Serokell

Haskell, Strong Types, and the Next Generation of Bioinformatics: interview with Michal Gajda

An interview with Michal Gajda exploring how Haskell's strong type system, purity, and functional abstractions apply to bioinformatics and biological data processing. Drawing on projects like hPDB (a highly scalable Protein Data Bank parser), JSON Autotype, and XML TypeLift, Gajda argues that Haskell's greatest strength is strongly typed pure functional programming rather than laziness, enabling linear multicore scalability. He discusses the challenges of handling diverse, large-scale biotech data formats, compares Haskell with OCaml, PureScript, and Agda, and identifies education and beginner-friendly tooling as the main barriers to wider adoption. He also sees AI-assisted programming as a potential path to making functional languages more accessible to junior scientists.

•5m read time•From serokell.io
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You have worked across bioinformatics, Haskell, Python, SML, FORTRAN, and commercial software. What makes Haskell a good fit for scientific and biological data problems, and where did it make the work harder than expected?Your hPDB project applied Haskell to processing biomolecular structures in Protein Data Bank format. What did this project teach you about using strong types, laziness, and functional abstractions for real-world biotech data?Bioinformatics often deals with messy formats, legacy tools, and huge volumes of semi-structured data. Do you think Haskell’s type system can realistically reduce errors in biotech pipelines, or does the surrounding ecosystem still matter more than the language?You have worked on tools like JSON Autotype and XML TypeLift, which generate typed representations from data formats. How would you compare this approach to the way biotech software often handles formats such as PDB, FASTA, XML, JSON, or custom lab data exports?You have experience with both Haskell and OCaml, two languages that share some ML-family roots but have very different cultures. How do you see their strengths differently when it comes to building reliable scientific or data-heavy software?Many biotech tools are still written in Python, C++, R, or older scientific languages. What would need to change for languages like Haskell or OCaml to become more widely used in biotech and bioinformatics: better libraries, better tooling, more education, or stronger industry examples?
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