Sequence demultiplexing

Multiplex sequencing is a technology to sequence multiple samples at the same time on a high-throughput DNA sequencer. Samples are distinguished by the short prefix of a DNA sequence called DNA barcode. The BioSequences offers the Demultiplexer type and the demultiplex function to identify the DNA barcode of a longer DNA sequence allowing small errors.

In the following example, four kinds of DNA sequences of length 4 are used as DNA barcodes. Demultiplexer takes these barcodes as its first argument with a few options:

julia> barcodes = DNASequence["ATGG", "CAGA", "GGAA", "TACG"];

julia> dplxr = Demultiplexer(barcodes, n_max_errors=1, distance=:hamming)
  distance: hamming
  number of barcodes: 4
  number of correctable errors: 1

n_max_errors specifies the number of maximum correctable errors in a barcode. The type of correctable errors depends on the distance parameter. When distance = :hamming as shown above only substitutions are correctable. When distance = :levenshtein substitutions, deletions, and insertions are correctable. The user is responsible for keeping enough distances among barcodes; Demultiplexer will throw an exception if two barcodes are within n_max_errors * 2.

The demultiplex function takes a demultiplexer object and a DNA sequence, and returns a tuple of a barcode index and a distance between the original barcode sequence and the prefix sequence:

julia> demultiplex(dplxr, dna"ATGGCGNT")  # 1st barcode with no errors
(1, 0)

julia> demultiplex(dplxr, dna"CAGGCGNT")  # 2nd barcode with one error
(2, 1)

julia> demultiplex(dplxr, dna"GGAACGNT")  # 3rd barcode with no errors
(3, 0)

julia> demultiplex(dplxr, dna"TGACCGNT")  # no matching barcode
(0, -1)

The optional third argument controls the search strategy. demultiplex uses an index to search the closest barcode within n_max_errors in the barcode set and returns it if any by default. If the third argument is true it falls back to a linear search after the index search and returns one of the closest barcodes at random. The next example shows the difference of these two strategies:

julia> demultiplex(dplxr, dna"TGACCGNT", false)  # linear search off (default)
(0, -1)

julia> demultiplex(dplxr, dna"TGACCGNT", true)   # linear search on
(3, 2)