@@ -7,15 +7,18 @@ This repository contains the supplementary code and intermediary data for the me
The repository includes the following contents:
1.**Code**:
-`metabarcoding_analysis.R`: R script for the metabarcoding data analysis, including data preprocessing, diversity analysis, and statistical testing.
-`metabarcoding_functions.R`: Custom functions used in the metabarcoding data analysis.
1.**'analysis' folder**: contains scripts and intermediary files used and generated in the analysis.
Analysis was largely conducted per primer used in metabarcoding and then synthesized in some higher level analyses.
For each primer pair, the following is provided
- R script for the metabarcoding data analysis, including data preprocessing, diversity analysis, and statistical testing.
- Intermediary metabarcoding data, including amplicon sequence variants (ASVs), taxonomy, ASV counts for each sample and DADA2 processing logs.
- Metadata for the samples, including NDVI, and other relevant variables.
2.**Data**:
-`raw_metabarcoding_data.csv`: Raw metabarcoding data, including OTU (Operational Taxonomic Unit) counts for each sample.
-`metadata.csv`: Metadata for the samples, including information such as field location, plant growth status, and other relevant variables.
2.**'manuscript files' folder**: contains the larger tables presented in the manuscript
3.**README.md**: This file, providing an overview of the repository and instructions for use.
3.**'SRA submission' folder**: contains metadata used for submission of reads to NCBI's Short Read Archive
4.**README.md**: This file, providing an overview of the repository and instructions for use.
## Usage
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@@ -27,7 +30,7 @@ The repository includes the following contents:
2. Ensure you have the necessary R packages installed. All necessary dependencies required by the analysis scripts are listed in the top chunks of the respective scripts.
3. Obtain the raw read data from [ENA](LINK-MISSING)
3. Obtain the raw read data from [SRA](https://www.ncbi.nlm.nih.gov/bioproject/) under the BioProject with the accession PRJNA1191665.
4. Open the R Markdown file and run the code to reproduce the metabarcoding data analysis.
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@@ -39,7 +42,7 @@ If you use the code or data from this repository in your own work, please cite t
To be updated upon publication
```
Additionally, please acknowledge the use of this supplementary repository by citing it as follows:
Before publication, please acknowledge the use of this supplementary repository by citing it as follows:
```
MP Tangvik et al. (2024). Supplementary code and data for 'Drone imagery and metabarcoding as diagnostic tools of poor growth in potato fields' [Data set]. GitLab. https://gitlab.nibio.no/simeon/persdatter-et-al-2025-metabarcoding
