Thesis Topic Details

Topic ID:
3568
Title:
Cloud-based computing for massively parallel single-cell transcriptomic analysis
Supervisor:
Joshua Ho
Research Area:
Bioinformatics, Cloud Computing, High-Performance-Computing
Associated Staff
Assessor:
Bruno Gaeta
Topic Details
Status:
Active
Type:
Research
Programs:
CS CE BIOM BINF SE
Group Suitable:
Yes
Industrial:
Yes
Pre-requisites:
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Description:
In recent years, two innovative technologies are transforming the landscape of biomedical research â€" Next Generation Sequencing (NGS) and single cell analysis. By applying these technical advances, we can now generate genome-wide NGS-based gene expression profiles of thousands of single cells simultaneously in one experiment, opening up enormous opportunities for biomedical research. Nonetheless, the pace of data generation far outstrips our ability to analyse them. Not only do we need new statistical methodologies to deal with inherent biases from single-cell technologies, we also need to handle the computational scalability issues in terms of processing huge datasets.

To illustrate the scalability issue, consider the problem of aligning 1,000 single-cell RNA-seq datasets simultaneously. If alignment were to be performed sequentially (one sample at a time using one CPU), it will take ~10.5 days to finish aligning all samples â€" a task that will only take 15 minutes if we can adaptively use 1,000 CPUs simultaneously. We will develop
tools (which will be made publicly available for the benefit of the community) to easily control a cloud-based system to ensure that computational resources can be adaptively requested or released to maximise their utility with minimal cost. We have already developed several integrated computational pipelines that mine Short Read Achieve (SRA) data in the Amazon Elastic Compute cloud (EC2). In this project, we will develop a statistically rigorous yet computationally efficient pipeline that can take advantage of the highly adaptive cloud computing infrastructure for single-cell RNA-seq data analysis.
Comments:
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Past Student Reports
 
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