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Phyloinformatics of Neuraminidase
at Micro and Macro Levels using
Grid-enabled HPC Technologies
B. Schmidt (UNSW)
D.T. Singh (Genvea Biosciences)
R. Trehan, T. Bretschneider (NTU, Singapore)
March 26, 2007
Contents
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H5N1 Genetics
H5N1 Phyloinformatics
Design Principles of Quascade
H5N1 Phyloinformatics with Quascade
Results
Conclusion and Future work
March 26, 2007
H5N1 Genetics
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Belongs to the Influenza A virus type
Segmented RNA genome
8 genes, 11 proteins
Classification based on:
– Hemagglutinin (HA): 15 subtypes
– Neuraminidase (NA): 9 subtypes
• Genetic variations in HA/NA
• Genetic drift
– Point mutations
– 1918 Spanish flu
• Genetic shift
– Reassortment of the segmented
genome
– 1957, 1968, 1997 pandemics
– 2003 Z strain of H5N1
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H5N1 Phyloinformatics
• Essential to monitor new emerging strains
– Molecular evolution at gene and genome level
– Phylogenetic analysis for determining the origin of new strains
• Phylogenetics
– How fast do proteins evolve?
– What is the best method to measure the evolution?
– How to obtain the best phylogenetic tree?
• Phylogenetic algorithms
– Character based
• Maximum Parsimony, Maximum Likelihood (ML)
– Distance based
• UPGMA, Neighborhood Join (NJ)
– Bayesian MCMC based
• Mr. Bayes, BEAST
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Quascade – User Interface Example
Processing
pipeline
Communication
• A data-flow tool in which each black-box represents Java objects
running on different computers!
• Assignment of objects to available computers done automatically
(manually if required)
• Communication between objects done transparently
• Configuration of objects done before run-time
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Object Features
Java
Object
Java
Object
Java
Object
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Coding in regular Java/ C/ C++
Persistent – activated whenever all data-inputs present
No explicit messaging protocol required
No distributed computing concepts need to be understood
Objects automatically or manually assigned to computers / CPU-cores
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Phyloinformatics Workflow with Quascade
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Parallelized Phyloinformatics Workflow
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Data and Algorithms
• Core Group
– 22 H5N1 NA sequences from SwissProt and TREMBL
• Medium Set
– 581 NA H5N1 sequences from Uniprot
• Large Set
– 909 NA Influenza A sequences from Uniprot
• ProtDist
– NJ
– UPGMA
• ProtPars
• ProtML
• Mr. Bayes
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Runtime and Scalability (NA Bird Flu Protein)
1 processor
25 processors
400
360
300
200
145
100
16
0
909
sequences
6
581
sequences
Processing time [h]
Processing time [h]
400
Distance-based workflow
MP workflow
360
300
200
140
100
16
5
909
sequences
581
sequences
0
March 26, 2007
P18269Sial
Q05JH9H9N2
Q6DTU0swinech03
Mr Bayes –
Tree Core Set
0.75
A1EHP1goBav06
0.99
A1EHP3goBa06
Q0A2H3Chsc59
1.00
Q710U6chSc59
1.00
Q0PEF9chIn06
0.99
Q0PEG0chIn06
Q5MD56TiTh04
Q6PUP6HuTh04
0.63
Q307V5catth04
Q5SDA6chTh04
0.71
Q45ZM8wpfth04
Q307U7PigeonTh04
Q6PUP7HuTh04
0.90
Q2L700HuTh05
0.70
0.91
Q2LDC0QuTh06
0.54
Q2LDC8chTh05
Q6B518chTh04
0.86
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Q4PKD4chTh04
Analysis and Observations
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Clustering possibilities
– Temporal, host-based, geographical
Algorithms
– Mr. Bayes and ProtML are most consistent in their performance
– Too compute-intensive for the larger “macro” sets
Observed pattern
– All phylograms yielded geographic-based clustering rather than timebased clustering
– Host ranges along clustered clades vary
– Same strain with identical NA sequences can infect different hosts
– NA may not be the sole factor responsible for determining the diverse
host range
– Glycan site acquisition or loss seems to play a critical role in the
molecular evolution of H5N1 NA
– Identification of “bridging isolates” may help in rapid monitoring and
development of global scale warning system for H5N1
March 26, 2007
Conclusion and Future Work
• Quascade
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New graphical data-flow tool to design automatically grid-enabled
pipelines / workflows
Supports implicit high-performance parallelization
Supports persistent components
Can be used with Java / C/ C++ code or application-binaries
• H5N1 Phyloinformatics
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Can take advantage of workflow system and HPC
Can be easily used and modified by biologists
Use H5N1 NA sequences to better understand evolution of H5N1
Analysis of H5N1 NA data with different algorithms indicates spatial
clustering based on geographical distribution rather than temporal or
host.
• Future work
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Studies in conjunction with other proteins such as HA, Polymerase
etc., and also at gene and genome level
March 26, 2007