/
gbs.jl
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/
gbs.jl
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## This file contains functions and types used for GBS data geneartion
"function yp sample QTL positions (randomly across the genome)"
function sampleQTLPosition(totalQTL::Int64)
println(
"INFO: A total of $totalQTL QTLs are sampled randomly across $numChr chromosome(s).",
)
qtlChr = sample([1:numChr...], totalQTL)
global numQTL = [sum(qtlChr .== c) for c = 1:numChr]
qtlPos = [sort(Int64.(sample([1:(chrLen[c]*1e6)...], numQTL[c]))) for c = 1:numChr]
qtlPos
end
"function to sample SNP positions (either randomly or via a two-stage appraoch)"
function sampleSNPPosition(totalSNP::Int64, winSize::Int64, mu::Float64, sigmasq::Float64)
if totalSNP != 0 ## set totalSNP = 0 to trigger option 1
println(
"INFO: A total of $totalSNP SNPs are sampled randomly across $numChr chromosome(s).",
)
snpChr = sample([1:numChr...], totalSNP) # same method as sampling QTL positions
global numSNP = [sum(snpChr .== c) for c = 1:numChr]
## option 1: sample SNP positions randomly across the genome
snpPos = [sort(Int64.(sample([1:(chrLen[c]*1e6)...], numSNP[c]))) for c = 1:numChr]
else
## option 2: sample SNP positions using densities
global numSNP = Array{Int64}(undef, 0)
snpPos = Array{Int64}(undef, 0)
for c = 1:numChr
numWin = Int(round(chrLen[c] * 1e6 / winSize)) # number of windows to be split in chromosome c (fixed window size, variable window number)
# theta = sigmasq/mu # calcualte Gamma paramter: scale
# alpha = mu^2/sigmasq # calcualte Gamma paramter: shape
density = exp.(rand(Laplace(mu, sigmasq), numWin)) # sample SNP density for each window
win = fill(winSize, numWin) # generate a vector (with length = numWin) contating windown size for each window
winPos = [0; cumsum(win)[1:end-1]] # starting postion of each window
sam = [rand(Bernoulli(1.4 * density[i]), winSize) for i = 1:numWin] # sampling the occurence of SNP at each bp using local SNP density (sampled from Gamma model for each win)
sam2 = map(x -> findall(x .> 0), sam) # record SNP positions within each window
sam3 = reduce(vcat, [sam2[i] .+ winPos[i] for i = 1:numWin]) # calcualte SNP position within chromosome
numSampled = length(sam3) # number of sampled SNP postions
numSNP = [numSNP; numSampled] # record the number of sampled SNP postions for each chromosome
snpPos = [snpPos; [sort(Int64.(sam3[randperm(numSampled)[1:numSampled]]))]] # sort SNP positions
println(
"CHROMOSOME $c: $numSampled SNPs sampled with average SNP density = $(round(mean(density),digits=3)) (window size = $winSize bp).",
)
end
end
snpPos
end
"function to sample variants (incl. SNP and QTL) allele frequency"
function sampleAlleleFrequency(numLoci::Array{Int64}, mu::Float64, sigmasq::Float64)
beta = ((mu - 1) * (mu^2 - mu + sigmasq)) / (sigmasq) # (mu * (1 - mu) / sigmasq -1) * (1 - mu)
alpha = (-beta * mu) / (mu - 1) # (mu * (1 - mu) / sigmasq -1) * mu # (-beta * mu) / (mu - 1)
af = Array{Float64}(undef, 0)
for c = 1:size(numLoci, 1)
af = [af; [rand(Beta(alpha, beta), numLoci[c])]] # sample allele frequency from a Beta distribution
end
af
end
"function to generate foudner SNPs"
function makeFounderSNPs(founders, snpAF::Array{Any,1})
for c = 1:size(snpAF, 1)
numHaps = 2 * size(founders, 1)
haps =
[rand(Bernoulli(snpAF[c][i]), 1)[1] for i = 1:size(snpAF[c], 1), j = 1:numHaps]
for i = 1:size(founders, 1)
founders[i].MatChrs[c].SNPs = haps[:, (2*i)-1]
founders[i].PatChrs[c].SNPs = haps[:, 2*i]
end
end
founders
end
"function to generate foudner QTLs"
function makeFounderQTL(founders, qtlAF::Array{Any,1})
for c = 1:size(qtlAF, 1)
numHaps = 2 * size(founders, 1)
haps =
[rand(Bernoulli(qtlAF[c][i]), 1)[1] for i = 1:size(qtlAF[c], 1), j = 1:numHaps]
for i = 1:size(founders, 1)
founders[i].MatChrs[c].QTL = haps[:, (2*i)-1]
founders[i].PatChrs[c].QTL = haps[:, 2*i]
end
end
founders
end
"function to fill haplotypes at individual level"
function fillHaplotypes(
samples,
founders,
numChr::Int64,
useChr::Array{Int64},
snpPos,
qtlPos::Array{Array{Int64,1},1},
)
if size(founders[1].MatChrs[1].QTL, 1) == 0
error("Haplotypes not made - need to run makeFounderSNPs(founders,snpAF)")
else
for c = 1:numChr
println("CHROMOSOME $(useChr[c]): Filling haplotypes!")
founderHaps = Array{Int64}(undef, size(snpPos[c], 1), 2 * size(founders, 1))
for i = 1:size(founders, 1)
founderHaps[:, 2*i-1] = founders[i].MatChrs[c].SNPs
founderHaps[:, 2*i] = founders[i].PatChrs[c].SNPs
end
for an = 1:size(samples, 1)
matOrig = [
samples[an].MatChrs[c].Origin[maximum(
findall(samples[an].MatChrs[c].Position .< snpPos[c][i]),
)] for i = 1:size(snpPos[c], 1)
]
patOrig = [
samples[an].PatChrs[c].Origin[maximum(
findall(samples[an].PatChrs[c].Position .< snpPos[c][i]),
)] for i = 1:size(snpPos[c], 1)
]
samples[an].MatChrs[c].SNPs =
[founderHaps[i, matOrig[i]] for i = 1:size(snpPos[c], 1)]
samples[an].PatChrs[c].SNPs =
[founderHaps[i, patOrig[i]] for i = 1:size(snpPos[c], 1)]
end
if size(qtlPos, 1) > 0
founderHaps = Array{Int64}(undef, size(qtlPos[c], 1), 2 * size(founders, 1))
for i = 1:size(founders, 1)
founderHaps[:, 2*i-1] = founders[i].MatChrs[c].QTL
founderHaps[:, 2*i] = founders[i].PatChrs[c].QTL
end
for an = 1:size(samples, 1)
matOrig = [
samples[an].MatChrs[c].Origin[maximum(
findall(samples[an].MatChrs[c].Position .< qtlPos[c][i]),
)] for i = 1:size(qtlPos[c], 1)
]
patOrig = [
samples[an].PatChrs[c].Origin[maximum(
findall(samples[an].PatChrs[c].Position .< qtlPos[c][i]),
)] for i = 1:size(qtlPos[c], 1)
]
samples[an].MatChrs[c].QTL =
[founderHaps[i, matOrig[i]] for i = 1:size(qtlPos[c], 1)]
samples[an].PatChrs[c].QTL =
[founderHaps[i, patOrig[i]] for i = 1:size(qtlPos[c], 1)]
end
end
println("CHROMOSOME $(useChr[c]): DONE!")
end
end
end
"function to extract haplotypes from each (diploid x2) individual"
function getHaplotypes(samples = ind)
numInd = size(samples, 1)
numChr = size(samples[1].MatChrs, 1)
numSNP = [size(samples[1].MatChrs[i].SNPs, 1) for i = 1:numChr]
totalSNP = cumsum(numSNP)
starts = [2; totalSNP[1:(end-1)] .+ 2]
ends = totalSNP .+ 1
haplotypes = Array{Int64}(undef, 2 * size(samples, 1), sum(numSNP) + 1)
for i = 1:numInd
haplotypes[2*i-1:2*i, 1] .= samples[i].ID
for c = 1:numChr
mat = samples[i].MatChrs[c].SNPs
pat = samples[i].PatChrs[c].SNPs
haplotypes[collect(1:2:2*numInd)[i], starts[c]:ends[c]] = mat
haplotypes[collect(2:2:2*numInd)[i], starts[c]:ends[c]] = pat
end
end
haplotypes
end
"function to geneate SNP genotypes"
function getSNPGenotypes(samples = ind)
numChr = size(samples[1].MatChrs, 1)
numSNP = [size(samples[1].MatChrs[i].SNPs, 1) for i = 1:numChr]
totalSNP = cumsum(numSNP) # count total number of SNPs across the genome
starts = [2; totalSNP[1:(end-1)] .+ 2]
ends = totalSNP .+ 1
genotypes = Array{Int64}(undef, size(samples, 1), sum(numSNP) + 1) # define empty array to store SNP genotypes
for i = 1:size(samples, 1)
genotypes[i, 1] = samples[i].ID
for c = 1:numChr
genos = samples[i].MatChrs[c].SNPs + samples[i].PatChrs[c].SNPs
genotypes[i, starts[c]:ends[c]] = genos
end
end
genotypes
end
"function to geneate QTL genotypes"
function getQTLGenotypes(samples = ind)
numChr = size(samples[1].MatChrs, 1)
numQTL = [size(samples[1].MatChrs[c].QTL, 1) for c = 1:numChr]
totalQTL = cumsum(numQTL)
starts = ifelse(size(totalQTL, 1) == 1, 2, [2; totalQTL[1:(end-1)] .+ 2])
ends = totalQTL .+ 1
genotypes = Array{Int64}(undef, size(samples, 1), sum(numQTL) + 1)
for i = 1:size(samples, 1)
genotypes[i, 1] = samples[i].ID
for c = 1:numChr
genos = samples[i].MatChrs[c].QTL + samples[i].PatChrs[c].QTL
genotypes[i, starts[c]:ends[c]] = genos
end
end
genotypes
end
"function to replicate values over array - Milan Bouchet-Valat (https://github.com/JuliaLang/julia/issues/16443)"
function rep(x, lengths)
if length(x) != length(lengths)
throw(DimensionMismatch("vector lengths must match"))
end
res = similar(x, sum(lengths))
i = 1
for idx = 1:length(x)
tmp = x[idx]
for kdx = 1:lengths[idx]
res[i] = tmp
i += 1
end
end
res
end
"function to sample read depth"
function sampleReadDepth(numLoci::Int64, numInd::Int64, meanDepth::Float64)
# function sampleReadDepth(numLoci::Int64, numInd::Int64, meanDepth::Float64, sigmasqReadDepth::Float64, sigmasqSampleDepth::Float64, meanCallRate::Float64, sigmasqReadCallRate::Float64, sigmasqSampleCallRate::Float64)
readDepth = rand(Gamma(meanDepth / 11, 11), numLoci)
sampleDepth = reshape(rand(Gamma(meanDepth * 3.1, 1 / 3), numInd), numInd, 1)
depthRaw = (sampleDepth * readDepth') ./ meanDepth
depthMat = [rand(NegativeBinomial(depthRaw[i, j], 0.5)) for i = 1:numInd, j = 1:numLoci]
# Caution: changes are needed here!
dp = log10.(Base.dropdims(mean(depthMat, dims = 1); dims = 1))
dp[isinf.(dp)] .= -1
# sampleCallRate = reshape(rand(Laplace(meanCallRate, sqrt(sigmasqSampleCallRate / 2)), numInd), numInd, 1)
# readCallRate = rand(Beta((meanCallRate * (1- meanCallRate) / sigmasqReadCallRate - 1) * meanCallRate, (meanCallRate * (1- meanCallRate) / sigmasqReadCallRate - 1) * (1 - meanCallRate)), numLoci)
cr = Base.dropdims(mean(depthMat .!= 0, dims = 1); dims = 1)
@. model(x, p) = p[1] / (1 + exp(-(x - p[2]) * p[3]))
p0 = [1, 0.5, 5]
fit_cr = curve_fit(model, dp, cr, p0)
add_var1 = rand(Normal(0, 0.01), numLoci)
add_var3 = rand(Beta(0.5, 1), numLoci) # rand(Uniform(-1,1), numLoci)
for i = 1:numLoci
par1 = fit_cr.param[1]
par2 = fit_cr.param[2]
par3 = fit_cr.param[3]
output = (1.1 - add_var1[i]) / (1 + exp(-(dp[i] - par2 - add_var3[i]) * par3))
if output > 1
output = 1
end
#if output > cr[i]
# output = cr[i]
#end
zeros = findall(x -> x == 0, depthMat[:, i]) #depthMat[i, sample([1:numLoci...],Int(round(numLoci*rand(Laplace(1-callRate,0.sigmasqCallRate),1))), replace=false)] .= 0
newzeros = Int64(floor((1 - output) * numInd))
potentials = deleteat!([1:numInd...], zeros)
diff = newzeros - length(zeros)
if diff > 0
setzero = sample(potentials, diff, replace = false)
s = sum(depthMat[setzero, i])
allzeros = sort([setzero; zeros...])
k = deleteat!([1:numInd...], allzeros)
l = sample(k, s, replace = true)
depthMat[setzero, i] .= 0
for j in l
depthMat[j, i] = depthMat[j, i] + 1
end
end
end
depthMat
end
"function to generate key file for simulated GBS data"
function getKeyFile(barcodeFile, numInd, flowcell, lane, numRow = 24, numCol = 16)
barcode = readdlm(barcodeFile, '\t', header = false)
useBarcode = barcode[1:numInd]
row = reduce(vcat, rep(string.(['A':'P'...]), fill(numRow, numCol)))
col = reduce(vcat, fill([1:numRow...], numCol))
key =
[fill("$flowcell", numRow * numCol)[1:numInd] fill(lane, numRow * numCol)[1:numInd] useBarcode [
"Ind_" * string(i) for i = 1:(numInd)
] fill("Plate1", numInd) row[1:numInd] col[1:numInd]]
writedlm("keyFile_$(flowcell)_$(lane).txt", key)
useBarcode
end
"""
GBS(totalQTL, totalSNP, muDensity, sigmasqDensity, winSize, muAlleleFreq, sigmasqAlleleFreq, re, meanDepth, barcodeFile, useChr, plotOutput, writeOutput, onlyOutputGBS)
Simulate Genotyping-by-Sequencing (GBS) data.
This function generates GBS reads by inserting genomic variants into _in silico_ digested genomic fragments, ligates the polymorphic sequence with barcodes and replicates based on sequencing depth.
# Arguments
* `totalQTL`: total number of QTL to be simulated
* `totalSNP`: total number of SNPs to be simulated (set to "0" if sampling SNP positions based on density)
* `muDensity`: location parameter of log-Laplace distribution (for sampling SNP density)
* `sigmasqDensity`: scale parameter of log-Laplace distribution (for sampling SNP density)
* `winSize`: Size of window and bin for sampling SNP positions
* `muAlleleFreq`: mean of sampled allele frequency
* `sigmasqAlleleFreq`: variance of sampled allele frequency
* `re`: restriction enzyme(s) to be used
* `barcodeFile`: file containing GBS barcodes
* `useChr`: either the number of chromosomes or a set of chromosome(s) to be simulated
* `plotOutput`: set to true if graphical outputs are required
* `writeOutput`: set to true if text outputs are required
* `onlyOutputGBS`: set to true if only GBS data is kept
# Examples
```julia
julia> GBS(100, 0, -2.0, 0.001, 1000000, 0.5, 0.001, [SimGBS.ApeKI], 20.0, "GBS_Barcodes.txt", [1], false, true, true)
```
"""
function GBS(
totalQTL::Int64,
totalSNP::Int64,
muSNPdensity::Float64,
sigmasqSNPdensity::Float64,
winSize::Int64,
muAlleleFreq::Float64,
sigmasqAlleleFreq::Float64,
re,
meanDepth::Float64,
barcodeFile::String,
useChr::Array{Int64},
plotOutput::Bool,
writeOutput::Bool,
onlyOutputGBS::Bool,
)
# function GBS(totalQTL::Int64, totalSNP::Int64, muSNPdensity::Float64, sigmasqSNPdensity::Float64, winSize::Int64, muAlleleFreq::Float64,sigmasqAlleleFreq::Float64, re, meanDepth::Float64, sigmasqReadDepth::Float64, sigmasqSampleDepth::Float64, meanCallRate::Float64, sigmasqReadCallRate::Float64, sigmasqSampleCallRate::Float64, barcodeFile::String, plotOutput::Bool, writeOutput::Bool,outputOnlyGBS::Bool)
## 1. sample variants
### 1.1 QTL positions, number and allele frequencies
qtlPos = sampleQTLPosition(totalQTL)
numQTL = length.(qtlPos)
qtlAF = sampleAlleleFrequency(numQTL, muAlleleFreq, sigmasqAlleleFreq)
### 1.2 SNP positions
snpPos = sampleSNPPosition(totalSNP, winSize, muSNPdensity, sigmasqSNPdensity)
numSNP = length.(snpPos)
numFrag = [length(findall(x -> x == useChr[c], GBSFrag.chr)) for c = 1:numChr] # number of GBS fragments found within each chromosome
### 1.3 SNP captured by GBS fragments
snpGBS = Array{Int64}(undef, 0) # empty array to store only SNP found on GBS fragments
hapSize = Array{Int64}(undef, 0) # empty array to store haplotype size (number of SNPs within each hap)
fragSNP = Array{Int64}(undef, 0) # empty array to store GBS fragments (conataining SNPs) index
for c = 1:numChr
#### 1.3.1 extract GBS fragments from chromosome c
chr = findall(x -> x == useChr[c], GBSFrag.chr) # index fragments from chromosome c
numFragChr = numFrag[c] # total number of selected GBS fragments
starts = GBSFrag.pos[chr] # starting position of selected GBS fragments
len = GBSFrag.len[chr] # length of selected GBS fragments
ends = starts + len .- 1 # ending position of selected GBS fragments
#### 1.3.2 select only SNPs captured by GBS fragments and non-empty GBS fragments (contain SNPs)
snpSelected = [
findall(x -> x == 1, (snpPos[c] .< ends[t]) .& (snpPos[c] .> starts[t])) for
t = 1:numFragChr
]
fragSelected = findall(x -> x != [], snpSelected) # findall(x -> x != [], [snpPos[c][snpSelected[t]] for t = 1:numFragChr])
hapSizeChr = length.(snpSelected)[fragSelected] # size of shortHaps (i.e., number of SNPs within each)
numSNPChr = sum(hapSizeChr) # total number of SNPs found on GBS fragments
numHapLoci = length(fragSelected) # total number of GBS fragments contating SNPs (or equivelently, number of shortHaps loci)
#### 1.3.3 store SNP index, shortHap size and fragment index
snpGBS = [snpGBS; [reduce(vcat, snpSelected[fragSelected])]] # store index of SNPs found on GBS fragments
# [snpGBS; [(reduce(vcat,snpSelected[fragSelected]) .+ [0;length.(snpPos)][c])]] # store index of SNPs found on GBS fragments
hapSize = [hapSize; hapSizeChr] # store shortHap size (number of SNPs with each shortHap)
fragSNP = [fragSNP; fragSelected .+ [0; numFrag][c]] # store only fragments contain SNPs
println(
"CHROMOSOME $(useChr[c]): Found $numSNPChr SNPs on $numHapLoci GBS fragments, with an average of $(round(mean(hapSizeChr),digits=2)) SNPs per GBS fragment.",
)
end
#### 1.3.4 (Optional) keep only SNPs captured by GBS fragments for the subsequent steps
if onlyOutputGBS == true
println("[INFO]: a total of $(sum(numSNP)) SNPs were sampled.")
numSNP = length.(snpGBS) # update number of SNPs
println("[INFO]: $(sum(numSNP)) SNPs captured by selected GBS SNPs.")
snpPos = [snpPos[c][snpGBS[c]] for c = 1:numChr]
end
### 1.4 SNP allele frequencies
snpAF = sampleAlleleFrequency(numSNP, muAlleleFreq, sigmasqAlleleFreq)
### 1.5 founder QTL and SNPs
makeFounderQTL(founders, qtlAF)
makeFounderSNPs(founders, snpAF)
### 1.6 fill haplotypes
@time fillHaplotypes(ind, founders, numChr, useChr, snpPos, qtlPos)
### 1.7 extract haplotypes
haps = getHaplotypes(ind)
## 2. generate GBS reads
### 2.1 sample read depth
totalInd = size(ind, 1) # number of samples to be simulated
totalFrag = length(fragSNP) # number of loci
totalDepth = Array{Int64}(undef, totalInd, totalFrag)
### 2.2 inster variants
#### 2.2.1 subset haplotype alleel by chromosome
hapStart = [1; numSNP[1:end-1] .+ 1] .+ 1
hapEnd = cumsum(numSNP) .+ 1
#### 2.2.2 genearte FASTQ file(s)
flowcell = "ABC12AAXX" # flowcell name
multiplex = 192 # 384 number of samples per lane
numLane = Int(ceil(totalInd / multiplex)) # numebr of lanes in total
for lane = 1:numLane
if totalInd < multiplex * lane
numInd = totalInd - multiplex * (lane - 1) # number of samples to be 'sequenced'
else
numInd = multiplex # number of samples to be 'sequenced'
end
readDepth = sampleReadDepth(totalFrag, numInd, meanDepth) # depth
useInd = [i + (lane - 1) * multiplex for i = 1:numInd] # which individual gets sequenced (ascending order)
totalDepth[useInd, :] = readDepth
println(
"[INFO] On Lane $lane of Flowcell $flowcell: Average GBS read depth equals to $(round(mean(readDepth),digits=2)).",
)
matHapDepth = [rand(Binomial(readDepth[i, j])) for i = 1:numInd, j = 1:totalFrag]
patHapDepth = readDepth - matHapDepth # depth of parternal haplotypes
matDepthFwd = [rand(Binomial(matHapDepth[i, j])) for i = 1:numInd, j = 1:totalFrag]
matDepthRevComp = matHapDepth - matDepthFwd
patDepthFwd = [rand(Binomial(patHapDepth[i, j])) for i = 1:numInd, j = 1:totalFrag]
patDepthRevComp = patHapDepth - patDepthFwd
println(
"[INFO] On Lane $lane of Flowcell $flowcell: Generating GBS data for $numInd samples.",
)
barcodes = getKeyFile(barcodeFile, numInd, flowcell, lane) # extract barcodes
io = GZip.open("$(flowcell)_$(lane)_fastq.txt.gz", "w") # output file
numReadsTotal = 0 # reads counter
for c = 1:numChr
##### 2.2.2.1 extract GBS fragments and SNPs
chr = findall(x -> x == useChr[c], GBSFrag.chr) # index fragments from chromosome c
numFragChr = numFrag[c] # total number of selected GBS fragments
starts = GBSFrag.pos[chr] # starting position of selected GBS fragments
len = GBSFrag.len[chr] # length of selected GBS fragments
ends = starts + len .- 1 # ending position of selected GBS fragments
frags = GBSFrag.frag[chr] # select only fragments from chromosome c
##### 2.2.2.2 select only SNPs that captured by each GBS fragment and non-empty (containing SNP(s)) GBS fragments
snpSelected = [
findall(x -> x == 1, (snpPos[c] .< ends[t]) .& (snpPos[c] .> starts[t])) for t = 1:numFragChr
]
fragSelected = findall(x -> x != [], snpSelected) # findall(x -> x != [], [snpPos[c][snpSelected[t]] for t = 1:numFragChr])
hapSizeChr = length.(snpSelected[fragSelected]) # number of SNPs within each non-empty GBS fragment
numSNPChr = sum(hapSizeChr) # total number of SNPs found on GBS fragments
numHapLoci = length(fragSelected) # total number of GBS fragments contating SNPs (or equivelently, number of shortHaps loci)
##### 2.2.2.3 genearte GBS reads with variants
hapChr = haps[:, hapStart[c]:hapEnd[c]]
for f = 1:length(fragSelected)
k = fragSelected[f]
frag = frags[k] # extract kth fragment in chromsome c
sites = snpPos[c][snpSelected[k]] .- starts[k] # (starts[k].-1) # SNP sites on this GBS fragment
startPos = [1; sites .+ 1] # starting position of segments of the specified GBS fragment
endPos = [sites .- 1; len[k]] # ending position of segments of the specified GBS fragment
seg = [SubString(frag, startPos[j], endPos[j]) for j = 1:length(startPos)] # poistional info of small segments of the specified GBS fragment
bases = [split(map(x -> nucl[x], frag[sites]), "") split(
map(x -> comp[nucl[x]], frag[sites]),
"",
)] # define SNPs (ref. allele = ref. allele found on the genome; alt. allele = complement of the ref. allele. E.g., A/T,C/G only)
hap = hapChr[:, snpSelected[k]] .+ 1 # extract shortHaps (i.e. short haplotypes defined by GBS fragmentation), adding 1 here to covert
matHap = hap[1:2:(totalInd*2), :]
patHap = hap[2:2:(totalInd*2), :]
if length(frag) < 101
stops = length(frag) # read the entire fragment if its less than 101 bp
else
stops = 101 # if the length of fragment is longer than 101, stop at 101
end
matHapChrTemp = [
join(
[
[seg[j] * bases[j, matHap[i, j]] for j = 1:length(sites)]
seg[length(sites)+1]
re[1].overhang[1]
],
) for i in useInd
]
patHapChrTemp = [
join(
[
[seg[j] * bases[j, patHap[i, j]] for j = 1:length(sites)]
seg[length(sites)+1]
re[1].overhang[1]
],
) for i in useInd
]
matHapChr = [matHapChrTemp[i][1:stops] for i = 1:numInd]
patHapChr = [patHapChrTemp[i][1:stops] for i = 1:numInd]
matHapRevCompChr = [
reverse(map(x -> comp[nucl[x]], matHapChrTemp[i]))[1:stops] for
i = 1:numInd
] # reverse complement GBS reads of maternal haplotypes
patHapRevCompChr = [
reverse(map(x -> comp[nucl[x]], patHapChrTemp[i]))[1:stops] for
i = 1:numInd
] # reverse complement GBS reads of paternal haplotypes
readGBS = collect(
Iterators.flatten([
[
rep([barcodes[i] .* matHapChrTemp[i]], matDepthFwd[i, f])
rep(
[barcodes[i] .* matHapRevCompChr[i]],
matDepthRevComp[i, f],
)
rep([barcodes[i] .* patHapChr[i]], patDepthFwd[i, f])
rep(
[barcodes[i] .* patHapRevCompChr[i]],
patDepthRevComp[i, f],
)
] for i = 1:numInd
]),
)
totalReads = shuffle(readGBS)
numReads = length(totalReads)
numReadsTotal = numReadsTotal + numReads
writedlm(
io,
[
"@SIM001:001:ABC12AAXX:$lane:0000:0000:$r 1:N:0:0\n" *
totalReads[r] *
"\n+\n" *
repeat("I", length(totalReads[r])) for r = 1:length(totalReads)
],
quotes = false,
)
end
end
println("INFO: A total of $numReadsTotal GBS reads generated.")
close(io)
end
## 3. GBS data
#### 3.1.1 SNP and QTL genotypes
snpGeno = getSNPGenotypes(ind)
qtlGeno = getQTLGenotypes(ind)
### 3.2 SNP and QTL datasets [ID chromosome position allele_frequency]
qtlData =
[["QTL_" * string(i) for i = 1:totalQTL] reduce(vcat, [repeat([useChr[i]], inner = numQTL[i]) for i = 1:numChr]) reduce(vcat, qtlPos) reduce(vcat, qtlAF)]
snpData =
[["SNP_" * string(i) for i = 1:sum(numSNP)] reduce(vcat, [repeat([useChr[i]], inner = numSNP[i]) for i = 1:numChr]) reduce(vcat, snpPos) reduce(vcat, snpAF)]
### 3.3 GBS fragments conatin SNPs
snpFragGBS = [GBSFrag.id[collect(Iterators.flatten(fragSNP))] GBSFrag.chr[collect(
Iterators.flatten(fragSNP),
)] GBSFrag.pos[collect(Iterators.flatten(fragSNP))] GBSFrag.len[collect(
Iterators.flatten(fragSNP),
)] GBSFrag.frag[collect(Iterators.flatten(fragSNP))]]
### 3.4 , shortHaps
hapIndex = [0; rep(fragSNP, hapSize)] # indexing shortHaps
hapGBS = vcat(hapIndex', haps)
### 3.5 SNP depth, call rates
snpDepth = transpose(
reshape(
vcat(
[
rep(totalDepth[i, :], collect(Iterators.flatten(hapSize))) for
i = 1:totalInd
]...,
),
sum(hapSize),
totalInd,
),
)
if writeOutput == true
writedlm("snpGeno.txt", snpGeno)
writedlm("qtlGeno.txt", qtlGeno)
writedlm("snpInfo.txt", snpData)
writedlm("qtlInfo.txt", qtlData)
writedlm("snpFragGBS.txt", snpFragGBS)
writedlm("shortHap.txt", hapGBS)
writedlm("readDepth.txt", totalDepth)
writedlm("snpDepth.txt", snpDepth)
# writedlm("allele.txt",allele[:,2:end])
end
if plotOutput == true
histogram(
snpAF,
normalize = :probability,
title = s"SNP Allele Frequency",
bins = 1000,
label = "",
fmt = :png,
)
savefig("snpAF")
histogram(
qtlAF,
normalize = :probability,
title = "QTL Allele Frequency",
bins = 1000,
label = "",
fmt = :png,
)
savefig("qtlAF")
histogram(
hapSize,
normalize = :probability,
title = "Number of SNP per GBS Fragment",
bins = 1000,
label = "",
fmt = :png,
)
savefig("snpPerTag")
end
end