A Python-based optimization framework for high-performance genomics

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2021-07-21 13:00:06

Exponentially-growing next-generation sequencing data requires high-performance tools and algorithms. Nevertheless, the implementation of high-performance computational genomics software is inaccessible to many scientists because it requires extensive knowledge of low-level software optimization techniques, forcing scientists to resort to high-level software alternatives that are less efficient. Here, we introduce Seq—a Python-based optimization framework that combines the power and usability of high-level languages like Python with the performance of low-level languages like C or C++. Seq allows for shorter, simpler code, is readily usable by a novice programmer, and obtains significant performance improvements over existing languages and frameworks. We showcase and evaluate Seq by implementing seven standard, widely-used applications from all stages of the genomics analysis pipeline, including genome index construction, finding maximal exact matches, long-read alignment and haplotype phasing, and demonstrate its implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq further opens the door to the democratization and scalability of computational genomics.

The vast growth of next-generation sequencing (NGS) data has provided us with a new understanding of many biological phenomena. As such technologies rapidly evolve, new sequencing datatypes (e.g. Illumina short-reads, PacBio long-reads or 10X barcoded reads) typically require new implementations of corresponding computational analysis techniques. Doing so requires software that is computationally efficient, quick to develop and easy to maintain in order to enable rapid adaptation to new kinds of data.