Summary: TIBA is a tool to reconstruct phylogenetic trees from rearrangement data that consist of ordered lists of synteny blocks (or genes), where each synteny block is shared with all of its homologues in the input genomes. The evolution of these synteny blocks, through rearrangement operations, is modelled by the uniform Double-Cut-and-Join model. Using a true distance estimate under this model and simple distance-based methods, TIBA reconstructs a phylogeny of the input genomes. Unlike any previous tool for inferring phylogenies from rearrangement data, TIBA uses novel methods of robustness estimation to provide support values for the edges in the inferred tree. Availability: http://lcbb.epfl.ch/softwares/tiba.html. Contact: vaibhav.rajan@epfl.c

Lin, Yu

Moret, Bernard M. E.

Rajan, Vaibhav

RERO DOC Digital Library

Vol. 28 no. 24 2012, pages 3324–3325BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/bts603Phylogenetics Advance Access publication October 11, 2012TIBA: a tool for phylogeny inference from rearrangementdata with bootstrap analysisYu Liny, Vaibhav Rajan*,y and Bernard M. E. MoretLaboratory for Computational Biology and Bioinformatics, EPFL, EPFL-IC-LCBB INJ 230, Station 14, CH-1015Lausanne, SwitzerlandAssociate Editor: David PosadaABSTRACTSummary: TIBA is a tool to reconstruct phylogenetic trees from re-arrangement data that consist of ordered lists of synteny blocks (orgenes), where each synteny block is shared with all of its homologuesin the input genomes. The evolution of these synteny blocks, throughrearrangement operations, is modelled by the uniform Double-Cut-and-Join model. Using a true distance estimate under this modeland simple distance-based methods, TIBA reconstructs a phylogenyof the input genomes. Unlike any previous tool for inferring phyloge-nies from rearrangement data, TIBA uses novel methods of robust-ness estimation to provide support values for the edges in the inferredtree.Availability: http://lcbb.epfl.ch/softwares/tiba.html.Contact: vaibhav.rajan@epfl.chReceived on October 31, 2012; revised on October 2, 2012; acceptedon October 4, 20121 INTRODUCTION‘Rare genomic changes’, such as rearrangements (Rokas andHolland, 2000), cause large-scale structural changes in thegenome, clarify distant or problematic relationships among or-ganisms and have been used in many phylogenetic studies.The first algorithm for phylogeny inference from rearrange-ment data was BPAnalysis (Blanchette et al., 1997). The algo-rithm seeks to reconstruct the tree and ancestral genomes withthe minimum breakpoint distance along each edge of the tree.This approach was extended in GRAPPA (Moret et al., 2001)by using inversion distances. These methods were restricted tounichromosomal genomes; the tool MGR (Bourque andPevzner, 2002) was the first to handle multichromosomal gen-omes. All these parsimony-based approaches must produce goodapproximations to the NP-hard problem of computing the rear-rangement median of three genomes (summarized in Tannieret al., 2008) which limits their scalability. Despite using cleverheuristics, MGR does not scale well, particularly for high reso-lution data. Yet to date, MGR [and its more recent derivativeMGRA (Alekseyev and Pevzner, 2009)] had remained the onlytool available for the analysis of multichromosomal genomicrearrangements.Distance-based methods like neighbour joining (NJ) (Saitouand Nei, 1987), in contrast with parsimony-based methods, runin time polynomial in the number and size of genomes—and fastand accurate heuristics exist for those where the scoring functioncannot be computed in polynomial time, such as FastME(Desper and Gascuel, 2004). Pairwise distances, given as inputto a distance-based method, are usually the edit distances, that is,the minimum-cost distances under the assumed model of evolu-tion. However, an edit distance typically underestimates the truedistance causing poor accuracy of trees inferred fromdistance-based methods. When given true evolutionary distances,NJ provably returns the true tree. The true evolutionary dis-tance—the actual number of evolutionary events between thetwo genomes—is impossible to measure, but it can be estimatedusing statistical techniques. Such distance corrections have longbeen used for sequence data and, more recently, for rearrange-ment data. For multichromosomal genomes, we have designedsuch an estimator (Lin and Moret, 2008) and have demonstratedthe accuracy and scalability of a reconstruction method that usesNJ with this distance estimator (Lin et al., 2011).A major shortcoming of phylogeny reconstruction from re-arrangement data has been the lack of any way to assess therobustness of the inferred edges. In phylogenetic analysis fromsequence data, such an assessment is de rigueur and is carried outby an adaptation of the standard non-parametric tests—the boot-strap and the jackknife, first proposed by Felsenstein(Felsenstein, 1985). Recently, we have designed several new meth-ods for statistically assessing the robustness of trees reconstructedfrom rearrangement data (Lin et al., 2011, 2012). Through care-ful and extensive experiments, we have shown that our bootstrap-ping approach for rearrangement data is on par with the classicphylogenetic bootstrap used in sequence-based reconstruction.Combining these methods with our distance based reconstructionmethod, we provide the first tool for phylogeny inference fromrearrangement data that is accurate, scalable and provides boot-strap support values for the edges of the tree.2 METHODSRearrangement data for a genome consists of lists of syntenic blocks(genes are an example) in the order in which they are placed along oneor more chromosomes. Each syntenic block is identified by a marker,which is shared with all (or most) of its homologues in the genomes understudy, and a sign, which represents the strandedness of the syntenic block.The markers typically used for syntenic blocks are integers. Any two ad-jacent syntenic blocks can be represented by a set of two integers—we callthis an adjacency. A telomere in a linear chromosome is represented by a*To whom correspondence should be addressed.yThe authors wish it to be known that, in their opinion, the first twoauthors should be regarded as joint First Authors.3324  The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.comsingleton set containing just the end marker. Thus, a genome is repre-sented by a set of adjacencies and telomeres. Any rearrangement oper-ation changes up to three adjacencies or telomeres in the genome. Formultichromosomal genomes, all the rearrangement operations can bemodelled by a single operation called ‘Double-Cut-and-Join’ (DCJ)(Yancopoulos et al., 2005).For reconstruction, we use either NJ or FastME. The pairwise dis-tances used are estimates of the true evolutionary distances under a modelof evolution that assumes uniform distribution of DCJ operations. Theevolutionary model is used to infer an estimate of the true distance byderiving the effect of a given number of DCJ operations on the number ofshared adjacencies and telomeres and numerically inverting the derivationto produce a maximum-likelihood estimate of the true distance under themodel. See (Lin and Moret, 2008) for details. Our extensive experimentson simulated and real datasets, described in (Lin et al., 2011), show thatthe error rates of trees reconstructed by NJ using this distance estimator is510% in all but the oversaturated cases. With FastME, the error rates areeven lower. Further, error rates are significantly reduced by an increase inthe size of the genome—because the larger number of syntenic blocksreduces the relative error in the estimated distances. Trees can be recon-structed on up to 500 genomes, each containing up to 10000 markerswithin a few seconds on a PC.As described in (Lin et al., 2012), we design and test several bootstrap-ping methods that can be used with distance-based reconstruction fromrearrangement data. The fact that our distance estimator computes theestimated true distance between two genomes based only on the numberof shared adjacencies in each genome allows us to design sampling meth-ods for bootstrapping that can handle replicate genomes that may beinvalid (e.g. because of additional copies of adjacencies), and yet be suf-ficient for computing the pairwise distance (by tallying the number ofshared adjacencies). Two of these methods, BC and PJ are equivalent intheir performance and are better than all the other methods designed.Their names come from their equivalent counterparts in the sequenceworld: the classic bootstrap (BC) of Felsenstein and parsimony jackknif-ing (PJ).The key idea behind these bootstrapping methods is to create replicatesby sampling adjacencies; from the list of all possible adjacencies, BCsamples with replacement to form a collection of adjacencies; only adja-cencies in this collection are then used to count the number of sharedadjacencies and then estimate the pairwise distances. PJ is (asymptotic-ally) equivalent to sampling with replacement (as in BC), but withoutovercounting, that is, when sampling gives an adjacency that has beenpreviously selected, it is not added to the replicate. In other words, se-lected adjacencies are not counted more than once for computing thenumber of shared adjacencies between leaf genomes. From each replicate,a tree is reconstructed using our true distance estimator and NJ orFastME. The bootstrap support of an edge (viewed as a bipartition ofleaves) in the inferred tree is the proportion of the trees from replicatesthat contain the edge (the same bipartition of leaves). Both BC and PJshow high sensitivity even at high levels of specificity, making them ex-cellent bootstrap methods. We have also demonstrated, in (Lin et al.,2012), that they outperform jackknifing methods based on sampling mar-kers, such as (Huang et al., 2010; Shi et al., 2010).3 SOFTWARETIBA is implemented in Cþþ and can be compiled and executedon the command line in any UNIX-based platform and in theCygwin environment on Windows. To run a phylogenetic ana-lysis, the program must be run with the following input param-eters: the input filename, the output filename, the reconstructionmethod and the bootstrap method. The input file format is thesame as that used by GRAPPA and MGR; FASTA like headersfor the names of the genomes (‘4’ followed by an alphanumericsequence followed by a newline), each chromosome representedby a signed permutation of integers ending with a ‘$’ symbol anda newline character. The output filename provided by the user issuffixed with ‘_1’ and ‘_2’ to create two output files, both inNewick format; the first contains the inferred tree with branchlengths, and the second contains the same inferred tree with sup-port values replacing the branch lengths. The reconstructionmethod can be either NJ or FastME. The two bootstrap methodsdiscussed earlier in the text are implemented. They can be speci-fied by ‘BC’ or ‘PJ’. The default number of replicates is 100, butthe user can change this with an additional input. Installationand usage details, with examples, are provided in the packageand on our website.4 CONCLUSIONTIBA is fast, scalable, accurate and provides support values forthe edges in the inferred tree. Fast and scalable distance-basedmethods, precise estimates of true pairwise distances and finally,for the first time in any rearrangement-based phylogeny infer-ence method, the use of bootstrap scores—together make TIBAunique.Conflict of Interest: none declared.REFERENCESAlekseyev,M. and Pevzner,P. (2009) Breakpoint graphs and ancestral genome re-constructions. Genome Res., 19, 943–957.Blanchette,M. et al. (1997) Breakpoint phylogenies. In Miyano,S. and Takagi,T.(eds.) Genome Informatics. University Academy Press, Tokyo, pp. 25–34.Bourque,G. and Pevzner,P. (2002) Genome-scale evolution: reconstructing geneorders in the ancestral species. Genome Res., 12, 26–36.Desper,R. and Gascuel,O. (2004) Fast and accurate phylogeny reconstruction algo-rithms based on the minimum-evolution principle. J. Comput. Biol., 9, 687–705.Felsenstein,J. (1985) Confidence limits on phylogenies: an approach using the boot-strap. Evolution, 39, 783–791.Huang,Y.-L. et al. (2010) Sort2: a tool for sorting genomes and reconstructingphylogenetic trees by reversals, generalized transpositions and translocations.Nucleic Acids Res., 38, W221–W227.Lin,Y. and Moret,B. (2008) Estimating true evolutionary distances under the DCJmodel. Bioinformatics, 24, i114–i122.Lin,Y. et al. (2011) Fast and accurate phylogenetic reconstruction from high-resolution whole-genome data and a novel robustness estimator. J. Comput.Biol., 18, 1131–1139.Lin,Y. et al. (2012) Bootstrapping phylogenies inferred from rearrangement data.Algorithms Mol. Biol., 7, 21.Moret,B. et al. (2001) A new implementation and detailed study of breakpointanalysis. Pac. Symp. Biocomput., 583–594.Rokas,A. and Holland,P. (2000) Rare genomic changes as a tool for phylogenetics.Trends Ecol. Evol., 15, 454–459.Saitou,N. and Nei,M. (1987) The neighbor-joining method: a new method for re-constructing phylogenetic trees. Mol. Biol. Evol., 4, 406–425.Shi,J. et al. (2010) Using jackknife to assess the quality of gene order phylogenies.BMC Bioinformatics, 11, 168.Tannier,E. et al. (2008) Multichromosomal genome median and halving problems.In Proceedings of the 8th International Workshop on Algorithms inBioinformatics (WABI’08). Vol. 5251, Springer, pp. 1–13.Yancopoulos,S. et al. (2005) Efficient sorting of genomic permutations by trans-location, inversion and block interchange. Bioinformatics, 21, 3340–3346.3325TIBA

TIBA: a tool for phylogeny inference from rearrangement data with bootstrap analysis

Abstract

Similar works

Full text

Available Versions

RERO DOC Digital Library