Reference Overview

Complete command-line and parameter documentation are provided for each pipeline step.

Each page details all options, outputs, and usage examples for reproducibility.

Pipelines

Projection (project)

Generate k-mer embeddings from input reads (FASTA/FASTQ). Includes normalization, dimensionality reduction, and PCA preprocessing.

kmer-ord project \
    -i reads.fastq.gz \
    -o output_dir \
    -k 6 \
    --dr umap,localmap,pacmap \
    --norm clr \
    --dims 2 \
    --pca-pre \
    --keep-variance 0.9 \
    --scale large

Clustering (cluster)

cluster

Perform high-dimensional embedding followed by clustering. Supports HDBSCAN, Leiden, and DBSCAN.

kmer-ord cluster \
    -i reads.fastq.gz \
    -o output_dir \
    --dr umap \
    --dims 15 \
    --cluster hdbscan,leiden \
    --hdbscan-sweep \
    --threads 8

Visualisation (visualise)

visualise

Generate plots from an existing database. Control embedding and feature plots, and limit categorical complexity.
```
kmer-ord visualise \
    -d results/kmer-ord.sqlite \
    --embedding-mode all \
    --max-categories 15
```
Interactive Binning (bin)

bin

Launch the b2w interactive Dash interface for visual binning of reads. Supports lasso selection, overlays, and bin export.
```
kmer-ord bin -d results/kmerord.sqlite -o results/bins
```