Grid enabling phylogenetic inference on virus sequences using BEAST - a possibility? EUAsiaGrid...

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Grid enabling phylogenetic inference on virus sequences using BEAST - a possibility? EUAsiaGrid Workshop 4-6 May 2010 Chanditha Hapuarachchi Environmental Health Institute National Environment Agency

Transcript of Grid enabling phylogenetic inference on virus sequences using BEAST - a possibility? EUAsiaGrid...

Grid enabling phylogenetic inference on virus sequences using BEAST - a possibility?

EUAsiaGrid Workshop

4-6 May 2010

Chanditha Hapuarachchi

Environmental Health Institute

National Environment Agency

Outline

Work scope

Analytical approach

Current limitations

What is expected from Grid-enabling?

Work scope

Understanding the molecular epidemiology of vector-borne, infectious diseases in Singapore with a view of utilizing information in disease control operations

Objectives To determine the routes of pathogen migration (mainly Dengue and

Chikungunya viruses)

To understand the evolutionary dynamics of pathogens

To understand the outbreak potential of pathogens within the country

Molecular epidemiology

of DENV & CHIKV

Phylogenetic relationships

(trees)

(BEAST, MEGA)

Evolutionary dynamics

(Evolutionary rates, selection pressure, recombination etc)

(BEAST, HYPHY etc.)

Population dynamics

(Bayesian skyline plots)

(BEAST)

Temporo-spatial distribution of viruses

(BEAST, NETWORK)

What phylogenetic inferences are made?

BEAST is a multi-task software package

CHIKV whole genome tree with spatial model

India

Sri Lanka

Singapore

Malaysia

Ind. Ocean Islands

Kenya

Time (yrs)

Spatial distribution of different lineages of DENV in Singapore

However……..

BEAST analysis is time consuming & requires substantial computing power

Limitations of the BEAST approach?

Size of dataset

Length of sequences

No. of sequences

E.g. Analyzing a dataset of ~90 whole genomes of CHIKV (11.8 kb) takes several days depending on the available computing power

Analytical parameters

A basic analysis takes ~0.3 hrs per million states

(Core 2 duo, 2.1 GHz, 4 GB RAM, >50% CPU)

A general run involves at least a 100 million sampling frame

(=~30 hrs)

The duration increases substantially with changing parameters

Incorporation of spatial model (7 states) alone increases the runtime to ~0.4 hrs per million states

The ultimate duration depends on Effective Sample Size (ESS)

values (general requirement >200)

Limitations…

BEAST Tracer output window

Limitations…

Number of parallel runs & users

↑ runs & users -------- ↓ analytical efficiency

Single run takes up >50% of CPU power

Why to Grid-enable BEAST?

Enables efficient data analysis

parallel runs

multiple users

expanded datasets

Enhances data interpretation

Can Grid-enabling help to improve the existing performance?