Numeric Tag Generation on fastqrab documentation

Mon, 01 Jan 0001 00:00:00 +0000

CalcLength #

Extract the length of a read as a tag.

[[step]]
 action = "CalcLength"
 out_label = "mytag"
 segment = "read1" # Any of your input segments, or 'All'

This transformation creates a tag containing the length of the specified read.

Mon, 01 Jan 0001 00:00:00 +0000

CalcExpectedError #

Compute aggregated per-base error probabilities (expected errors) for each read assuming PHRED+33 qualities.

[[step]]
 action = "CalcExpectedError"
 out_label = "expected_error"
 aggregate = "sum" # or "max"
 segment = "read1" # Any of your input segments, or 'All'

If your data is not encoded as Phred+33, convert it first (for example, with ConvertQuality) before running this step. Values outside of the Phred+33 range will lead to an stop with an error.

Set aggregate = "sum" to calculate the sum of per-base error probabilities. Use aggregate = "max" to store only the worst base’s error probability for each read or read pair.

Mon, 01 Jan 0001 00:00:00 +0000

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:

Builds a kmer database from the specified sequence files (all input formatws)
Extracts all kmers of length k from the reference sequences
Filters kmers by min_count (minimum occurrences in the reference to be included)
For each read, counts how many of its kmers appear in the database
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

Mon, 01 Jan 0001 00:00:00 +0000

CalcComplexity #

[[step]]
 action = "CalcComplexity"
 out_label = "complexity"
 segment = "read1" # Any of your input segments, or 'All'

Calculate read complexity, based on the percentage of bases that are changed from their predecessor. Ranges from 0.0 to 1.0.

A good filter value might be 0.30, which means 30% complexity is required. See FilterByNumericTag.

Corresponding options in other software #

fastp: -low_complexity_filter

Mon, 01 Jan 0001 00:00:00 +0000

CalcMeanQuality #

We don’t support calculating the ‘average quality’.

This is typically a bad idea, see https://www.drive5.com/usearch/manual/avgq.html for a discussion of the issues.

To illustrate, 140 x Q35 + 10 x Q2 reads have an ‘average’ phred of 33, but 6.4 expected wrong bases. A read with 150 x Q25 has a much worse ‘average’ phred of 25, but a much lower expected number of errors at 0.5.

Corresponding options in other software #

Trimmomatic: AVGQUAL:
fastp: –average_qual

Mon, 01 Jan 0001 00:00:00 +0000

CalcQualifiedBases #

[[step]]
 action = "CalcQualifiedBases"
 threshold = 'C' # the quality value >= which a base is qualified 
 # In your phred encoding. Typically 33..75
 # a byte or a number 0...255
 op = 'worse' # see below.
 segment = "read1" # Any of your input segments, or 'All'
 out_label = "tag_name"
 relative = true # a rate (true) or a count (false)

Calculate the number of bases that are ‘qualified’, that is abov/below a user defined threshold.

Calc Base Content

Mon, 01 Jan 0001 00:00:00 +0000

CalcBaseContent #

[[step]]
 action = "CalcBaseContent"
 segment = "read1" # Any of your input segments, or 'All'
 out_label = "at_content"
 bases_to_count = "AT"
 bases_to_ignore = "N"
 relative = true # default

Counts the rate (0..=1) of bases that match bases_to_count, while removing any bases listed in bases_to_ignore from the denominator (if relative = True).

Both lists are case-insensitive, and accept only ascii letters. When no bases remain after filtering, the step returns 0.

Set relative = false to emit absolute base counts instead of a rate. Absolute mode requires bases_to_ignore to remain unset, otherwise the configuration check fails.

Calc GC Content

Mon, 01 Jan 0001 00:00:00 +0000

CalcGCContent #

[[step]]
 action = "CalcGCContent"
 segment = "read1" # Any of your input segments, or 'All'
 out_label = "gc"
 relative = true # a rate (true) or a count (false)

Count what percentage of bases are GC (as opposed to AT). Non-AGTC bases (e.g. N) are ignored.

Output is 0..100.

Wrapper around CalcBaseContent with bases = "GC", ignore="N", relative=true).

CalcNContent

Mon, 01 Jan 0001 00:00:00 +0000

CalcNCount #

[[step]]
 action = "CalcNContent"
 segment = "read1" # Any of your input segments, or 'All'
 out_label = "ncount"
 relative = true # a rate (true) or a count (false)

Count how many N are present in the read.

This step is a convenient wrapper for CalcBaseContent with bases_to_count = "N".

Corresponding options in other software #

fastp: –n_base_limit (if combined with FilterByNumericTag)