Sustainable Software Development for Next-Gen Sequencing (NGS) Bioinformatics on Emerging Platforms

TL;DR

This paper emphasizes the importance of developing sustainable, accelerated bioinformatics software to bridge the gap between NGS technology and emerging computing platforms, fostering collaboration across disciplines.

Contribution

It highlights the need for a collaborative approach to create sustainable, accelerated bioinformatics software infrastructure for next-generation sequencing applications.

Findings

Identifies critical software infrastructure needs.

Stresses interdisciplinary collaboration for software sustainability.

Proposes constructing a sustainable bridge between bioinformatics and emerging platforms.

Abstract

DNA sequence analysis is fundamental to life science research. The rapid development of next generation sequencing (NGS) technologies, and the richness and diversity of applications it makes feasible, have created an enormous gulf between the potential of this technology and the development of computational methods to realize this potential. Bridging this gap holds possibilities for broad impacts toward multiple grand challenges and offers unprecedented opportunities for software innovation and research. We argue that NGS-enabled applications need a critical mass of sustainable software to benefit from emerging computing platforms' transformative potential. Accumulating the necessary critical mass will require leaders in computational biology, bioinformatics, computer science, and computer engineering work together to identify core opportunity areas, critical software infrastructure, and software sustainability challenges. Furthermore, due to the quickly changing nature of both bioinformatics software and accelerator technology, we conclude that creating sustainable accelerated bioinformatics software means constructing a sustainable bridge between the two fields. In particular, sustained collaboration between domain developers and technology experts is needed to develop the accelerated kernels, libraries, frameworks and middleware that could provide the needed flexible link from NGS bioinformatics applications to emerging platforms.