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A Python-based programming language for high-performance computational genomics

Nature Biotechnology volume 39, pages 1062–1064 (2021)Cite this article

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To the Editor — The vast growth of next-generation sequencing data has provided us with a new understanding of many biological phenomena. As sequencing technologies evolve, sequencing datatypes (such as standard Illumina short reads, PacBio long reads or 10x Genomics barcoded reads) typically require new implementations of corresponding computational analysis techniques, necessitating software that is not only computationally efficient, but also quick to develop and easy to maintain so as to enable rapid adaptations to new kinds of data.

However, developing an efficient software tool requires domain expertise in performance engineering, computational modeling, and the ability to translate biological assumptions into algorithm and software optimizations1. As a result, most high-performance genomics software is highly application specific, difficult to understand, and difficult to maintain. These factors collectively have sparked a reproducibility crisis in the fields of computational biology and life sciences in general2,3,4. Ultimately, researchers in the field lack good tools for developing and updating software; one typically must choose between a software ecosystem that allows rapid development at the expense of performance and scalability (for example, Python or R) or low-level languages that have higher performance but are harder to develop and maintain (for example, C, C++ or Rust)5. Existing solutions that try to fill the void between these extremes (for example, Matlab or Julia) are primarily geared toward numerical computing rather than computational genomics, and the few attempts that have been made at simplifying the development of bioinformatics software (for example, Rust-Bio6, SeqAn7,8, BioJulia9 or Biopython10) are based on languages that are either unfamiliar11, complex and hard to program, or too high level to attain good performance.

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Fig. 1: The Seq programming language.

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Author notes

  1. These authors contributed equally: Ariya Shajii, Ibrahim Numanagić.

Authors and Affiliations

  1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ariya Shajii, Ibrahim Numanagić, Alexander T. Leighton, Saman Amarasinghe & Bonnie Berger

  2. Department of Computer Science, University of Victoria, Victoria, British Columbia, Canada

    Ibrahim Numanagić & Haley Greenyer

  3. Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Alexander T. Leighton & Bonnie Berger

Authors
  1. Ariya Shajii
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  3. Alexander T. Leighton
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  5. Saman Amarasinghe
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  6. Bonnie Berger
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Correspondence to Saman Amarasinghe or Bonnie Berger.

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Peer review information Nature Biotechnology thanks Ivan Costa and Judith Zaugg for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Notes 1–4, including Figs. 1–5 and Tables 1–8

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Shajii, A., Numanagić, I., Leighton, A.T. et al. A Python-based programming language for high-performance computational genomics. Nat Biotechnol 39, 1062–1064 (2021). https://doi.org/10.1038/s41587-021-00985-6

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