Poxviruses, Biodefense and Bioinformatics

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Poxviruses, Biodefense and Bioinformatics

• Working towards a better understanding of viral
  pathogenesis and evolution

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Bioinformatics

• Managing Complexity
• Technology development
• Enhancing Understanding
• Research

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Managing Complexity

• Data
• Acquisition
• Storage
• Manipulation
• Retrieval

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Managing Complexity

• Data Analysis
• Development and Utilization of
• Analytical tools
• Visualization tools

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Enhancing Understanding

• What distinguishes one organism from another?
• Sequence
• Molecular Biology
• Physiology
• Pathogenesis
• Epidemiology
• Evolution
• Will the genomic sequence provide an explanation
  for the differences?

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What is Bioinformatics?

• Computer-aided analysis of biological information
• Discerning the characteristic (repeatable)
  patterns in biological information that help to
  explain the properties and interactions of
  biological systems.
• Caveat
• In the end, bioinformatics (a.k.a. computers) can
  only help in making inferences concerning
  biological processes.
• These inferences (or hypotheses) have to be
  tested in the laboratory

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The Poxvirus Bioinformatic Resource
PBR

• www.poxvirus.org

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PBR Collaborators

• UAB
• Elliot Lefkowitz
• St. Louis University
• Mark Buller
• University of Victoria
• Chris Upton
• ATCC
• Charles Buck
• Medical College of Wisconsin
• Paula Traktman

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The UAB MGBF ContingentMolecular and Genetic
Bioinformatics Facility

• Programmers
• Jim Moon
• Don Dempsey
• Uma Dave
• Bei Hu
• Students
• Chunlin Wang
• Fellows
• Shankar Changayil
• Xiaosi Han

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Poxviruses

• Large dsDNA genome
• 150,000 300,000 base pairs
• 150 260 genes
• Complex virion morphology
• Cytoplasmic replication
• Array of immunoevasion strategies.
• Human pathogens
• Molluscum contagiosum
• Variola
• Monkeypox

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The PBR is Designed to Support

• Basic and applied research on Poxviruses
  including the development of new
• Environmental Detectors
• Diagnostic Reagents
• Animal Models
• Vaccines
• Antiviral Compounds

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PBR Design Philosophy

• Useful and Used
• Supporting all poxvirus investigators
• UAB PBR Web-based application requirements
• Web Browser
• Java plugin
• In-depth analyses
• UVic analytical tools

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BLAST

• Search a sequence database for primary sequence
  similarities to some query sequence
• Provides a measure of the significance of the
  similarity
• Does not necessarily imply common evolutionary
  origin
• Developed at NCBI
• Altschul, S.F., Gish, W., Miller, W., Myers, E.W.
  Lipman, D.J. (1990) "Basic local alignment
  search tool." J. Mol. Biol. 215403-410.

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18 Genomes 563 genes Avg. 31 genes/genome
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PBR Knowledge Database

• Mini review of available structure-function
  information
• Human-curated database based on the literature
• Bibliographic information
• Available scientific resources
• clones, mutants, and antibodies
• Empirically-derived properties
• MW, pI . . .
• Post-translational modifications
• Expression
• Functional Assignments
• Gene Ontology controlled vocabulary
• Molecular function
• Biological Process
• Cellular component
• Virulence Ontology

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Molecular Evolution and GenomicAnalyses of
Poxviruses
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Objectives

• To better understand the role individual genes
  and groups of genes (or other genetic elements)
  play in poxvirus (especial smallpox ) host range
  and virulence
• Try to describe and understand poxvirus diversity
  via reconstruction of the families evolutionary
  history

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Orthopoxvirus Phylogeny
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Orthopoxvirus Phylogeny
132 gene tree possible
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65 gene treepossible forChordopoxviruses
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Horizontal Gene Transfer

• The acquisition of genetic material from another
  organism that becomes a permanent addition to
  the recipients genome
• Many poxvirus genes involved in immune evasion
  may have been acquired thorough HGT
• Detection of HGT
• Alternative base composition
• Alternative codon usage pattern
• Alternative evolutionary inheritance pattern

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Detecting HTGs by plotting codon usage
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GC distribution of Molluscum Contagiosum
MOCV-SB1_011
MOCV-SB1_055
MOCV-SB1_132
GC distribution in Molluscum Contagiosum genome.
It is smoothened by wavelet technique. The blue
number is the position in genome. The green bars
mark significant deviation and a putative gene is
marked there.
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VARV Proteins with Similarity to Human Proteins

• 3-beta-hydroxysteroid dehydrogenase
• Ankyrin
• CD47 antigen
• Carbonic Anhydrase
• Casein kinase 1
• Complement control protein
• DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide
• DNA ligase
• Glutaredoxin
• Hypothetical protein
• JNK-stimulating phosphatase
• Kelch-like protein
• Lymphocyte activation-associated protein
• Makorin zinc-finger protein
• Myosin heavy chain
• Plasminogen activator inhibitor
• Profilin
• RNA polymerase
• Ribonucleotide reductase M2

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Ribonucleotide Reductase Homolog Evolution
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TNF Receptor Homolog Evolution
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TNF Receptor GenBank nr Hits
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VARV B22R BLASTN Results
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Genome Comparison Variola major vs. minor
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Genome vs. Gene Phylogeny
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Molecular Evolution and GenomicAnalyses of
Poxviruses

• We have a problem

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Poxvirus Gene Prediction

• Little consistency from one genome to another
• Methods employed
• Minimum ORF size
• Similarity with previously described proteins

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Consistently predict and annotate the gene set
for all Poxvirus genomes

• Development of a comprehensive gene prediction
  tool
• Discovery of new or missed genes
• Removal of pseudo genes
• As an added bonus
• Computational annotation of each predicted gene

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What is a gene?

• Does it looks like a gene?
• Open Reading Frame
• Base composition
• Codon usage
• Is it expressed?
• Regulatory signals
• Transcription
• Translation
• Has it been previously recognized?
• Similarity searching

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Proposal gene finding tool

• Combination of a series of complementary gene
  prediction algorithms
• DNA Signals
• ORF detection
• Base composition
• Codon preference
• HMM gene models
• Similarity searching
• BLAST similarity searches
• Similarity to identified poxvirus protein domains
  using an HMM-based domain database
• Promoter detection
• Neural Network promoter detection tool
• Patterns of amino acid sequence conservation
• Biodictionary-based analysis
• Knowledge-based integration of all predictive
  methods
• Computational conclusions
• Visualization tool for human inspection

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Using High Performance Computing to Speedup
Bioinformatic Applications
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Features to consider in porting an application to
a cluster environment

• Balancing the processing workload among nodes is
  critical to successful implementation
• A computational method with a lower percentage
  load imbalance (PLIB) is more efficient than one
  with a higher PLIB. The workload is perfectly
  balanced if PLIB is equal to zero.
• Similarity searching workload can be difficult to
  estimate
• Dependent on the nature of both the database and
  query sequences
• sequence length
• number of sequences
• complexity of the sequences

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Data Segmentation

• Database Sequences
• Utilize when the database size is larger than
  physical memory of each computational node
• Results need to be combined and statistics
  recalculated
• Not possible with some applications (PSI-BLAST)
• Query Sequences
• Flexible and allows for better balancing of the
  workload
• Statistics remain valid
• Database remains intact
• Best performance when the database can be fully
  loaded into available memory

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Work Flow for Database segmentation

• Database is split evenly and formatted
• Database fragments are sent to each node
• Query file is distributed to all nodes
• The search is initiated
• Output is collected for merging and formatting

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Work Flow for Query Segmentation

• Database is distributed to all nodes
• 90 of the query sequences are split into bins
  and distributed among the available nodes
• Balanced for sequence length and number
• The remaining 10 query of the query sequences
  are delivered to nodes as they finish the initial
  search
• Individual results are merged and reported

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Implementation

• Utilizes the LAM/MPI Message Passing Interface
  package from Indiana University
• The application executables are not altered
• The implementation wraps the executable and data
  and sends it to each node
• Easily accommodate application updates
• Easily extends to similar applications
• Currently have implemented two wrappers
• BLAST
• HMMPFAM
• Sean Eddy, Washington University School of
  Medicine, St. Louis, Missouri
• Benchmarks performed on the UAB School of
  Engineer Linux cluster
• 2 storage servers (IBM x345).
• one compile node and 64 compute nodes (IBM x335)
• 2 x 2.4 GHz Xeon processors per node
• 2-4 GB of RAM per node
• 18 GB SCSI hard drive
• connected via Gigabit Ethernet to a Cisco 4006
  switch

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Comparison of gene finding methods
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Gene prediction Putting it all together
38000
32000
40000
36000
34000
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Now the real work can begin

• More rigorous comparative analysis
• Shared and unique sets of gene composition
• SNP analysis of gene differences
• Whole genome phylogenetic prediction
• Individual gene phylogenetic prediction
• Unique patterns of evolutionary inheritance
• Clustering of evolutionary inheritance with
  pathogenesis