a b c d
- 20. Softwired clusters “softwired” cluster : cluster of a tree contained in the network Tree-like model of gene transmission: each gene comes from a single parent abc ab cd bc a b c d
- 21. Combinatorial phylogenetic network reconstruction Idea: change the type of data to process {trees} {clusters} {triplets} optimal optimal optimal supernetwork N supernetwork N' supernetwork N'' N = N' = N''?
- 22. Combinatorial phylogenetic network reconstruction Idea: change the type of data to process {trees} {clusters} {triplets} optimal optimal optimal supernetwork N supernetwork N' supernetwork N'' { N } ⊆ { N' } ⊆ { N'' }
- 23. Plan • Phylogenetic networks • Motivations for the combinatorial reconstruction approach • Combinatorial reconstruction methods • Practical use • Illustrations • Perspectives
- 24. Reconstruction from softwired clusters {trees} Fast exact galled network reconstruction method from softwired clusters Huson, Rupp, Berry, Gambette & Paul, ISMB 2009 Step 1- Solve cluster conflicts by deleting taxa, reconstruct tree on remaining taxa {clusters} MAXIMUM COMPATIBLE SUBSET a b c d x y Step 2- Attach taxa involved in conflicts to the tree with the minimum number of arcs: MINIMUM ATTACHMENT N' galled network a b x y c d http://www.dendroscope.org
- 25. Reconstruction from softwired clusters {arbres} Exact method for level k network reconstruction from softwired clusters Iersel, Kelk, Rupp & Huson, ISMB 2010 less reticulations, but slower for level > 2. {clusters} level = maximum number of reticulations per blob. a b c d e f g h i j k level-2 network N' level-k network level-1 network (“galled tree”) a b c d e f g h i j k http://www.dendroscope.org
- 26. Reconstruction from triplets {trees} Exact methods to reconstruct level-1 and 2 networks (if there exist any) from a dense triplet set Jansson, Nguyen & Sung, SODA'05 : O(n3) pour niveau 1, van Iersel, Kelk & al, RECOMB'08 : O(n8) pour niveau 2, To & Habib, CPM'09 : O(n5k+4) pour niveau k T dense triplet set = {triplets} On any subset of 3 leaves, T contains at least one triplet Program Simplistic N' Yeast phylogenetic network - level-k Van Iersel et al. : Constructing level-2 phylogenetic network networks from triplets. RECOMB 2008 http://homepages.cwi.nl/~kelk/simplistic.html
- 27. Reconstruction from triplets {trees} Fast heuristic method to reconstruct a level-1 network containing most of the input triplets Huber, van Iersel, Kelk & Suchecki, TCBB, 2011 Program Lev1athan {triplets} Phylogenetic network built from triplets extracted from 2 trees of HIV-1 strains Huber, van Iersel, Kelk & Suchecki A practical algorithm for reconstructing level-1 phylogenetic networks TCBB, 2011 N' level-1 network http://homepages.cwi.nl/~kelk/lev1athan/
- 28. Outline • Phylogenetic networks • Motivations for the combinatorial reconstruction approach • Combinatorial reconstruction methods • Practical use • Illustrations • Perspectives
- 29. Solution ambiguity Ambiguity of the results even with complete and correct data Many distinct minimal networks have exactly the same set of contained trees, triplets, and clusters. a c a c b b b c a Characterization for level-1 networks : The only ambiguous cases have such blobs (< 5 vertices) Gambette & Huber, 2011
- 30. Solution ambiguity Ambiguity of the results even with complete and correct data Many distinct minimal networks have exactly the same set of contained trees, triplets, and clusters. a|bc a c a c b b b c a Characterization for level-1 networks : The only ambiguous cases have such blobs (< 5 vertices) Gambette & Huber, 2011
- 31. Solution ambiguity Ambiguity of the results even with complete and correct data Many distinct minimal networks have exactly the same set of contained trees, triplets, and clusters. c|ab a c a c b b b c a Characterization for level-1 networks : The only ambiguous cases have such blobs (< 5 vertices) Gambette & Huber, 2011
- 32. Solution ambiguity Ambiguity of the results even with complete and correct data Many distinct minimal networks have exactly the same set of contained trees, triplets, and clusters. x1 x1 x2 x2 b a b a 2 level-2 networks with exactly the same triplet set Gambette & Huber, 2011
- 33. Solution ambiguity Ambiguity of the results even with complete and correct data Many distinct minimal networks have exactly the same set of contained trees, triplets, and clusters. x1 x1 x2 x2 b a b a 2 level-2 networks with exactly the same triplet set Even with complete and correct data, impossible to choose among the ambiguous configurations! Gambette & Huber, 2011
- 34. Practical use existing methods still work to do! Conditions for use Available data Possible processings rooted trees unrooted trees Rooting with a reference species tree or topological constraints Single-copy gene trees MUL-trees (with duplicated MUL-tree processing genes) Scornavacca, Berry & Ranwez, 2009 Correct clusters and triplets noisy data Tree cleaning PhySIC_IST, 2008 Data filtering (clusters with high bootstrap value, present in >x% of the trees) Data editing : solution containing most of the input data Complete data (density for partial data, deleted genes Selection of a large number of trees triplet sets, complete clusters) with a large number of common species Selection of the maximal number of taxa with triplet density NP-complete problems
- 35. Outline • Phylogenetic networks • Motivations for the combinatorial reconstruction approach • Combinatorial reconstruction methods • Practical use • Illustrations • Perspectives
- 36. Illustrations 16 trees on 47 taxa from the HOGENOM database Lev1athan (proteobacteria) (heuristic 24 Enterobacteriales triplets, 2 Pasteurellales level-1) 1 Aeromonadales 24 sec. 9 Alteromonadales 1 Oceanospirillales 6 Rhodobacterales 4 Rhizobiales Networks containing triplets, softwired clusters, present in at least 20% of the trees Simplistic Dendroscope (triplets, level-7 (clusters, galled network) network) 63 sec. <1 sec.
- 37. Illustrations 16 trees on 47 taxa from the HOGENOM database Dendroscope (proteobacteria) (clusters, 24 Enterobacteriales cluster 2 Pasteurellales network, 1 Aeromonadales level 1) 9 Alteromonadales <1 sec. 1 Oceanospirillales 6 Rhodobacterales 4 Rhizobiales Networks containing triplets, softwired clusters, present in at least 20% of the trees Dendroscope Dendroscope (clusters,galled (clusters, network) level-7 <1 sec. network) 2 sec.
- 38. Illustrations 9 trees on 279 procaryote species Clusters in at least 2 trees Auch, Steigele, Huson & Henz, 2009 Dendroscope (clusters, galled network) 2 sec.
- 39. Illustrations 23 trees, 45 species from the 3 domains of life Dendroscope clusters with 99% bootstrap confidence (galled network) 4 sec. Data from Brown JR, Douady CJ, Italia MJ, Marshall WE, Stanhope MJ: Universal trees based on large combined protein sequence data sets. Nat Genet 2001, 28:281--285
- 40. Illustrations 23 trees, 45 species from the 3 domains of life Dendroscope clusters with 80% bootstrap confidence (galled present in at least 2 trees network) <1 sec. Data from Brown JR, Douady CJ, Italia MJ, Marshall WE, Stanhope MJ: Universal trees based on large combined protein sequence data sets. Nat Genet 2001, 28:281--285
- 41. Illustrations 23 trees, 45 species from the 3 domains of life Dendroscope clusters with 80% bootstrap confidence (level-3 present in at least 2 trees network) <1 sec. Data from Brown JR, Douady CJ, Italia MJ, Marshall WE, Stanhope MJ: Universal trees based on large combined protein sequence data sets. Nat Genet 2001, 28:281--285
- 42. Outline • Phylogenetic networks • Motivations for the combinatorial reconstruction approach • Combinatorial reconstruction methods • Practical use • Illustrations • Perspectives
- 43. Perspectives Combinatorics : - Better knowledge of small level networks - Update of a network with new data - Unrooted explicit phylogenetic network reconstruction Bioinformatics : - Function of transferred genes (“transfer highways”) - Integration of combinatorial methods in a statistical framework Sequence data Combinatorial reconstruction of a set of candidates Construction of combinatorial data Choice among the candidates By statistical methods Phylogenetic network
- 44. Thank you ! Coauthors: - Vincent Berry, Christophe Paul (LIRMM) - Daniel Huson, Regula Rupp (Tübingen) - Katharina Huber (East Anglia) Brown et al.'s data provided by Sophie Abby Réticulogramme des 25 mots les plus fréquents de ma thèse, construit par TreeCloud, SplitsTree et T-Rex Coloration : rouge au début, bleu à la fin http://www.treecloud.org