PreprintCC BY-NC-NDbioRxiv
seqproc: An efficient, flexible, and concise tool for sequence geometry description and transformation
- 1University of Maryland
Abstract
Complex sequencing protocols encode technical information in read structure and require accurate, e[ff]icient 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.
Related
- Network geometry shapes the transformation of task representations across the human cortex
- CellPatch: a flexible and efficient framework for single-cell foundation models empowered by heuristic gene patching
- Suboptimal human inference reflects an efficient and flexible information bottleneck
- An efficient and flexible generalization of transit compartment models for delayed biological processes
- Restoring post-electroporation envelope integrity enables efficient spheroplast transformation of non-model Gram-negative bacteria
BibTeX
@misc{cape2026seqproc,
title = {{seqproc: An efficient, flexible, and concise tool for sequence geometry description and transformation}},
author = {N. Cape and E. Fisher and D. Liu and R. Patro},
howpublished = {bioRxiv},
year = {2026},
month = jul,
doi = {10.64898/2026.07.28.741211},
url = {https://www.biorxiv.org/content/10.64898/2026.07.28.741211v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - seqproc: An efficient, flexible, and concise tool for sequence geometry description and transformation
AU - N. Cape
AU - E. Fisher
AU - D. Liu
AU - R. Patro
PY - 2026
DA - 2026/07/28
PB - bioRxiv
DO - 10.64898/2026.07.28.741211
UR - https://www.biorxiv.org/content/10.64898/2026.07.28.741211v2
AB - Complex sequencing protocols encode technical information in read structure and require accurate, e[ff]icient 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.
ER -