SRARec: A program for detecting recombination in sequencing reads and its application to uncover recombination patterns in SARS-CoV-2 and HIV-1

Authors

Gonzalez Vazquez, L. D., Iglesias Rivas, P., Arenas, M., Martin, D. P.

Abstract

The detection of recombination using consensus genome sequences has key limitations including failure to consider rare genetic variants and misidentification of artifactually assembled genome chimaeras as biological recombinants. However, commonly used recombination detection tools are not designed to directly analyse sequencing read data. Here, we present SRARec, a recombination detection tool that operates directly on raw reads. SRARec identifies polymorphic sites and applies the four-gamete test to detect recombination at the read level. The software incorporates mapping and quality filters and can analyse large repositories of raw sequencing data. Simulation validations showed that, given sufficient sequence diversity, SRARec can accurately detect recombination breakpoints. The consideration of rare variants makes SRARec particularly useful for detecting recombination in intra-host viral populations. Therefore, we applied the tool to 601,045 SARS-CoV-2 and 4,999 HIV-1 read datasets from the Sequence Read Archive (SRA, NCBI), enabling unprecedented genome-wide screening of intra-host recombination breakpoint signals at read-level resolution. Aggregating across all analysed datasets, the distribution of detected recombination breakpoint counts along genomes differed between these viruses, with pervasive breakpoint signals detectable in HIV-1 and sporadic clustered breakpoint hotspots in SARS-CoV-2.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 01 Aug 2026.

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