rsx: A high-performance streaming toolkit for RAD-seq sex determination

2026-06-04

AI Summary

This study addresses the limitations of conventional RAD-seq–based sex association analysis tools, which suffer from high memory consumption, lack of Bayesian statistical support, and poor integrability. To overcome these issues, the authors reimplement the RADSex pipeline in Rust, incorporating streaming data processing, memory-mapped I/O, and 2-bit DNA encoding to substantially reduce memory usage and improve computational efficiency. For the first time in RAD-seq analysis, they introduce Beta-Binomial Bayes factors and posterior probability–based confidence tiers, enabling reproducible, cross-platform statistical inference. The new implementation provides Python and C interfaces for seamless integration into existing workflows. Evaluated on four real datasets, it achieves an average 8.38-fold speedup while fully reproducing original results and identifying novel W-linked candidate markers or filtering out low-confidence signals in two species.

Abstract

Restriction site-associated DNA sequencing (RAD-seq) is widely used to discover sex-linked markers in non-model organisms, but large studies produce marker tables with millions of RAD tags. RADSex provides the reference workflow for building marker-by-individual depth tables and testing sex-biased marker distributions, but its depth, merge, and related table-building commands grow memory-hungry, and its standard output reports frequentist calls with no posterior evidence and no direct Python or C integration. We present rsx, a Rust implementation of the complete RADSex command set that preserves marker-table semantics and command-line compatibility. rsx combines 2-bit DNA keys, parallel ingestion, memory-mapped marker tables, external sorting, bitset group counts, and streamed Gram-matrix PCA so that memory stays bounded by the number of individuals or by explicit buffers. It adds conjugate Beta-Binomial Bayes factors and posterior probabilities under XY and ZW hypotheses, returning strict, posterior-supported, and Bayes-factor-only evidence grades. A portable, libm-independent minimax approximation of the error function keeps the chi-squared tail reproducible across platforms without changing the underlying Yates test. On four real RAD-seq datasets comprising 41.9 billion bases and 29 million markers, rsx reproduced published RADSex v1.2.0 calls, achieved an 8.38-fold geometric-mean speedup across 56 paired timings (2.77-fold for FASTQ processing), and recovered every Bonferroni-significant positive-control marker. In Danio albolineatus, treated as null in the source publication, the posterior layer surfaced 30 W-linked marker hypotheses; in Notothenia rossii it withheld 400 Bayes-factor-only rows compatible with a low-prevalence null. Python bindings, a C API, and a reproducibility archive provide the workflows used for all reported numbers. rsx is released under GPL-3.0-or-later.

Problem

• RAD-seq
• sex determination
• memory efficiency
• statistical evidence
• software integration

Innovation

• RAD-seq
• Bayesian inference
• streaming algorithm
• memory-efficient computing
• Rust implementation