Human Analysts at Superhuman Scales: What Has Friendly Software To Do?

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• The increasing volume of big data necessitates more efficient analytical tools.
• Genome assembly presents significant computational challenges due to data size.

Purpose of the Study:

• To develop a scalable and efficient big data software for genome assembly.
• To create a programming framework that abstracts computational complexity, enabling focus on scientific problems.

Main Methods:

• Development of RayPlatform, a parallel message-passing software framework.
• Utilization of established technologies like C++ and Message Passing Interface (MPI).
• Application across diverse species' genomes (viruses to plants) on various computing infrastructures (desktops to supercomputers).

Main Results:

• RayPlatform successfully handles large-scale genome data from hundreds of species.
• The framework demonstrates usability and scalability across different computing environments.
• Achieved efficient resource utilization for timely and accurate genome assembly.

Conclusions:

• RayPlatform effectively addresses big data challenges in genomics.
• The framework enhances computational efficiency, making computer time more valuable.
• Enables researchers to concentrate on complex scientific inquiries by abstracting computational overhead.