CalcKmers #

Count the number of kmers from a read that match those in a database built from reference sequences.

[[step]]
    action = "CalcKmers"
    out_label = "mytag"
    segment = "read1"  # Any of your input segments, or 'All'
    filename = ['reference.fa', 'database.fq'] # Path (string) or list of such
    count_reverse_complement = true # whether to also include each revcomp of a kmer in the database
    k = 21
    min_count = 2  # optional, defaults to 1

This transformation:

1. Builds a kmer database from the specified sequence files (all input formatws)
2. Extracts all kmers of length k from the reference sequences
3. Filters kmers by min_count (minimum occurrences in the reference to be included)
4. For each read, counts how many of its kmers appear in the database
5. Creates a numeric tag with the kmer match count

Parameters #

• out_label: Tag name to store the kmer count
• segment: Which segment to quantify (read1, read2, index1, index2, or ‘All’)
• files: List of sequence files to build the kmer database from
• count_reverse_complement: (alias: “canonical”) Whether to include reverse complements of kmers in the database (‘canonical kmers’)
• k: Kmer length
• min_count: Minimum number of times a kmer must appear in the reference files to be included in the database (default: 1). Sum of forward and reverse complement counts if count_reverse_complement is true.

Use Cases #

• Contamination detection: Quantify or filter reads matching known contaminant sequences
• Quality control: Count kmers from adapter or primer sequences
• Species identification: Measure presence of species-specific kmers

Notes #

• Only kmers with only valid DNA bases (A, C, G, T) are counted; kmers containing N or other ambiguous bases are skipped
• Kmer matching is case-insensitive