2026-09-18T22:00:02Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1400812022-02-08T03:58:24Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Berger, Bonnie
Amarasinghe, Saman
Shajii, Ariya
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-02-07T15:22:57Z
2022-02-07T15:22:57Z
2021-09
2021-09-21T19:30:53.561Z
https://hdl.handle.net/1721.1/140081
Next-generation sequencing data is growing at an unprecedented rate, leading to new revelations in biology, healthcare, and medicine. Many researchers use high-level programming languages to navigate and analyze this data, but as gigabytes grow to terabytes or even petabytes, high-level languages become prohibitive and impractical for performance reasons. This thesis introduces Seq, a Python-based, domain-specific language for bioinformatics and genomics 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. Seq is showcased and evaluated by implementing a range of standard, widely-used applications from all stages of the genomics analysis pipeline, including genomic index construction, data pre- and post-processing, read mapping and alignment, and haplotype phasing. We show that the Seq implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. Seq's substantial performance gains are made possible by a host of novel genomics-specific compiler optimizations that are out of reach for general-purpose compilers, coupled with a static type system that avoids all of Python's runtime overhead and object metadata. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq aims to act as a catalyst for scientific discovery and innovation. Finally, we also generalize many of the principles used by Seq to create a domain-configurable compiler called Codon, which can be applied to other domains with similar results.
Ph.D.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
High-Performance Computational Genomics
Thesis
application/pdf
Doctoral
Doctor of Philosophy
Publication
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<Abstract>Next-generation sequencing data is growing at an unprecedented rate, leading to new revelations in biology, healthcare, and medicine. Many researchers use high-level programming languages to navigate and analyze this data, but as gigabytes grow to terabytes or even petabytes, high-level languages become prohibitive and impractical for performance reasons. This thesis introduces Seq, a Python-based, domain-specific language for bioinformatics and genomics 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. Seq is showcased and evaluated by implementing a range of standard, widely-used applications from all stages of the genomics analysis pipeline, including genomic index construction, data pre- and post-processing, read mapping and alignment, and haplotype phasing. We show that the Seq implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. Seq's substantial performance gains are made possible by a host of novel genomics-specific compiler optimizations that are out of reach for general-purpose compilers, coupled with a static type system that avoids all of Python's runtime overhead and object metadata. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq aims to act as a catalyst for scientific discovery and innovation. Finally, we also generalize many of the principles used by Seq to create a domain-configurable compiler called Codon, which can be applied to other domains with similar results.</Abstract>
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