NGS datasets of the article "Host infection selects for sRNA variants that drive bacterial social cheating"

The .zip files contain NGS data, either in .gz format, which corresponds to the raw Illumina sequencing data of individual strains or to Illumina sequencing of a PCR fragment of arcZ from a strain mixture, or in .fasta format, when a complete genome sequencing has been performed using Oxford Nanopore.The .zip files are named after the figure to which they are associated.This repository contains also a Python script developed for the high-throughput analysis of arcZ sequence variants from Illumina amplicon sequencing data. The pipeline was designed to quantify single-nucleotide polymorphisms (SNPs), insertions, deletions, and combinations of mutations within the arcZ locus from merged paired-end FASTQ files. The workflow consists of two stages. Raw paired-end reads are first processed in Galaxy by merging reads with PEAR and trimming the flanking primer sequences using Cutadapt. The resulting FASTQ file is then analyzed with the provided Python script in Google Colab. The analysis pipeline performs automated quality filtering based on read length, trims variable poly-T tails to avoid spurious indel detection, and compares each read to a reference arcZ sequence. Reads identical in length to the reference are rapidly screened for SNPs by direct comparison, whereas reads with length differences undergo global pairwise alignment using BioPython to identify genuine insertions and deletions. Variants are reported using a standardized position numbering system relative to the mature arcZ sequence. The script summarizes all detected genotypes, calculates their abundance and frequency within the sequencing dataset, distinguishes wild-type from mutant alleles, and exports the complete results as a CSV table suitable for downstream statistical analyses. The software was developed for the analysis of arcZ variants in Dickeya solani but can be readily adapted to other loci by modifying the reference sequence and primer trimming parameters. It requires only standard Python packages (BioPython and pandas) and is intended to run directly in Google Colab, providing an accessible solution for researchers without dedicated bioinformatics infrastructure.