An alphabetical list of all available APIs is provided here. Documentation can be assessed in julia using the help ? command.

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• binomialExpansion(ploidy::Array{Int64,1}, freq::Float64)

Return the prior probabilities under HWE for a given ploidy and allele frequency.

julia> binomialExpansion([2,3], 0.5) for diploid and triploid with allele frequency 0f 0.50
2-element Vector{Any}:
 Any[0.25, 0.5, 0.25]
 Any[0.125, 0.375, 0.375, 0.125]

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• convertSyms(read::Reads, site::Site)

Convert symbols in the fifth element (sequence reads) of pileup (i.e. sequenced reads) to nucleotides from Reads and Site objects. Pileup format as defined here http://samtools.sourceforge.net/pileup.shtml

rawReads=Reads(".G..G...","1533474323") # 10 reads and associated base qualities in Phred scores
mySite=Site("chrom12", 835132, 'A')
julia> convertSyms(rawReads, mySite)
("AGAAGAAA", Any[])

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• calcAlleleLike(read::Reads, allele::Array{Int64,1}; phredScale::Int64=33)

Calculate allele frequency likelihoods from a Reads object.

julia> myReads=Reads("AGAAAGAAAA","1533474323")
julia> calcAlleleLike(myReads, [3], 1) # for allele G and haploid
-41.732245037944345

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• calcFreqLike(read::Reads, allele::Array{Int64,1}, maf::Float64; phredScale::Int64=33)

Calculate the likelihood (in ln format) for a given minor allele frequency, assuming haploid state.

julia> myReads=Reads("AGAAAGAAAA","1533474323")
julia> calcFreqLike(myReads, [1,3], 0.10) # for major-minor alleles A and G with minor frequency 0.10
-5.545510518505054

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• calcGenoLike(read::Reads, allele::Array{Int64,1}, ploidy::Int64; phredScale::Int64=33)

Calculate allele frequency likelihoods from a Reads object.

julia> myReads=Reads("AGAAAGAAAA","1533474323")
julia> calcGenoLike(myReads, [1,3], 2) # for alleles A and G and diploid
3-element Vector{Float64}:
 -12.002917877610738
  -7.031999955835661
 -41.732245037944345

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calcNonMajorCounts(read::Reads)

• Calculate the sum of non major alleles from a Reads object, useful to filter data based on the proportion (or count) of minor allele.

julia> myReads=Reads("AGAAAGAAAA","1533474323")
julia> calcNonMajorCounts(myReads)
2

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• filterReads(read::Reads; phredScale::Int64=33, minBaseQuality::Int64=5)

Filter reads based on minimum base quality.

julia> myReads=Reads("AGAAAGAAAA","1533474323")
julia> filterReads(myReads, minBaseQuality=20)
Reads("GG", "57")

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• optimFreq(read::Reads, allele::Array{Int64,1}, tol::Float64)

Golden-search optimization for minor allele frequency given major and minor alleles. Return likelihood value and the most likely minor allele frequency.

julia> myReads=Reads("AGAAAGAAAA","1533474323")
julia> optimFreq(myReads, [1,3], 1e-4) # for major-minor alleles A and G with tolerance for low frequency of 1e-4
(-5.122068763452686, 0.19894055410912365)

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• optimFreq_GS(read::Reads, allele::Array{Int64,1}, nGrids::Int64)

Grid-search optimization for minor allele frequency given major and minor alleles. Return likelihood value and the most likely minor allele frequency.

julia> myReads=Reads("AGAAAGAAAA","1533474323")
julia> optimFreq_GS(myReads, [1,3], 100) # for major-minor alleles A and G with grid density of 1/100
(-5.122357762466471, 0.20202020202020202)

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• snpTest(read::Reads, maxlike::Float64, allele::Array{Int64,1})

Return the (likelihood ratio test (LRT) statistic) for SNP calling.

julia> myReads=Reads("AGAAAGAAAA","1533474323")
julia> freqsMLE=optimFreq(myReads, [1,3], 1e-4) # for alleles A and G
julia> snpTest(myReads, freqsMLE[1], [1, 3])
13.761698228316105

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