Supplementary data for: Environmental DNA is an efficient tool for monitoring pesticide resistance mutations in pest insects

Scripts for making the figures and analyses from "Environmental DNA is an efficient tool for monitoring pesticide resistance mutations in pest insects" plus the raw reads used for the study.

Description of the data and file structure

File: supplements_pesticide_edna_devivo.zip

Description: Zipped file with all the data, split like this:

brassica.fas

Sequences of Brassica rapa from the in-vitro experiment using the REF918_fw and sKDR R primers. It can be opened with genomic file editors (e.g., MEGA) or text editors.

quiagen_extraction_from_filters.pdf

Protocol used for eDNA extraction from filters (i.e., leaves from in-field experiment and old stem samples).

2 folders: bash and R

In "bash", as the name suggest, there is everything for using BASH to process the reads. "R" is for plotting with elaborated files.

Inside bash:

metabarcoding.yml

File for creating the used Conda environment. Open it with Conda.

reads_processing.sh

Used scripts for generating zOTU tables. The file can be opened with tools for editing codes (e.g., Visual Studio Code or Notepad).

###reads_pesticide_2026-001.zip

Reads used in the study, in gzipped FASTQ files (*fastq.gz).

In the R folder:

###meta_2024.csv

Metadata file for the samples collected in 2024. It is a comma-delimited CSV file, which can be opened with a spreadsheet software (e.g., Microsoft Excel), a text editor or R. It is split in the following columns:

• sample: The sample name;
• field: Where the sample was collected.

###meta_2025.csv

Metadata file for the samples collected in 2025 in Oberkorn. It is a comma-delimited CSV file, which can be opened with a spreadsheet software (e.g., Microsoft Excel), a text editor or R. It is split in the following columns:

• sample: The sample name;
• type: if the sample was a beetle or a leaf.

###meta_invitro.csv

Metadata file for the samples from the in-vitro experiment. It is a comma-delimited CSV file, which can be opened with a spreadsheet software (e.g., Microsoft Excel), a text editor or R. It is split in the following columns:

• sample: The sample name;
• type: if the sample was a beetle or a leaf;
• group: which experimental group the sample belonged to.

###rdata.RData

Open in R. It is basically everything that has been done in R, already elaborated.

###scripts_plots.rmd

Scripts for plotting, in RMarkdown format, which can be opened in Rstudio and tools for editing codes (e.g., Visual Studio Code or Notepad).

###supplements.Rproj

RProj file, for starting everything from scratch. It can be opened with R.

###table_2024.csv

zOTu table file for the samples collected in 2024. It is a comma-delimited CSV file, which can be opened with a spreadsheet software (e.g., Microsoft Excel), a text editor or R. It is split in the following columns:

• sample: The sample name;
• M918L: relative abundance of the mutation M918L;
• reg: relative abundance of the wildtype allele.

###table_2024.csv

zOTu table file for the samples collected in 2025 in Oberkorn. It is a comma-delimited CSV file, which can be opened with a spreadsheet software (e.g., Microsoft Excel), a text editor or R. It is split in the following columns:

• sample: The sample name;
• M918L: relative abundance of the mutation M918L;
• Wildtype: relative abundance of the wildtype allele.

###table_invitro.csv

zOTU table file for the samples from the in-vitro experiment. It is a comma-delimited CSV file, which can be opened with a spreadsheet software (e.g., Microsoft Excel), a text editor or R. It is split in the following columns:

• sample: The sample name;
• Wildtype: relative abundance of the wildtype allele;
• L925I: relative abundance of the mutation L925I;
• M918L: relative abundance of the mutation M918L.

Code/software

We used BASH scripts and shell-based tools for processing the reads. FASTQ files were merged using PEAR (version 0.9.6), with minimum base pair overlap of 50 and minimum quality of 20. USEARCH (version 12.0 beta) was used with a minimum error rate of 1 for converting the FASTQ outputs to FASTA. The primer sequences were trimmed from the FASTA files and sequences of the right amplicon size (154 and 112, which is the sizes of the amplicons without primers for the REF918_fw-sKDR R set and sKDR F-sKDR R set, respectively) were filtered through BASH scripts. After this, we used USEARCH for first dereplicating reads and then clustering them into zero-radius Operational Taxonomic Units (zOTUs); each OTU therefore had at least a nucleotide change compared to other ones. Only zOTUs with at least 8 sequences were retained.

The analyses were run in R (version 4.2.2) inside RStudio (version 2023.12.1), using the package “tidyverse” (version 2.0.0) for data processing and analyses. For making plots, we used “tidyverse”, “ggpmisc” (version 0.6.3) and “patchwork” (version 1.3.2).