Fast Algorithms for Minimum Evolution

Fast Algorithms for Minimum Evolution

Fast Algorithms for Minimum Evolution Richard Desper, NCBI Olivier Gascuel, LIRMM Overview I. II. III. IV. V. Statement of phylogeny reconstruction problem and various approaches to solving it. Tree length formula as a function of average distances. Greedy algorithms for tree building and tree swapping. Simulation results. A few extras regarding consistency and branch lengths. Phylogeny Reconstruction General problem: reconstruct the evolutionary

history for a set L of extant species. Input: multiple sequence alignment for L or matrix of estimates of pairwise evolutionary distances. Output: weighted phylogeny representing history of L and common ancestors. Methods Likelihood methods: model-based likelihood maximization. Parsimony methods: minimize total number of mutations in tree. Distance methods: fit tree structure to inferred evolutionary distances. Leading methods include Felsenstein-Fitch-Margoliash weighted least-squares and Neighbor-Joining and its variants. Felsenstein-FitchMargoliash Least-squares Method FITCH searches the space of topologies by iteratively adding leaves and by tree swapping. Edge weights and topology are chosen to

minimize the sum of squares ( is the input metric, T is the induced tree metric): T 2 ij ij i, j 2 ij If ij = 1 for all i and j, this is called the ordinary least-squares method. Minimum Evolution Developed by Rzhetsky and Nei (1992) as a modification of the OLS method For each topology , Define function l assigning OLS lengths to edges of Define size of tree

l (T ) eE ( T ) Choose minimizing l() l (e) Recursive Definition of A|B If A = {a}, B = {b}, A|B = ab, For B B1 B2 , A|B A B1 B2 A|B1 A|B2 B1 B2 B1 B2

B1 B2 All average distances for all pairs of non-intersecting subtrees of a given topology can be calculated in O(n2) time. External OLS Edge Length Function If e is the edge connecting the leaf i to the subtrees A and B, i e A B 1 l (e) Ai| B|i A|B 2 Internal OLS Edge Length Function The length of the edge e is (Vach, 1988) A

e B C D 1 l (e) ( A|C B|D ) (1 )( A|D B|C ) ( A|B B|C ) 2 where | A || D | | B || C | . (| A | | B |)(| C | | D |) Tree length formula Lemma: with T as to the right, A let rX denote the root of subtree X, and eX the edge to X for X { A, B, C , D}. C Then,

B e A|B A|rA l (eA ) l (eB ) B|rB D Tree Length Formula With T as in prior slide, l (T ) l (e) l ( X ) l (e ) X X A, B ,C , D Using lemma and branch length formula for l(e), AC| B|D 1 l (T ) l ( X ) X |rX (1 ) A|D B|C

2 X A, B ,C , D A|B C|D General approach To search the space of topologies, well keep in memory two data structures: Sizes of each subtree of given topology Matrix of average distances X|Y for X,Y disjoint subtrees in given topology As we move from one topology to another, well update the matrix, but only as much as needed, in an efficient manner. Tree Swapping by NNI A B

A e C C e D B D NNI swapping is a basic step in topology building and searching Tree Length Formula With T as in prior slide, l (T ) l (e) l ( X ) l (e ) X

X A, B ,C , D Using lemma and branch length formula for l(e), AC| B|D 1 l (T ) l ( X ) X |rX (1 ) A|D B|C 2 X A, B ,C , D A|B C|D Tree Length after NNI Given the tree swap in prior slide, l the edge length function: (1) ( 1)( AC| B|D )

1 ' ' l (T ) l (T ) ( 1)( A|B C|D ) 2 ' ( )( A|D B|C ) where and are constants depending on the topologies. OLS: FASTNNI Pre-compute average distances between nonintersecting sub-trees. (O(n2) computations) 2. Loop over all internal edges, select the best swap using Equation (1). (O(n)) 3. If no swap improves length of the tree, stop and return the tree, else perform the best swap and update the matrix of average distances and repeat Step 2. (O(n) per swap; there is only one new split.) Thus, if we require p swaps, the total complexity of FASTNNI is O(n2 + pn). 1. Balanced Minimum

Evolution Gascuel (2000) observed that the OLS/ME method was weaker than NJ in approximating the correct topology. Pauplin (2000) to simplify tree length computation proposed to use a balanced version of Minimum Evolution, weighting each sub-tree equally when calculating averages: if A and B are sub-trees of , with B B1 B2 , 1 T 1 T T A|B A|B1 A|B2 2 2 BNNI 1. 2. Calculate balanced averages of all pairs of sub-trees. (O(n2)) Calculate improvement for each swap using (2)

l (T ) l (T ') 1 TA| B CT | D TA|C TB| D 2 If no tree swap improves length of the tree, stop and return tree, else update matrix of average distances and repeat Step 2. (O(n diam(T)) per swap) The average complexity, when performing p swaps, is O(n2 + pn diam(T)). 3. Updating Subtree Averages T x Here, X A... A X

y ...and B C D Y C e If we perform the B-C tree swap, then we T must recalculate X |Y B Y D Q: How many recalculations? Typical values for diam(T): (Hint: you can count (x,y) pairs). Yule-Harding distribution: O (log n)

A: O(n diam(T)) Uniform distribution: O n Building trees from scratch We have NNI algorithms for OLS and balanced branch lengths. But what if we have no initial topology for NNIs? OLS: Greedy Minimum Evolution Start with three-taxon tree T3 2. For k=4 to n, 1. a) Calculate k|A for each subtree A in Tk-1 b) Express cost of inserting k along edge e as f(e). (Use Equation (3) on the next slide.) c) Choose e minimizing f. Insert k along e to form Tk. d) Update matrix of average distances between every pair of 2-distant subtrees.

GME runs in O(n2) running time Greedy Minimum Evolution We use a variant of Equation (1), where D = {k}. Let L = l(T). C C k T T A B Then (3) k A

B ' ( k | A B|C ) 1 l (T ' ) L ( ' 1)( A|B k |C ) 2 (1 )( A|C k | B ) Balanced Minimum Evolution Same as GME,except: 2. (modifications) d) e) f) Calculate balanced average distances instead of ordinary average distances Use = to find weights for insertion points Must keep average distances for all pairs of subtrees.

BME runs in O(n2 diam(T)) running time. Simulations Created 24- and 96-taxon trees, 2000 per each size, Yule-Harding process ( molecular clock). Edge lengths multiplied by (1.0 + X), where X is exponentially distributed. Generated trees with three rates of evolution SeqGen used to generate sequences for each tree and rate (12,000 in all) DNADIST used to calculate distance matrices Results: topological distances 24-Taxa Trees - Slow Rate of Evolution 0.120 0.115 with FastNNI without NNIs 0.110

with BNNI 0.105 0.100 U R T E FI J R N LS O O /W HB BI J G N EI W

H C T NJ BM E M G E HG F T/ P BNNI improved all input trees

Results: topological distances 96-Taxa Trees - Fast Rate of Evolution 0.120 0.110 with FastNNI without NNIs 0.100 with BNNI 0.090 0.080 0.070 U TR E / NJ FI

H TC E W J R N O O BI HB IG NJ BM E M G E

HG F T/ P This improvement is large with fast rates and high numbers of taxa Results: topological distances 24-Taxa Trees - Slow Rate of Evolution 0.120 0.115 with FastNNI without NNIs 0.110 with BNNI 0.105

0.100 U R T E FI J R N LS O O /W HB BI J G N EI W H C

T NJ BM E M G E HG F T/ P NNI trees are close to the best possible for BME Results: topological distances 24-Taxa Trees - Slow Rate of Evolution

0.120 0.115 with FastNNI without NNIs 0.110 with BNNI 0.105 0.100 U R T E FI J R N LS O

O /W HB BI J G N EI W H C T NJ BM E M G E HG

F T/ P The quality of the NNI tree is (mostly) independent of starting point Results: topological distances 96-Taxa Trees - Slow Rate of Evolution 0.2 0.195 0.19 with FastNNI 0.185 without NNIs 0.18

with BNNI 0.175 0.17 FASTNNI trees comparable to NJ as n grows to 96 Computational Times in (MM:SS) 24 Taxa 96 Taxa 1000 Taxa 4000 Taxa GME + BNNI 0.0263 0.0842 11.3390

06:02.1 HGT/FP 0.0252 0.1349 13.8080 03:33.1 NJ/BIONJ 0.0630 0.1628 21.2500 20:55.9 WEIGHBOR

0.4244 26.8818 FITCH 4.3745 Computations done on Sun Enterprise E4500/E5500 running Solaris 8 on 10 400-Mhz processors with 7 Gb memory. Average number of NNIs 24 Taxa 96 Taxa 1000 Taxa 4000 Taxa GME + FASTNNI 1.244 8.446

44.9 336.50 GME + BNNI 1.446 11.177 59.1 343.75 BME + BNNI 1.070 6.933 29.1 116.25

We see that the average number of NNIs is considerably lower than the number of taxa. BME = WLS Why does the balanced approach work so well? Pauplins formula for the length of a tree is 1 pT ( i , j ) l (T ) 2 ij , i j Where pT(i,j) is the length of the (i,j) path in T. BME is a weighted least squares approach with ij cpT (i, j ). Distantly related taxa see their importance decrease exponentially. Bonus features BME is a consistent method. As observed distances converge to true distances, the true

topology becomes the minimum evolution tree. The BNNI tree has no negative branch lengths. A negative value to the branch length function implies a NNI leading to a smaller tree. Consistency of Balanced ME Theorem: Suppose S is a weighted tree, and is a tree topology incompatible with S. Let T be the tree of topology with weights determined by the balanced scheme. Then l(T) > l(S). Lemma: it suffices to prove the case when S is a split metric. Balanced ME consistency Basic idea: let l be the tree length function on the space of topologies. We find a sequence of topologies, T=T0, T1, ... Tk=S such that Each Ti+1 can be reached from Ti via one of two simple topological transformations

l(Ti) > l(Ti+1) for all i. Proof structure modeled after OLS/ME proof (Rzhetsky and Nei, 1993). Type I transformation Color the leaves black or white according to the split metric S. A Type I transformation uses a NNI to form a larger monochromatic cluster D D A C B A B C This transformation reduces the size of the tree under l

Type II transformation A Type II transformation uses two NNIs to form two monochromatic subtrees A1 A1 B1 A2 C C A2 B2 B1 B2 This transformation also reduces the value of the size of the tree under l

Positive Branch Lengths after BNNI B Recall that the length of an edge is described by A e C 11 l (e) ( A|C B|D A|D B|C ) ( A|B C|D ) 22 D We do not perform the l (T ) l (T ') i.e. BC switch because

1 TA| B CT | D TA|C TB| D 0, 2 A|C B|D A|B C |D . Similarly, A|D B|C A|B C |D . Thus l (e) 0 Conclusions BME + BNNI runs in O((n2 + pn) diam(T)), outputs trees comparable to (better than) FITCH, Weighbor, BioNJ, or NJ. FastME is faster than NJ or its variants. BNNI consistently improved output trees in all settings, even when WLS/Fitch trees were input. BNNI outputs tree without negative branch lengths. FASTME software available at http://www.ncbi.nlm.nih.gov/ CBBResearch/Desper/FastME.html or http://www.lirmm.fr/~w3ifa/MAAS/.

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