Work

SEQPROC: an efficient, flexible, and concise tool for sequence geometry description and transformation

Year: 2026

Type: preprint

Abstract: Complex sequencing protocols encode technical information in read structure and require accurate, efficient preprocessing. We introduce seqproc , which compiles concise sequence-geometry descriptions into execution graphs. Across four single-cell RNA-sequencing protocols, seqproc has the lowest mean runtime at every tested thread count and uses substantially less memory than the next-fastest tool. It has the highest F1 agreement with conservative structural references on all three discriminative chemistries and ties both alternatives on the 10x length-filter control. By separating protocol description from execution, seqproc makes complex read transformations compact, reusable, and efficient.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.64898/2026.07.28.741211

Authors: Noah Cape, Elan Fisher, Daniel Liu, Rob Patro

Institutions: Princeton University, University of Maryland, College Park, Anthropic (United States)

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Silicon Valley Community Foundation, National Institutes of Health, National Human Genome Research Institute

Awards: G1335879155, G6742237731, A Modular Framework for Accurate, Interpretable, and Reproducible Analysis of Long Read RNA-Seq Data