Novel domain-specific languages and compiler optimization methods for computational biology

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Motivation: The vast scale of data generated by next-generation sequencing (NGS) experiments necessitates the development of efficient computational methods, as the computational aspect is currently the biggest bottleneck of NGS pipelines. However, many promising methods cannot handle the scale of current and emerging NGS technologies and are too hard to use and replicate. Root cause of this problem lies in widely used general-purpose development environments that cannot efficiently express and optimize biological data workflows. Users are forced to use either high-level but slow languages such as Python, or low-level languages such as C that produce efficient tools but at a significant time and maintainability costs. Approach: A domain-specific language (DSL) and associated suite of compiler optimization techniques specifically tailored for biological and sequencing data would provide flexibility, simplicity and modularity for experimenting with new computational algorithms, while generating high-performance code and making the programs portable from resource-constrained architectures to the biggest supercomputers. Objectives: We propose a novel DSL and associated compiler named Seq that enables rapid and easy development of high-performance sequencing pipelines. To achieve this, we will: (i) design a programming language and a compiler that allows ease of development of high-level languages such as Python, while providing raw performance of low-level languages such as C (Objective 1); (ii) explore data access patterns in genomic workflows and devise methods that can exploit these patterns at the compiler level for automatic low-level optimizations across various computational environments, such as multicore CPUs, GPUs and handheld devices (Objective 2); and (iii) provide means to easily integrate Seq into popular bioinformatics and scientific environments and develop a curated library of algorithmic primitives for NGS data (Objective 3). The short-term goal is to develop a DSL that can efficiently handle various kinds of NGS data on common architectures. The long-term goal is to build a comprehensive and widely used infrastructure that allows rapid and easy method development for biological data. As computational biology HQP are in high demand in Canada, one of the key goals of this proposal is to train HQP over the course of five years. Impact: We envision our DSL to significantly boost Canadian genomics and health research by enabling researchers to express their ideas in a more natural way and by allowing them to use the best algorithmic methods for the job. Furthermore, we expect our DSL to aid large-scale scientific Canadian health projects by providing huge time and cost savings. We also anticipate Seq to become a key building block in the wide specter of widely used bioinformatics tools. Finally, we expect that HQP trained by this program will contribute to the Canadian knowledge-based economy.