Optimizing Genome Pipelines: A Case Study of Wellcome Sanger Institute
Technology Category
- Application Infrastructure & Middleware - Database Management & Storage
- Infrastructure as a Service (IaaS) - Cloud Storage Services
Applicable Industries
- Equipment & Machinery
- Oil & Gas
Applicable Functions
- Product Research & Development
Use Cases
- Time Sensitive Networking
Services
- Cloud Planning, Design & Implementation Services
About The Customer
The Wellcome Sanger Institute is a globally recognized center for genomic discovery and understanding. It spearheads ambitious collaborations worldwide to lay the groundwork for further research and transformative healthcare innovations. The Institute's Cancer Genome Project uses high-throughput genome sequencing to identify somatically acquired mutations, with the goal of characterizing cancer genes, mutational processes, and patterns of clonal evolution in human tumors. The Institute is at the forefront of genomic research, carrying out genome projects to find cures for cancer, a disease that, according to Cancer Research UK, will affect 1 in 2 people born after 1960 at some point in their lives.
The Challenge
The Wellcome Sanger Institute, a leading center for genomic discovery, was facing a significant challenge in managing the immense amount of data generated by their cancer genome projects. Each cancer sample produced approximately 250GB of data after initial processing, necessitating efficient data storage solutions. The team needed to make one of their cancer pipelines portable and optimize it for cloud deployment. Most pipelines were written and tested on local machines and then run in parallel on compute clusters with shared storage. However, the I/O behavior on clusters was very different, and without comprehensive I/O profiling tools, inefficient I/O patterns could negatively impact storage performance and hinder other work processes.
The Solution
The Wellcome Sanger Institute employed Altair Mistral™ to profile the pipeline and identify inefficient I/O patterns. While the pipeline had been optimized in certain areas, Mistral revealed areas that required further improvement. It identified a large number of small reads, which could negatively impact computational performance and create suboptimal I/O patterns on shared storage. By optimizing these small reads, storage could run at maximum bandwidth with minimal impact on other jobs. The team also used Altair Breeze™ to profile the containerized workload in the cloud on Amazon Web Services (AWS). Breeze determined that the default storage option provided the best value over faster, more expensive options.
Operational Impact
Quantitative Benefit
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