A reproducible k-mer set-algebra workflow to quantify nuclear–chloroplast–mitochondrial sequence sharing in plant genomes

Overview This Zenodo record provides a complete, end-to-end reproducibility package accompanying our Plant Methods manuscript. The deposit is designed so that a reader can (i) retrieve all analysis scripts and configuration files, (ii) reproduce the key intermediate summaries, (iii) regenerate the final figures/tables reported in the manuscript, and (iv) verify data provenance through accessioned external resources listed in Table 1. The package focuses on compartment-aware k-mer type analyses across nuclear (nc), chloroplast (cp), and mitochondrial (mt) sequences, including: set-algebra partitioning of k-mer types across compartments (seven 3-set categories), robust handling of circular organellar genomes to retain wrap-around k-mers, read-backed validation of assembly-derived k-mers using PacBio HiFi reads with a minimum-count threshold sweep (-ci), and optional evaluation of minimal absent words (MAWs / “nullomers”) with a Markov-model + FDR framework. Large raw sequencing files are not redistributed in this Zenodo record to avoid duplicate deposition and to respect original hosting terms. Instead, all external inputs are referenced by stable accessions and sources in Table 1 included in the deposit. This keeps the record lightweight and FAIR while preserving traceability. Contents of this record This record includes a single self-contained archive plus integrity/entry-point files: Files at the top level (Zenodo record): reproducibility_package.zip — the complete manuscript + code + derived outputs bundle (see structure below) checksums.sha256 — SHA-256 checksums to verify file integrity after download README_zenodo.txt — a short entry-point guide mapping manuscript items to deposited files and how to run the workflow Inside reproducibility_package.zip (high-level structure): manuscript/ manuscript_PlantMethods.docx (main manuscript) manuscript_PlantMethods.md (Markdown version, if provided) figures/ Main figures (e.g., Fig. 1–3) and supplementary figures (Fig. S1–S3) as PDF/PNG tables/ Main tables (Table 1–3) and supplementary tables (Table S1–S2) as TSV supplementary/ “Additional files” packaged for journal submission (e.g., Additional file 1–12) A manifest file describing each supplementary component scripts/ Environment setup, preprocessing, k-mer counting, set operations, optional QC, and plotting scripts Makefile, run_all.sh, and README.md One-command execution entry points and a detailed usage guide positive_control/ (if present) Inputs/scripts/results for a minimal positive-control demonstration (if included in the manuscript package) Relationship to the manuscript (where to find each item) This repository is organized so that manuscript components map directly to deposited artifacts: Manuscript files Main text: manuscript/manuscript_PlantMethods.docx (and optional Markdown) Figures Fig. 1–3: figures/Figure1_*, figures/Figure2_*, figures/Figure3_* Fig. S1–S3: figures/FigureS1_*, figures/FigureS2_* (or a zoomed panel file), figures/FigureS3_* Tables Table 1 (data resources / provenance): tables/Table1_data_resources.tsv(mirrored in Supplementary as supplementary/Additional_file_3_Table1_data_resources.tsv, if applicable) Table 2 (compartment set partitions across k): tables/Table2_*.tsv Table 3 (assembly–read overlap metrics; -ci sweep): tables/Table3_*.tsv Table S1–S2: tables/TableS1_*.tsv, tables/TableS2_*.tsv Supplementary / Additional files supplementary/Additional_file_* and supplementary/ADDITIONAL_FILES_MANIFEST.txt External inputs (not redistributed) and provenance External inputs are referenced in Table 1 (tables/Table1_data_resources.tsv), including: the reference nuclear assembly used in the analysis, reference organellar sequences (RefSeq accessions or the organellar sequences bundled with the assembly distribution, as described in Table 1), the PacBio HiFi read dataset used for assembly–read k-mer validation (stable accession/run identifiers). Readers can fetch these external inputs from their original repositories using the accessions in Table 1. The analysis scripts are designed to work from these accessions and/or from the bundled equivalents when provided. Key methodological notes encoded in the workflow Circular organellar genomes and wrap-around k-mersChloroplast and mitochondrial sequences are treated as circular. To preserve k-mers spanning the junction, the workflow circularizes organellar sequences by appending the first E bases to the 3′ end (“circE”). To guarantee complete wrap-around representation for all k up to a maximum KMAX, the workflow uses: E = KMAX − 1For the manuscript’s default configuration (KMAX = 31), this corresponds to E = 30 (circ30). k ranges and thresholds Compartment set analyses are performed across k = 11–31 (manuscript defaults). Read-backed validation uses a minimum-count threshold sweep in read k-mer counting (e.g., -ci = 1–10) to show how low-frequency read k-mers affect the “only raw” fraction and how results stabilize under more conservative thresholds. MAW/“nullomer” evaluation uses a Markov-model + FDR approach in a constrained k range compatible with the nullomer assessment tool used in the workflow. Reproducibility: quick start Download the files from this Zenodo record. Verify integrity (optional but recommended): sha256sum -c checksums.sha256 Unzip the main archive: unzip reproducibility_package.zipcd reproducibility_package Run the entire workflow (recommended): bash run_all.sh or equivalently: make all The workflow will create the analysis environment (via conda/mamba), run preprocessing, perform k-mer counting and set operations, compute validation summaries, and regenerate manuscript-ready figures and tables. Detailed options and expected outputs are documented in README.md inside the package. Software environment and platform The package is intended for Linux/macOS/WSL environments with Bash. A conda environment specification and setup script are provided; the setup step emits an explicit error if neither conda nor mamba is available. Version information for key tools (k-mer counter, FASTA/Q toolkit, Python packages) is recorded during execution to support computational reproducibility.