Evaluation of recombination detection methods for viral sequencing

Recombination is a key evolutionary driver in shaping novel viral populations and lineages. When unaccounted for, recombination can impact evolutionary estimations, or complicate their interpretation. Therefore, identifying signals for recombination in sequencing data is a key prerequisite to further analyses. A repertoire of recombination detection methods have been developed over the past two decades, however, the prevalence of pandemic-scale viral sequencing data poses a computational challenge for existing methods. Here, we assessed five recombination detection methods (PhiPack (Profile), 3SEQ, GENECONV, VSEARCH (UCHIME), and gmos) to determine if any are suitable for the analysis of bulk sequencing data. To test the performance and scalability of these methods, we analysed simulated viral sequencing data across a range of sequence diversities, recombination frequencies, and sample sizes. Further, we provide a practical example for the analysis and validation of empirical data. We f..., , , Simulated .fasta files by SANTA-SIM across different parameters.

All file names follow the structure of msa_m_r_n_dual_rep.fasta with a numeric value following each letter.

The parameters are:

m = mutation rate
r = recombination rate
n = number of sequences
dual = dual-infection probability
rep = replicate

The numeric indicates the parameter value it follows, for example, msa_m0.001_rc0.05_n100_dual1_rep2.fasta means that the alignment was simulated with the parameters:

mutation rate = 0.001
recombination rate = 0.05
number of sequences = 100
dual-infection probability = 1
replicate = 2

## Files

performance.tar.gz - alignments for the performance benchmark
scale.tar.gz - alignments for the scalability benchmark