Cheetham, J. (James J.)

Dehne, F. (Frank)

Pitre, S. (Sylvain)

Rau-Chaplin, A. (Andrew)

Taillon, P.J. (Peter J.)

Carleton University's Institutional Repository

Parallel CLUSTAL W For PC ClustersJames Cheetham1, Frank Dehne2, Sylvain Pitre3Andrew Rau-Chaplin4, Peter J. Taillon51 Institute of Biochemistry, Carleton University,Ottawa ON Canada, jcheetha@ccs.carleton.ca2 School of Computer Science, Carleton University,Ottawa, ON Canada, frank@dehne.net3 School of Computer Science, Carleton University,Ottawa, ON Canada, spitre@scs.carleton.ca4 Faculty of Computer Science, Dalhousie University,Halifax, NS Canada, arc@cs.dal.ca5 School of Computer Science, Carleton University,Ottawa, ON Canada, ptaillon@scs.carleton.caAbstract. This paper presents a parallel version of CLUSTALW, calledpCLUSTAL. In contrast to the commercial SGI parallel Clustal, whichrequires an expensive shared memory SGI multiprocessor, pCLUSTALcan be run on a range of distributed and shared memory parallel ma-chines, from high-end parallel multiprocessors (e.g. Sunfire 6800, IBMSP2, etc.) to PC clusters, to simple networks of workstations. We haveimplemented pCLUSTAL using C and the MPI communication library,and tested it on a PC cluster. Our experimental evaluation shows thatour pCLUSTAL code achieves similar or better speedup on a distributedmemory PC clusters than the commercial SGI parallel Clustal on a sharedmemory SGI multiprocessor.Key Words:Multiple Sequence Alignment, CLUSTALW, Parallel Com-puting, Computational Biochemistry.1 IntroductionThe alignment of DNA or protein sequences is by far the most common taskin Bioinformatics. Procedures relying on sequence comparison are diverse andrange from database searches and studies of evolution to protein structure pre-diction. Pairwise alignment of sequences is the most simple form of sequencealignment and is mainly used for searching sequence databases. More than twosequences can also be aligned, and this multiple sequence alignment has manyuses. Sequences can be aligned along their entire length (global alignment) oronly in certain regions (local alignment). Global and local alignment can beused for both pairwise and multiple alignments. Global alignments use gaps (in-sertions/deletions) while local alignments don’t have to and only align regionsbetween gaps (Figure 1). A global alignment compares the two sequences overtheir entire lengths, and is appropriate when comparing sequences that are ex-pected to share similarity over their entire lengths. Global alignment maximizesregions of similarity and minimizes gaps using scoring matrices and gap param-eters set by the user.Protein sequences can be related by homology or convergence. Multiple se-quence alignments are useful in both cases. Homologous proteins, by deﬁnition,Fig. 1. A comparison of local and global sequence alignments.have a common ancestor and usually a common function. Converged proteinshave evolved independently and have similar amino acid sequences and usuallysimilar structures and/or functions. The helix-turn-helix DNA binding domainis an example of convergence in archael, eubacterial and eukaryotic DNA bindingproteins.The alignment of novel DNA or protein sequences with well characterizedhomologous sequences can provide information on the potential functions of thenovel sequences. A multiple alignment of protein sequences determines the po-sition and nature of conserved regions in each member of the group. Conservedamino acid sequences usually correspond to structurally and/or functionally im-portant parts of a protein. It is a common occurrence that we only know thestructure or function for one or two members of a group of protein sequences.Constructing multiple alignments allows us to infer structures and functions forother members of the group and to generate hypotheses about the functionalimportance of speciﬁc sequences which can be tested experimentally.Phylogenetic analysis provides a conceptual framework for understandingevolution but involves several computational challenges. The generation of evo-lutionary trees can be seen as two distinct NP-complete problems: multiple se-quence alignments and phylogenetic tree searchs. Phylogenetic analysis of se-quence data depends strongly on accurate multiple alignments (Figure 2). Inaddition, there are other problems, such as orthologs and paralogs. Orthologsare sequences derived from a common ancestor through vertical descent. In moredirect terms, this means the same gene in diﬀerent species. Paralogs are geneswithin the same genome that have been generated by duplication. Distinguish-ing between orthologs and paralogs is important is we hope to build accuratephylogenetic trees.One objective of multiple sequence alignments can be the generation of fullyannotated phylogenetic trees. (An example is shown in Figure 2.) This is diﬃcultfor two reasons. (1) To considers all possible multiple sequence alignments andthen, all possible phylogenetic trees and pick the best one, would be impossible.Thus, most phylogenetic programs use previously aligned sequences. (2) Theresult will be inﬂuenced by the criteria used to determine the best tree.There are three common types of tree building algorithms: distance matrix,maximum likelihood and parsimony. Distance matrix methods estimate pairwisedistances between the sequences (which reduces the information in the alignmentto a single number). Other methods build several trees from the information inthe multiple alignment and select the best tree. The guide tree in CLUSTALis derived from the distances between pairwise aligned sequences. These dis-tances may not be equal to the distances between sequence pairs in the multiplesequence alignment.Fig. 2. An example phylogenetic tree constructed from a multiple sequence alignment.The tree was made using protein sequences from the synapsin family. Bootstrap valuesare percentages for 1000 trials. The tree was calculated using a multiple alignment fromthe ClustalW program and drawn with the TreeView program.As the number of DNA and protein sequences in databases increases, it isincreasingly important to be able to create multiple sequence alignments forvery large numbers of sequences. Using standard multiple sequence alignmenttools like Clustal W [10] (see Figure 3) a set of 100 sequences can be alignedin under an hour on a fast workstation. However, given that the underlyingalignment algorithm requires O(n2) steps, where n is the number of sequencesto be aligned, it is not surprising that these standard tools soon begin to takemany hours to run.In this paper, we describe a parallel version of Clustal W, called pCLUSTAL,that can be run a range of distributed memory parallel machines, from high-endparallel multiprocessors (e.g. Sunﬁre, IBM SP, SGI Origin, etc.), to PC clusters,to simple networks of workstations. We have implemented pCLUSTAL using CFig. 3. CLUSTAL W.and the MPI communication library, and tested it on a PC cluster. Our exper-iments, presented in Section 4, investigate the speedup and scalability of ourmethod on gene sequences obtained from the National Center for BiotechnologyInformation.A parallelization of Clustal for shared memory SGI multicomputers, likethe SGI Origin 3000, was previously developed by the SGI ChemBio group [8].Their implementation was based on SGI OpenMP shared memory compilers andruns only on shared memory SGI machines. They report achieving a maximumspeedups of 10 on 16 processor machines [8].The main contribution of this paper is to provide an eﬃcient distributedmemory implementation of CLUSTAL W that can be run on a wide range ofdistributed memory PC clusters and parallel multicomputers. Our experimentalevaluation shows that our pCLUSTAL code achieves similar or better speedupon distributed memory PC clusters than the SGI parallel Clustal on a sharedmemory SGI Origin 3000.The remainder of this paper is organized as follows. Section 2 reviews themultiple sequence alignment problem and sequential Clustal W algorithm. InSection 3, we present a parallelization of CLUSTAL W for distributed memorymulticomputers. Section 4 presents the experimental performance results for ourpCLUSTAL code on a PC cluster, and Section ?? concludes the paper.2 Review: Sequential CLUSTAL WCLUSTAL W has become the most popular algorithm for multiple sequencealignment [4, 5, 3, 10, 6, 9, 7]. This program implements a progressive method formultiple sequence alignment. A high level description of the three basic phasesin the CLUSTAL W alignment algorithm are given in Algorithm 1.Algorithm 1 Sequential CLUSTAL WInput: A set S of n sequences. Output: A multiple alignment of S.(1) Pairwise alignment: Compute pairwise alignments for all sequences againstall other sequences and store the result in a similarity matrix. Convert thevalues in the sequence similarity matrix to distance measures which reﬂectthe evolutionary distance between each pair of sequences.(2) Guide-tree: Construct a guide-tree which deﬁnes the order in which pairsof sequences are aligned and combined with previous alignments using thesequence similarity matrix and a neighbour-joining algorithm.(3) Multiple alignment: Align progressively following guide tree. Start by align-ing most closely related pairs of sequences and at each step align two se-quences or one to an existing subalignment.— End of Algorithm —ScoringThere are two main types of scoring used in CLUSTAL W: pairwise scores andmultiple alignment scores.When aligning two sequences, scoring matrices (e.g. PAM250 or BLOSUM62)are used in order to determine a similarity score for matches and mismatches foreach amino acids or nucleotides. A penalty is incurred when gaps are inserted,as well as smaller penalties for extending those gaps. The pairwise score betweena pair of sequences is calculated as the sum of similarity scores for all alignedpairs of characters minus the gap penalties introduced in either sequences. Thesescores are necessary for building the guide tree used in the multiple alignmentphase.The multiple alignment score is a sum-of-pair score or SP [1]. For an align-ment of N sequences, each of M columns (length), we will denote the i-th columnin the alignment by Ai1, Ai2, ..., AiN . We deﬁne Pijk = 1 for every pair Aij andAik which are aligned with each other and Pijk = 0 otherwise. The score Si forthe i-th column isSi =N∑j=1,j =kN∑k=1Pijkand the SP for the alignment isSP =∑Mi=1 Si∑Mri=1 Sri.Here Mr is the number of columns in the reference alignment and Sri is thescore Si for the i-th column in the reference alignment. Other attempts havebeen made to improve this scoring method, such as Circular tours and TravelingSalesman Problem [2].3 Parallel CLUSTAL for Distributed MemoryMulticomputerIn the following, we describe a parallelization of the tree basic phases of theCLUSTAL W alignment algorithm. Our method assumes a set of p processors,P0, P1, . . ., Pp−1 where each processor.Algorithm 2 pCLUSTALInput: set of sequences, 〈S〉. Output: guide tree, TS; alignment, AS .(1.0) Processor P0 reads the sequence set S. It then determines the mapping ofsequence-pairs to processors, based on the number of processors, p, and thenumber of sequences, n = |S|.(1.1) Processor P0 sends the precomputed subset of sequences to the correspond-ing processors, Pi, 1 ≤ i ≤ p− 1.(1.2) Each processor Pi, 0 ≤ i ≤ p − 1, performs a pairwise alignment on itsassigned sequences using the CLUSTAL W sequential algorithm.(1.3) Each processor Pi, 1 ≤ i ≤ p − 1, sends its relative pairwise alignmentscores to P0.(2.0) Processor P0 builds the alignment guide tree, TS , using the scores gatheredin Step (1.3).(3.0) Processor P0 analyzes TS to identify sequence pairings that can be eval-uated independently in Step (3.1). Sequences that can be evaluated inde-pendently are assigned to processors, Pi, 1 ≤ i ≤ j − 1.(3.1) Processor P0 gathers resulting pairs evaluated in Step (3.0) and, using TS ,sequentially completes the multiple alignment of the sequences, AS .— End of Algorithm —CorrectnessThe correctness of Algorithm pCLUSTAL follows from the correctness of thesequential CLUSTAL W. The reported results are identical.PerformanceWe assume that n is the number of input sequences, each of which is boundedin length by m, and that p is the number of processors.Phase One: Step (1.0) requires O(nm) time to read the sequence ﬁle andO(n2) time to map the sequence pairs to the processors. In Step (1.1), speciﬁcsubsets of sequences are sent to speciﬁc processors requiring O(1) h-relation op-erations (MPI AllToAllv). Each processor sends it scores to P0 in Step (1.3),using O(1) h-relation operations. In Step (1.2), each processor performs a se-quential pairwise alignment on its sequence subsets, requiring O(n2m2/p) time.Phase Two: Building the guide tree in Step (2.0) requires O(n) time. Thereis no communication required in this phase.Phase Three: The analysis of TS in Step (3.0) requires O(n) time. Themultiple alignment operation in Step (3.1) requires O(nm2) time. The dispersaland gathering of independent candidate pairs in Steps (3.0) and (3.1) requireO(1) h-relation operations.Algorithm Complexity: From the previous analysis, the total time forAlgorithm pCLUSTAL is O(n2m2/p) local computation and O(1) h-relation op-erations for communication. This shows the eﬀectiveness, in theory, of AlgorithmpCLUSTAL. The local computation shows linear speedup, and the communica-tion overhead consists only of a small, ﬁxed, number of data permutations. Ourperformance analysis in Section 4 below shows that this property of AlgorithmpCLUSTAL does indeed translate into very good practice performance of ourpCLUSTAL code.4 Experimental Performance AnalysisWe implemented Algorithm 2 (pCLUSTAL), using C and the MPI communica-tion library. For our experiments, we used a 64 node PC cluster with 1.8 GHzIntel Xeon processors, 512 MB RAM per node and 60 GB of disk storage pernode. Every node is running Linux Redhat 7.2 with gcc 2.95.3 and MPI/LAM6.5.6. The nodes are interconnected via a gigabit Ethernet switch. All paralleltimes are measured as the wall clock time between the start of the ﬁrst processand the termination of the last process. We will refer to this as parallel wall clocktime. All times include the time taken to read the input graph from a ﬁle andwrite the solution into a ﬁle. Furthermore, all wall clock times were measuredwith no other user except us on the parallel machine.Our experiments measured the following: (1) Parallel wall clock times as afunction of the number of processors. (2) Relative speedup (ratio of parallel wallclock time on one processor and parallel wall clock time on p processors) as afunction of the number of processors.As test data, we obtained protein sequence data sets from the National Centerfor Biotechnology Information (NCBI). The sequences were, on average, about300 amino acids in length. We used the following sequence sets:– SYNAPSIN: 28 sequences– SYNAPTOTAGMIN: 66 sequences– RELAXIN: 426 sequences– GLUCAGON: 478 sequences– KINASE: 647 sequences– TRYPSIN: 878 sequencesFigures 4 to 6 show the parallel wall clock times as a function of the numberof processors for SYNAPSIN, SYNAPTOTAGMIN, RELAXIN, GLUCAGON,KINASE, and TRYPSIN, respectively. The ﬁgures show the parallel wall clocktimes for the three phases of the algorithm (pairwise alignment, guide tree con-struction, and multiple alignment) as well as the total measured parallel wallclock time. We observe, that all six sets of curves are very similar. The guide treeconstruction and multiple alignment times are small compared to the pairwisealignment times. The pairwise alignment requires time O(n2m2/p) in theory andthe times measured are indeed proportional to 1/p. Furthermore, the pairwisealignment times dominate the guide tree construction and multiple alignmenttimes and, therefore, the total measured parallel wall clock times are similar tothe pairwise alignment times and proportional to 1/p.Figure 7 shows the measured relative speedup (ratio of parallel wall clocktime on one processor and parallel wall clock time on p processors), as a functionof the number of processors, for SYNAPSIN, SYNAPTOTAGMIN, RELAXIN,GLUCAGON, KINASE, and TRYPSIN, respectively. Except for the SYNAPSINcase (small data set), the speedup is very good for up to 8 processors, good forup to 16 processors, and still reasonable for up to 32 processors.Figure 8 is taken from [8] and shows the speedups reported in [8] for the com-mercial SGI parallel CLUSTAL on a shared memory SGI Origin 3000. Despitethe much more powerful machine, the speedups reported are similar or less thanthe speedups obtained by pCLUSTAL on a much simpler distributed memoryPC cluster. 0 2 4 6 8 10 12 14 16 18 0  10  20  30  40  50  60  70Time (wall clock)Number of ProcessorsPairwiseGuide TreeMultiple AlignmentTotal 0 5 10 15 20 25 30 35 40 45 0  10  20  30  40  50  60  70Time (wall clock)Number of ProcessorsPairwiseGuide TreeMultiple AlignmentTotal(a) (b)Fig. 4. Parallel Wall Clock Times Measured For SYNAPSIN And SYNAPTOTAG-MIN. 0 20 40 60 80 100 120 140 0  10  20  30  40  50  60  70Time (wall clock)Number of ProcessorsPairwiseGuide TreeMultiple AlignmentTotal 0 50 100 150 200 250 300 0  10  20  30  40  50  60  70Time (wall clock)Number of ProcessorsPairwiseGuide TreeMultiple AlignmentTotal(a) (b)Fig. 5. Parallel Wall Clock Times Measured For RELAXIN And GLUCAGON.References1. H. Carillo. and D. Lipman. The multiple sequence alignment problem in biology.SIAM J. Appl. Math, 48(5):1073–1082, 1998. 0 1000 2000 3000 4000 5000 6000 0  10  20  30  40  50  60  70Time (wall clock)Number of ProcessorsPairwiseGuide TreeMultiple AlignmentTotal 0 1000 2000 3000 4000 5000 6000 7000 0  10  20  30  40  50  60  70Time (wall clock)Number of ProcessorsPairwiseGuide TreeMultiple AlignmentTotal(a) (b)Fig. 6. Parallel Wall Clock Times Measured For KINASE And TRYPSIN.2. G.H. Gonnet, C. Korostensky, and S. Benner. Evaluation measures of multiplesequence alignments. J Comput Biol., 7(1-2):261–276, 2000.3. H.G. Higgins, A.J. Bleasby, and R. Fuchs. Clustal v: improved software for multiplesequence alignment. CABIOS, 8:189–191, 1992.4. H.G. Higgins and P.M. Sharp. Clustal: a package for performing multiple sequencealignment on a microcomputer. Gene, 73:237–244, 1988.5. H.G. Higgins and P.M. Sharp. Fast and sensitive multiple sequence alignments ona microcomputer. CABIOS, 5:151–153, 1989.6. H.G. Higgins, J.D. Thompson, and T.J. Gibson. Using clustal for multiple sequencealignments. Methods Enzymol, 266:383–402, 1996.7. F. Jeanmougin, J.D. Thompson, M. Gouy, D.G. Higgins, and T.J. Gibson. Multiplesequence alignment with clustal x. Trends Biochem Sci, 23:403–405, 1998.8. Performance optimization of clustal w: Parallel clustal w, ht clustal, and multi-clustal. http://www.sgi.com/ industries/ sciences/ chembio/ resources/ papers/Clustal DevNews/ Clustal DevNews.html.9. J.D. Thompson, T.J. Gibson, F. Plewniak, F. Jeanmougin, and H.G. Higgins. Theclustalx windows interface: flexible strategies for multiple sequence alignment aidedby quality analysis tools. Nucleic Acids Research, 24:4876–4882, 1997.10. J.D. Thompson, H.G. Higgins, and T.J. Gibson. Clustal w: improving the sen-sitivity of progressive multiple sequence alignment through sequence weighting,positions-specific gap penalties and weight matrix choice. Nucleic Acids Research,22:4673–4680, 1994. 0 10 20 30 40 50 60 70 0  10  20  30  40  50  60  70SpeedupNumber of ProcessorsSynapsin (28)Synaptotagmin (66)Relaxin (426)Glucagon (478)Kinase (647)Trypsin (878)LinearFig. 7. Relative Speedup For pCLUSTAL On A PC Cluster As Measured In Figures4 To 6Fig. 8. Relative Speedup of SGI’s parallel CLUSTAL on a SGI Origin 3000. From[8]: “Parallel Clustal W scaling. Calculation was done for 100 and 600 GPCR proteinsequences with the average length 390 amino acids.”

Parallel CLUSTAL W for PC clusters

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Parallel CLUSTAL W for PC clusters

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