Summary: Clann has been developed in order to provide
methods of investigating phylogenetic information through the
application of supertrees.
Availability: Clann has been precompiled for Linux, Apple
Macintosh and Windows operating systems and is available
from http://bioinf.may.ie/software/clann. Source code is
available on request from the authors.
Supplementary information: Clann has been written in
the C programming language. Source code is available on
request

Creevey, C.J.

McInerney, J.O.

English

MURAL - Maynooth University Research Archive Library

BIOINFORMATICS APPLICATIONS NOTE Vol. 21 no. 3 2005, pages 390–392doi:10.1093/bioinformatics/bti020Clann: investigating phylogenetic informationthrough supertree analysesC. J. Creevey∗ and J. O. McInerneyBioinformatics and Pharmacogenomics Laboratory, Department of Biology,National University of Ireland, Maynooth, Co. Kildare, IrelandReceived on March 11, 2004; revised on September 3, 2004; accepted on September 6, 2004Advance Access publication September 16, 2004ABSTRACTSummary: Clann has been developed in order to providemethods of investigating phylogenetic information through theapplication of supertrees.Availability: Clann has been precompiled for Linux, AppleMacintosh and Windows operating systems and is avail-able from http://bioinf.may.ie/software/clann. Source code isavailable on request from the authors.Supplementary information: Clann has been written inthe C programming language. Source code is available onrequest.Contact: chris.creevey@may.ieThe aim of constructing phylogenetic supertrees is to combinethe information contained in source trees with partially over-lapping leaf-sets. Supertree methods can combine the inform-ation from trees with no taxa in common as long as additionaltrees that overlap both exist. Increasingly many methods forsupertree construction exist (see Bininda-Emonds et al., 2002for a review), and there is a need for a tool that permits theexploration of the congruence across the input data and thequality of the hypotheses that are derived from the data. In thismanuscript we report one such software product. Some desir-able properties of supertrees have been described elsewhere(Wilkinson et al., 2004), however, no method is guaranteedto have all these properties. As a result we need to explore thedata and trees using a variety of methods, each with differentproperties. This amounts to a sensitivity analysis to examinewhich hypotheses of relationships are most frequently sup-ported by the different methods and therefore more likely tobe the correct relationships.At present there are four supertree methods implementedin Clann: Matrix Representation using Parsimony (MRP);Most Similar Supertree (MSSA) (Creevey et al., 2004); Max-imum Quartet Fit (QFIT) and Maximum Splits Fit (SFIT).With MRP, the Baum and Ragan coding scheme, which isadditive and binary, is used to create a matrix from the set ofsource trees (Baum, 1992; Ragan, 1992). This matrix consists∗To whom correspondence should be addressed.of rows representing each taxon and columns representingeach internal branch from each source tree. Each internalbranch of a source tree divides the taxa into two groups(those descended from the branch versus those ancestral to it).Scoring the taxa with either ‘1’ or ‘0’ according to the groupin which they are found represent the hypotheses of relation-ships defined by each internal branch. If a taxon is not presentin a source tree, it is scored with a ‘?’. Parsimony analysesare then used to reconstruct the supertree from this data. Theparsimony step must be carried out by ‘PAUP*’ (Swofford,2002) as Clann writes a nexus formatted file containing theMRP coding scheme and commands for PAUP* to carry outthe analysis.The MSSA scoring method compares each source treeseparately to the supertree by comparing the path lengthdistance matrix (Steel and Penny, 1993) derived from asource tree to another distance matrix derived from a prunedsupertree. The differences between the matrices are scoredand the sum of the scores from all the comparisons is calcu-lated. The user can choose to impose several weightingschemes on this score to adjust for the influence of differ-ential tree size. The weighted or un-weighted sum is the scoreassigned to the supertree. This sum is used as an optimality cri-terion to determine the supertree that best fits the set of sourcetrees. This method is related to the average consensus method(Lapointe and Cucumel, 1997) with branch lengths set to unityand as such is also related to MRP (Lapointe et al., 2003).With both the QFIT and SFIT method, each source treeis individually compared to a proposed supertree by deter-mining all the quartets (relationships between any four taxa)(QFIT) or splits (components) (SFIT), respectively for boththe source tree and appropriately pruned supertree. A scoreis then calculated which is defined by the number of quartetsor splits that are shared between the supertree and the setof source trees. The sum of the scores calculated for all thesource trees is used as an optimality criterion to determine theoptimal supertree (the supertree that shares the most quartetsor splits with the set of source trees).For each of the optimality criteria, several differentmethods of searching tree-space and analysing the underlying390 Bioinformatics vol. 21 issue 3 © Oxford University Press 2004; all rights reserved.Clann: investigating phylogenetic informationphylogenetic information are implemented in Clann. Thesemethods include complete exhaustive searches of tree-space,heuristic methods of searching tree-space (though not forMRP), methods of bootstrapping the trees to examine theunderlying support for any hypothesis and methods fordetermining whether any phylogenetic signal present in thedata is better than would be expected from random data.Two heuristic algorithms for searching supertree-space areimplemented in Clann. They are nearest neighbour inter-change (NNI) and sub-tree pruning and re-grafting (SPR) asdescribed and implemented in PAUP* (Swofford, 2002).Bootstrapping is a statistical technique for empiricallyestimating the variability in an estimate. It assumes that thesamples are independent and identically distributed (Efron,1979). In a phylogenetic context, bootstrapping allows theestimation of support for a phylogeny. This can be extended tothe supertree context as implemented in Clann, by consideringthe source trees as one possible set of trees that could have beenused in the analysis. Choosing a slightly different set of sourcetrees may result in a different optimal supertree. In order toestimate the likely nature of the universe of optimal supertrees,the source trees may be bootstrapped. For each bootstrap rep-licate, the source trees are sampled with replacement until anew dataset is created with the same number of source trees asthe original dataset. This means that some source trees may berepresented in the dataset more than once, while others maynot be represented at all. For each repetition, the supertreethat best represents this (bootstrapped) set of source trees,according to the chosen optimality criterion, is determined.Repeating this procedure a large number of times gives anindication as to how much support there is for the clades ina supertree (Purvis, 1995). If during any bootstrap replicatetaxon is not represented (due to the initial low occurrence ofthe taxon), the software will alert the user to the unsuitabilityof the data to bootstrapping and refuse to continue.A randomization method to test the null hypothesis thatthe phylogenetic signal in the source trees is no better thanrandom is also implemented in Clann. This test has beenimplemented for all the supertree methods except MRP wherea normal Permutation Tail Probability (PTP) (Archie, 1989;Faith and Cranston, 1991) test is available. We have calledthis method the YAPTP (Yet Another Permutation Tail Prob-ability) test (Creevey et al., 2004). For each repetition of thetest, each source tree is replaced with a randomly chosentopology for the same leaf-set. This removes any congruentphylogenetic signal between source trees, while leaving thenumbers and sizes of source trees, the frequency with whichany particular taxon was found across the source trees and thefrequency of cooccurrence of any group of taxa within sourcetrees unaltered. A search of tree space can then be carriedout and the score of the best supertree recorded. The user canrepeat this test as many times as required and the distributionof the resulting scores can be compared to the score of thereal data (or the distribution of scores from bootstrapping)to assess if the real data contains a signal that is betterthan random. Permutation tests of this kind are extremelyforgiving in nature. Passing them may however be considereda minimal requirement for any dataset to be considered forfurther analysis.While both bootstrapping and the YAPTP test providemeans of assessing the results of the supertree analysis, itmust be pointed out that such assessments must be regardedwithin the context of what the supertree analysis was trying toachieve and the methods used to achieve them. For instance,was the goal of the analysis to reconstruct a phylogeny, testfor tree-likeness of the data, to assess the support for partic-ular clades or to reconstruct a historical timeline? Then, howdo the methods chosen to carry out these analyses affect theinterpretation of the results? Clann provides a necessary toolto help achieve these and other goals in a supertree context.ACKNOWLEDGEMENTSThe authors would like to thank Dr Mark Wilkinson forreading the manuscript, the four anonymous referees for theirhelpful comments and for the many users who contributedsuggestions and reported bugs in earlier versions of thesoftware.REFERENCESArchie,J.W. (1989) A randomisation test for phylogeneticinformation in systematic data. Syst. Zoo., 38, 239–252.Baum,B.R. (1992) Combining trees as a way of combining datasets for phylogenetic inference, and the desirability of combininggene trees. Taxon, 41, 3–10.Bininda-Emonds,O.R.P., Gittleman,J.L. and Steel,M. (2002) The(super)tree of life: procedures, problems and prospects. Ann. Rev.Ecol. Syst., 33, 265–289.Creevey,C.J., Fitzpatrick,D.A., Philip,G.K., Kinsella,R.J.,O’Connell,M.J., Pentony,M.M., Travers,S.A., Wilkinson,M. andMcInerney,J.O. (2004) Does a tree-like phylogeny only exist atthe tips in the prokaryotes? Proc. R. Soc. Lond. B. Biol. Sci.,(in press).Efron,B. (1979) Bootstrap methods: another look at the jackknife.Ann. Stat., 7, 1–26.Faith,D.P. and Cranston,P.S. (1991) Could a cladogram this shorthave arisen by chance alone? On permutation tests for cladisticstructure. Cladistics, 7, 1–28.Lapointe,F.-J. and Cucumel,G. (1997) The average consensusprocedure: combination of weighted trees containing identical oroverlapping sets of taxa. Syst. Biol., 46 306–312.Lapointe,F.J., Wilkinson,M. and Bryant,D. (2003) Matrixrepresentations with parsimony or with distances: two sides ofthe same coin? Syst. Biol., 52, 865–868.Purvis,A. (1995) A composite estimate of primate phylgoeny.Philos. Trans. R. Soc. Lond. B. Biol. Sci., 348, 405–421.Ragan,M.A. (1992) Matrix representation in reconstructingphylogenetic-relationships among the eukaryotes. Biosystems,28, 47–55.391C.J.Creevey and J.O.McInerneySteel,M. and Penny,D. (1993) Distributions of tree comparisonmetrics—some new results. Syst. Biol., 42, 126–141.Swofford,D.L. (2002) PAUP*. Phylogenetic Analysis UsingParsimony (*And Other Methods). Version 4. Sinauer Associates,Sunderland, MA.Wilkinson,M., Thorley,J.L., Pisani,D., Lapointe,F.-J. andMcInerney,J. (2004) Some desiderata for liberal supertrees.In Bininda-Emonds,O.R.P. (ed.), Phylogenetic Supertrees:Combining Information to Reveal the Tree of Life. KluwerAcademic, Dordrecht.392

Clann: investigating phylogenetic information
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Clann: investigating phylogenetic information through supertree analyses

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