Poxviruses, Biodefense and Bioinformatics

Working towards a better understanding of viral pathogenesis and evolution

PBR Bioinformatics

Managing Complexity– Technology development

Enhancing Understanding– Research

PBR Managing Complexity

Data– Acquisition– Storage– Manipulation– Retrieval

PBR Managing Complexity…

Data Analysis– Development and Utilization of

• Analytical tools

• Visualization tools

PBR 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?

PBR 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

The Poxvirus Bioinformatic Resource

www.poxvirus.org

PBR

PBR PBR Collaborators

UAB– Elliot Lefkowitz

St. Louis University– Mark Buller

University of Victoria– Chris Upton

ATCC– Charles Buck

Medical College of Wisconsin– Paula Traktman

PBR 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

PBR 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

PBR The PBR is Designed to Support

Basic and applied research on Poxviruses including the development of new:

Environmental DetectorsDiagnostic ReagentsAnimal ModelsVaccinesAntiviral Compounds

PBR PBR Design Philosophy

Useful and UsedSupporting all poxvirus investigators

– UAB PBR Web-based application requirements• Web Browser

• Java plugin

In-depth analyses– UVic analytical tools

PBR 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. 215:403-410.

18 Genomes; 563 genes = Avg. 31 genes/genome

PBR 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

PBR

Molecular Evolution and GenomicAnalyses of Poxviruses

PBR 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

10 nucleotide changes

Orthopoxvirus PhylogenyDNA Polymerase

CMPV-M96

VARV-BSH

VMNV-GAR

ECTV-MOS

CPXV-BR

VACV-COP

MPXV-ZAI

100

100100

59

Nucleoside triphosphatase

MPXV-ZAI

CMPV-M96

VARV-BSH

VMNV-GAR

ECTV-MOS

CPXV-BR

VACV-COP

100100

7894

Orthopoxvirus Phylogeny

132 gene tree possible

65 gene treepossible forChordopoxviruses

PBR Horizontal Gene Transfer

The acquisition of genetic material from another organism that becomes a “permanent” addition to the recipient’s 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

Detecting HTGs by plotting codon usage

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.

GC distribution of Molluscum Contagiosum

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 SNF2 transcription activator Serine proteinase inhibitor Squamous cell carcinoma antigen Superoxide dismutase Thymidine kinase Tumor necrosis factor receptor

Ribonucleotide Reductase Homolog Evolution

TNF Receptor Homolog Evolution

TNF Receptor GenBank nr Hits

VARV B22R BLASTN Results

Genome Comparison: Variola major vs. minor

Genome vs. Gene Phylogeny

Molecular Evolution and GenomicAnalyses of Poxviruses

We have a problem…

PBR

PBR Poxvirus Gene Prediction

Little consistency from one genome to another

Methods employed– Minimum ORF size– Similarity with previously described proteins

PBR 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

PBR 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

PBR 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

Using High Performance Computing to Speedup Bioinformatic

Applications

PBR 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

100arg

arg

estLoadL

adSmallestLoestLoadLPLIB

PBR 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

PBR 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

PBR 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

PBR 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

PLIB for BLAST in query segmentation

Processors

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PLIB for BLAST in database segmentation

Processors

4 8 16 32 64

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MPI-HMMPFAM ( query segmentation)

Processors

3 7 15 31 63

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Comparison of gene finding methodsMethods Pros Cons

DNA Signal sensor Based on empirically-derived, statistical evidence distinguishing biological signals.

Difficult to distinguish background noise from real signals. Frequently not sensitive enough.

Content sensor(Glimmer)

Dependent on having a reasonable gene model.

Short genes and genes present due to HGT are more difficult to detect.

Similarity searching

(BLAST, HMM)

Relies on accumulated pre-existing biological data. Clearly detects highly relevant matches.

Limited to pre-existing biological data; Sensitive to database errors in; Difficult to detect more distant relationships.

Promoterdetection

Reflects actual poxvirus biology (gene expression).

Weak signals difficult to detect.

Bio-dictionaries Useful for detecting novel genes.

Difficult to implement; no biological evidence.

PBR Gene prediction: Putting it all together

32000 3800036000 40000

Similar searching

ORFs

Glimmer

Promoter detector

34000G/C plotting

Bio-Dictionary

PBR 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 predictionUnique patterns of evolutionary inheritance“Clustering” of evolutionary inheritance

with pathogenesis