CMSC 838 Presentation
Authors : Dmitri Mikhailov, Haruna Cofer, Roberto Gomperts
SGI

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Multiple Sequence Alignment (MSA)
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Basis for phylogenetic analysis - Infer homology relationships
Building protein families - conserved region may imply common function
Aids in function/structure prediction of new proteins
Global MSA – Clustal W
Is it computationally expensive ? Yes, for 100 sequences.
Goal : Parallelize Clustal W
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Clustal W takes hours for 100 or more sequences
Parallelization possible for the algorithm
Contribution of the paper
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Parallel Clustal W
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Parallel version of basic Clustal W
HT Clustal
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Parallelize heterogeneous Multiple Sequence Alignment problems
 MULTICLUSTAL
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Parallel version of an optimization on Clustal W
CMSC 838T – Presentation

 Overview of talk
 Motivation
 Background
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Sequential Clustal W
 Parallel Clustal W
 HT Clustal
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Problem Statement
Optimizations
 MULTICLUSTAL
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Sequential Algorithm
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Optimizations
 Observations
CMSC 838T – Presentation

 Sequential Clustal W Algorithm
 Given N sequences of length M each
 Pairwise Alignment (PA)
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Creates distance matrix N x N based on pairwise alignment scores
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Evolutionary distance
 Guide Tree (GT) construction (Phylogenetic tree)
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Use Neighbor-joining algorithm
 Progressive Multiple Alignment (PA)
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Use guide tree to align closely related pairs of sequences
Progressively align next sequence to existing alignment
CMSC 838T – Presentation

 Problem Statement
 Parallelize the Sequential Clustal W
 Execution time breakup
 PW = pairwise alignment, GT = guide tree, PA = progressive alignment
CMSC 838T – Presentation

 Pairwise Alignment Stage
 N(N-1)/2 pairwise alignments
 Send them randomly to different processors
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Random – as jobs of different load
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Random also produces statistically uniform distribution
(over a large set of jobs)
 1.8X speedup achieved on a 1000 sequence MSA with 8 CPUs
 Guide Tree Stage
 Parallelize “find closest neighbors from distance matrix”
 Used in the neighbor joining algorithm
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Find minimum element of each row concurrently
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Use this to find minimum element of matrix
CMSC 838T – Presentation

 Progressive Alignment Stage
 Computation of a function score(I,J) precomputed in parallel
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Alignment score of sequence I and J
 Not much parallelization in the third stage
 Overall Speedup
 Speedup of 10x for 600 MA sequences using 16 CPUs
 Time reduced from 1 hr 7 minutes to 6.5 minutes
 Relative scaling is better for larger inputs
CMSC 838T – Presentation

 Problem Statement
 Calculate large numbers of MSAs of various sizes (independent problems)
 Such problems seen in high-throughput (HT) research environments
 Representative Problem (from paper) :
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Perform independent MSA over
100 sets of sequences
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Each set has between 20 to
100 sequences with average of 60 sequences
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Average Length of sequence = 390
CMSC 838T – Presentation

 Basic Idea
 Each MSA operation (on one set of sequences) is independent of the other
 Run ClustalW as a uniprocessor job on one MSA problem
 Launch multiple Clustal W jobs on different processors
 Job Scheduling
 Jobs of different duration – depends on sequence set
 Two scheduling options explored:
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Schedule dynamically – if processor is free, schedule an
MSA job – chosen randomly
Schedule dynamically – Sequences are presorted (based on filesize)
CMSC 838T – Presentation

 Speedups
 Almost linear speedups
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31x on 32 CPUs for the representative MSA problem
116X on 128 CPUs for a larger test case
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Solution time reduced from 18.5 hours to 9.5 minutes
 Speedup shown for the example MSA set:
CMSC 838T – Presentation

 Effect of presorting
 Figure shows effect of presorting for the example
MSA set
32 CPUs, 100 sets,
~3 jobs per CPU
 If average number of jobs per CPU < 5 presorting helps
 For larger number of jobs per CPU statistical averaging reduces load imbalance
CMSC 838T – Presentation

 MULTICLUSTAL Algorithm
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A Perl script to generate high quality MSA with little user intervention
Searches for best combination of Clustal W input parameters
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To reduce gaps, increase clustering
Parameters to vary :
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Scoring matrices : pairwise and multiple
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Gap open and extension penalties (pairwise and multiple)
Sequential Algorithm :
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Till all parameters are sufficiently varied { alignment = Run Clustal W ()
Calculate quality of alignment
Change Parameters }
Quality of alignment
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A numerical quantity based on
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 identitical amino acid matches
Conservative amino acid substitutions
Gap events, amino acid islands I.e. –X-, -XX-, -XXX-, -XXXX-
CMSC 838T – Presentation

 Optimization on MULTICLUSTAL
 Run Clustal W once
 Reuse tree generated in the PW/GT Stages
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Guide tree calculated only once for multiple runs
Results in speedups from 1.5X to 3X
 Use Parallel Clustal W for each run of Clustal W
CMSC 838T – Presentation

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Parallelizability
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First (pairwise alignment) and second (guide tree) stages are parallelizable
Third stage is mostly sequential – speedup limited
100 sequence MSAs possible ?
 PIR at NBRF (Georgetown University) takes maximum of 20 sequences for MSA
 Speedup improves user response, for 20 sequences a PC would be sufficient
 Probable applications:
 Research Environments ?
 PIR servers ?
 Speedup only on shared memory SGI 3000 workstation ?
CMSC 838T – Presentation