New sequencing technologies and evolving experimental chemistries often produce complex read structures in which the sequence itself encodes important technical metadata, such as the originating cell or molecule. Effective preprocessing of these data requires tools that can accurately identify, extract, and normalize such information prior to downstream analysis. The authors introduce seqproc to meet this need by providing a concise, general-purpose system for describing and transforming sequence geometry.
seqproc is built around a compact descriptive grammar that lets users specify how sequence matching and transformations should be performed. Instead of hard-coding a particular pipeline for a given chemistry, users provide a geometry and transformation description in this grammar. The design goal is to make descriptions concise while expressive enough to cover the diverse set of chemistries and protocols encountered in modern sequencing applications, including single-cell approaches where read segments capture technical identifiers.
The grammar-centric approach separates the specification of what to extract and how to transform reads from the execution engine. This reduces boilerplate and aims to simplify the creation and maintenance of preprocessing configurations across multiple protocols.
A key architectural detail is that seqproc compiles the user-specified geometry and transformation description into an execution graph. That execution graph is executed by the ANTISEQUENCE library. By transforming the declarative description into a compiled execution plan, seqproc moves work into an optimized runtime path and enables the underlying library to handle low-level sequence operations efficiently.
This compiled execution graph model supports the tool's flexibility: different user descriptions produce different execution graphs without changing the underlying codebase, allowing a single tool to serve many preprocessing needs.
In comparisons reported by the authors, seqproc is described as being faster on most chemistries, substantially more memory efficient, and at least as accurate as alternative tools that provide similar functionality. The source emphasizes that seqproc achieves these performance characteristics while offering a more concise description syntax than the compared tools.
The summary in the source does not provide granular benchmark numbers, exact datasets, or the list of specific alternative tools used for comparison. Therefore, no precise performance figures, test conditions, or statistical results can be stated beyond the relative claims reported by the authors.
seqproc is implemented in Rust. It can be used either as a command-line binary or integrated as a Rust crate within other software. The project is released under the BSD 3-clause license, and the source code repository is publicly available at https://github.com/COMBINE-lab/seqproc.
These choices—a systems language implementation and a permissive open-source license—support integration into diverse workflows and reuse in both research and applied settings.
The authors disclose a competing interest: one author (R.P.) is a co-founder of Ocean Genomics Inc. Funding acknowledged in the article includes support from the National Institutes of Health (R01HG009937) and grants from the Silicon Valley Community Foundation (grant identifiers reported in the source). No other conflicts or additional funders were listed in the summary.
This work was posted as a preprint on bioRxiv on July 29, 2026, with DOI https://doi.org/10.64898/2026.07.28.741211. The preprint is available under a CC-BY-NC-ND 4.0 International license. Supplementary material and code links are referenced in the source; the primary code repository is at the GitHub URL given above.
The source summary highlights relative performance advantages but does not include detailed benchmark methodology, dataset descriptions, or numeric results. Where those specifics are necessary for adoption or evaluation, readers should consult the full preprint or the project repository for implementation details, usage examples, and performance data that were not reported in the brief abstract-level summary.