BioSequences extends the Base.count method to provide some useful utilities for counting the number of sites in biological sequences.

Most generically you can count the number of sites that satisfy some condition i.e. cause some function to return true:

julia> count(isambiguous, dna"ATCGM")

You can also use two sequences, for example to compute the number of matching or mismatching symbols:

julia> count(!=, dna"ATCGM", dna"GCCGM")

julia> count(==, dna"ATCGM", dna"GCCGM")

Alias functions

A number of functions which are aliases for various invocations of Base.count are provided.

Alias functionBase.count call(s)
n_ambiguouscount(isambiguous, seq), count(isambiguous, seqa, seqb)
n_certaincount(iscertain, seq), count(iscertain, seqa, seqb)
n_gapcount(isgap, seq), count(isgap, seqa, seqb)
matchescount(==, seqa, seqb)
mismatchescount(!=, seqa, seqb)

Bit-parallel optimisations

For the vast majority of Base.count(f, seq) and Base.count(f, seqa, seqb) methods, a naive counting is done: the internal count_naive function is called, which simply loops over each position, applies f, and accumulates the result.

However, for some functions, it is possible to implement highly efficient methods that use bit-parallelism to check many elements at one time. This is made possible by the succinct encoding of BioSequences. Usually f is one of the functions provided by BioSymbols.jl or by BioSequences.jl

For such sequence and function combinations, Base.count(f, seq) is overloaded to call an internal BioSequences.count_*_bitpar function, which is passed the sequence(s). If you want to force BioSequences to use naive counting for the purposes of testing or debugging for example, then you can call BioSequences.count_naive directly.