Mercurial > repos > padge > mcdoe
changeset 0:cc0957c46408 draft
"planemo upload for repository https://github.com/kirstvh/MultiplexCrisprDOE commit b6c1b1860eee82b06ed4a592d1f9eee6886be318-dirty"
| author | padge |
|---|---|
| date | Thu, 12 May 2022 17:39:18 +0000 |
| parents | |
| children | 4a5c94d1d8bb |
| files | MultiplexCrisprDOE.jl README.rst main.jl mcdoe.xml report.jmd test-data/example_data.xlsx test-data/test_ccp_report.html test-data/test_countKOs.xlsx test-data/test_gRNA_edit.xlsx test-data/test_gRNA_reads.xlsx test-data/test_ged_report.html test-data/test_gfd_report.html test-data/test_sim_report.html |
| diffstat | 13 files changed, 4927 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/MultiplexCrisprDOE.jl Thu May 12 17:39:18 2022 +0000 @@ -0,0 +1,1048 @@ +""" + gRNA_frequency_distribution(m, + sd, + l, + u, + n_gRNA_total; + normalize = true, + visualize = false) + +Generates vector with frequencies in the combinatorial gRNA/Cas9 construct library for all gRNAs + +***INPUT*** +m: the average abundance of the gRNAs (in terms of absolute or relative frequency) +sd: the standard deviation on the gRNA abundances (in terms of absolute or relative frequency) +l: minimal gRNA abundance (in terms of absolute or relative frequency) +u: maximal gRNA abundance (in terms of absolute or relative frequency) +n_gRNA_total: the total number of gRNAs in the experiment +normalize: if set to "true", the gRNA abundances (absolute frequencies) are converted into relative frequencies +visualize: if set to "true", a histogram of all gRNA abundances is plotted + +***OUTPUT*** +p_gRNA_freq: vector with frequencies for all gRNAs in the construct library +""" +function gRNA_frequency_distribution(m, + sd, + l, + u, + n_gRNA_total; + normalize = true, + visualize = false) + + d_gRNA_freq = truncated(Normal(m, sd), l, u) # gRNA frequency distribution + p_gRNA_freq = collect(rand(d_gRNA_freq, n_gRNA_total)) # sample gRNA frequencies from distribution + + if normalize # convert into relative frequencies + p_gRNA_freq /= sum(p_gRNA_freq) + end + + if visualize + return histogram(p_gRNA_freq, label="", + xlabel="Number of reads per gRNA", + linecolor="white", + normalize=:probability, + xtickfontsize=10,ytickfontsize=10, + color=:mediumturquoise, size=(600,350), bins = 25, + ylabel="Relative frequency", + title="gRNA frequency distribution") + else + return p_gRNA_freq + end +end + +""" + gRNA_edit_distribution(f_act, + ϵ_edit_act, + ϵ_edit_inact, + sd_act, + n_gRNA_total; + visualize = false) + +Generates vector with genome editing efficiencies for all the gRNAs in the experiment. + +***INPUT*** +f_act: fraction of all gRNAs that is active +ϵ_edit_act: Average genome editing efficiency for active gRNAs - mean of the genome editing efficiency distribution for active gRNAs +ϵ_edit_inact: Average genome editing efficiency for inactive gRNAs - mean of the genome editing efficiency distribution for inactive gRNAs +sd_act: standard deviation of the genome editing efficiency distributions for active and inactive gRNAs +n_gRNA_total: the total number of gRNAs in the experiment +visualize: if set to "true", a histogram of all genome editing efficiency is plotted + +***OUTPUT*** +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +""" +function gRNA_edit_distribution(f_act, ϵ_edit_act, ϵ_edit_inact, sd_act, n_gRNA_total; visualize=false) + d_act = Binomial(1, f_act) # there is a probability f_act that a gRNA is active + d_high_act = truncated(Normal(ϵ_edit_act, sd_act), 0.01, 1) # average genome editing efficiency for active gRNAs is equal to ϵ_edit_act + d_low_act = truncated(Normal(ϵ_edit_inact, sd_act), 0.01, 1) # average genome editing efficiency for inactive gRNAs is equal to ϵ_edit_inact + p_gRNA_edit = zeros(n_gRNA_total) # initialize vector with genome editing efficiencies for gRNAs + + for i in 1:n_gRNA_total + if rand(d_act, 1) == [1] # gRNA is active + p_gRNA_edit[i] = rand(d_high_act, 1)[1] + else # gRNA is inactive + p_gRNA_edit[i] = rand(d_low_act, 1)[1] + end + end + + if visualize + return histogram(p_gRNA_edit, + normalize = :probability, + linecolor = "white", + label="", + color=:turquoise4, + xtickfontsize=10,ytickfontsize=10, xlim = (0, 1), + xticks=(0:0.1:1), + bins = 150, + xlabel="gRNA editing efficiency", + ylabel="Relative frequency", + title="gRNA genome editing effiency distribution") + + else + return p_gRNA_edit + end +end + +""" + simulate_Nₓ₁(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO; + iter = 500) + +Computes the expected value and the standard deviation of the minimal plant library size for full coverage of all single gene knockouts (E[Nx,1] and σ[Nx,1]) using simulation + +***INPUT*** +x: number of target genes in the experiment +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout +iter: number of CRISPR/Cas experiments that are simulated to obtain E[Nₓ₁] and σ[Nₓ₁] + +***OUTPUT*** +E_Nₓ₁: expected value of the plant library size for full coverage of all single gene knockouts +sd_Nₓ₁: standard deviation on the plant library size for full coverage of all single gene knockouts +""" +function simulate_Nₓ₁(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO; + iter = 500) + + @assert x * g == n_gRNA_total + Nₓ₁_vec = [] #stores number of plants to reach full coverage for each simulated experiment + for i in 1:iter + genes_vec = [] # Initialize vector to store single gene knockouts that are observed in plants + Nₓ₁ = 0 + while genes_vec != collect(1:x) # check if all possible single gene knockouts are present: if no full coverage, sample an additional plant + Nₓ₁ += 1 # count how many plants must be sampled to observe all single gene knockouts + + # sample combinatorial gRNA/Cas9 construct + gRNA_indices_construct = findall((rand(Multinomial(r, p_gRNA_freq))) .!= 0) + + # execute mutations + gRNA_indices_mutations = [gRNA for gRNA in gRNA_indices_construct if rand(Binomial(1, p_gRNA_edit[gRNA])) == 1] + + # effective gene knockout (loss-of-function) ? + gRNA_indices_KO = [gRNA for gRNA in gRNA_indices_mutations if rand(Binomial(1, ϵ_KO)) == 1] + + # which genes are knocked out? + genes_indices_KO = Int.(ceil.(gRNA_indices_KO / g)) + append!(genes_vec, genes_indices_KO) + + # update vector with observed gene knockouts + genes_vec = Int.(sort(unique(genes_vec))) + end + push!(Nₓ₁_vec, Nₓ₁) # add plant library size for full coverage of current experiment to vector + end + + # Calculate expected value and standard deviation + E_Nₓ₁ = mean(Nₓ₁_vec); + sd_Nₓ₁ = std(Nₓ₁_vec) + + return E_Nₓ₁, sd_Nₓ₁ +end + + +""" + BioCCP_Nₓ₁(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + +Computes the expected value and the standard deviation of the minimal plant library size for +full coverage of all single gene knockouts (E[Nx,1] and σ[Nx,1]) using BioCCP + +***INPUT*** +x: number of target genes in the experiment +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout + +***OUTPUT*** +E_Nₓ₁ : expected value of the plant library size for full coverage of all single gene knockouts +sd_Nₓ₁ : standard deviation on the plant library size for full coverage of all single gene knockouts +""" +function BioCCP_Nₓ₁(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + + # prepare input for BioCCP functions + p_gRNAs = p_gRNA_freq .* p_gRNA_edit * ϵ_KO # calculate probability for each gRNA to induce effective gene knockout + p_genes = [sum(p_gRNAs[i:i+g-1]) for i in 1:g:n_gRNA_total] # obtain probability of single gene knockout by summing up probability of all corresponding gRNAs to induce effective gene knockout + + # Apply BioCCP functions + E_Nₓ₁ = expectation_minsamplesize(x; p=p_genes, r=r, normalize=false) + sd_Nₓ₁ = std_minsamplesize(x; p=p_genes, r=r, normalize=false) + + return E_Nₓ₁, sd_Nₓ₁ +end + + +""" + simulate_Nₓ₂(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO; + iter=500) + +Computes the expected value and the standard deviation of the minimal plant library size for full coverage of all pairwise combinations of gene knockouts +in a multiplex CRISPR/Cas experiment (E[Nx,2] and σ[Nx,2]) using simulation + +***INPUT*** +x: number of target genes in the experiment +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout +iter: number of CRISPR/Cas experiments that are simulated to obtain E[Nₓ₂] and σ[Nₓ₂] + +***OUTPUT*** +E_Nₓ₂ : expected value of the plant library size for full coverage of all pairwise combinations of gene knockouts +sd_Nₓ₂ : standard deviation on the plant library size for full coverage of all pairwise combinations of gene knockouts +""" +function simulate_Nₓ₂(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO; + iter = 500) + + @assert x * g == n_gRNA_total + Nₓ₂_vec = [] #stores number of plants to reach full coverage of all pairwise combinations of gene knockouts for each simulated experiment + + for i in 1:iter + X_interactions_count = zeros(x, x) # Initialize matrix to count pairwise interactions + Nₓ₂ = 0 + while X_interactions_count != ones(x, x) # check if all pairwise combinations are present + Nₓ₂ += 1 # count how many plants must be sampled to fill pairwise interaction matrix + + # sample combinatorial gRNA/Cas9 construct + gRNA_indices_construct = findall((rand(Multinomial(r, p_gRNA_freq))) .!= 0) + + # execute mutations + gRNA_indices_mutations = [gRNA for gRNA in gRNA_indices_construct if rand(Binomial(1, p_gRNA_edit[gRNA])) == 1] + + # effective gene knockout (loss-of-function) ? + gRNA_indices_KO = [gRNA for gRNA in gRNA_indices_mutations if rand(Binomial(1, ϵ_KO)) == 1] + + # which genes are knocked out? + genes_indices_KO = Int.(ceil.(gRNA_indices_KO / g)) + + # which pairwise combinations are present? + interactions = collect(combinations(genes_indices_KO, 2)) + + # Store represented combinations in matrix + for interaction in interactions + j = interaction[1]; k = interaction[2] + X_interactions_count[j,k] = 1; X_interactions_count[k,j] = 1; X_interactions_count[j,j] = 1; X_interactions_count[k,k] = 1 + end + end + + push!(Nₓ₂_vec, Nₓ₂) + end + + # Calculate expected value and standard deviation + E_Nₓ₂ = mean(Nₓ₂_vec) + sd_Nₓ₂ = std(Nₓ₂_vec) + + return E_Nₓ₂, sd_Nₓ₂ +end + +""" + BioCCP_Nₓ₂(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, ϵ_KO) + +Computes the expected value and the standard deviation of the minimal plant library size for full coverage of all pairwise combinations of gene knockouts in a multiplex CRISPR/Cas experiment (E[Nx,2] and σ[Nx,2]) using BioCCP + + ***INPUT*** +x: number of target genes in the experiment +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout + +***OUTPUT*** +E_Nₓ₂: expected value of the plant library size for full coverage of all pairwise combinations of gene knockouts +sd_Nₓ₂: standard deviation on the plant library size for full coverage of all pairwise combinations of gene knockouts +""" +function BioCCP_Nₓ₂(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, ϵ_KO) + + # how many pairwise combinations of gRNAs + ind_combinations_gRNA = collect(combinations(1:n_gRNA_total, 2)) + n_combinations_gRNA = length(ind_combinations_gRNA) + + # calculate probability and activity of gRNA combinations + p_combinations_gRNA_library = zeros(n_combinations_gRNA) + p_combinations_gRNA_act = zeros(n_combinations_gRNA) + for i in 1:n_combinations_gRNA + p_combinations_gRNA_library[i] = p_gRNA_freq[ind_combinations_gRNA[i][1]] * p_gRNA_freq[ind_combinations_gRNA[i][2]] + p_combinations_gRNA_act[i] = p_gRNA_edit[ind_combinations_gRNA[i][1]] * p_gRNA_edit[ind_combinations_gRNA[i][2]] + end + + # normalize probability gRNA combinations + p_combinations_gRNA_library /= sum(p_combinations_gRNA_library) + + # select pairwise gRNA combinations of which each component codes for different gene (goal is to study combinations of knockouts in different genes) + p_combinations_gRNA_library_interest = [] + p_combinations_gRNA_act_interest = [] + ind_combinations_gRNA_interest = [] + for i in 1:n_combinations_gRNA + if ceil(ind_combinations_gRNA[i][1]/g) != ceil(ind_combinations_gRNA[i][2]/g) + push!(p_combinations_gRNA_library_interest, p_combinations_gRNA_library[i]) + push!(p_combinations_gRNA_act_interest, p_combinations_gRNA_act[i]) + push!(ind_combinations_gRNA_interest, ind_combinations_gRNA[i]) + end + end + + n_combinations_gRNA_interest = length(p_combinations_gRNA_library_interest) + p_combinations_gRNA = p_combinations_gRNA_library_interest .* p_combinations_gRNA_act_interest * ϵ_KO^2 + + # sum up probabilities or gRNA combinations for corresponding gene knockout combinations + p_genes_matrix = zeros(x, x) + for i in 1:n_combinations_gRNA_interest + gene1 = Int(ceil(ind_combinations_gRNA_interest[i][1]/g)) + gene2 = Int(ceil(ind_combinations_gRNA_interest[i][2]/g)) + p_genes_matrix[gene1, gene2] += p_combinations_gRNA[i] + end + p_genes = collect([p_genes_matrix[i, j] for j in 2:size(p_genes_matrix, 1) for i in 1:j-1]) + n_combinations_genes = length(p_genes) + combinations_pp = length(collect(combinations(1:r, 2))) + + # Apply BioCCP functions + E_Nₓ₂ = expectation_minsamplesize(n_combinations_genes; p=p_genes, r=combinations_pp, normalize=false) + sd_Nₓ₂ = std_minsamplesize(n_combinations_genes; p=p_genes, r=combinations_pp, normalize=false) + + return E_Nₓ₂, sd_Nₓ₂ +end + +""" + simulate_Nₓ₂_countKOs(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, ϵ_KO; iter=100000) + +Counts the number of knockouts per plant in the experiment. + +***INPUT*** +x: number of target genes in the experiment +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout +iter: number of plants that are sampled + +***OUTPUT*** +n_KOs_vec: vector with the number of knockouts for each plant +""" +function simulate_Nₓ₂_countKOs(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO; + iter = 100000) + + @assert x * g == n_gRNA_total + + n_KOs_vec = [] + + for j in 1:iter + # sample combinatorial gRNA/Cas9 construct + gRNA_indices_construct = findall((rand(Multinomial(r, p_gRNA_freq))) .!= 0) + + # execute mutations + gRNA_indices_mutations = [gRNA for gRNA in gRNA_indices_construct if rand(Binomial(1, p_gRNA_edit[gRNA])) == 1] + + # effective gene knockout (loss-of-function) ? + gRNA_indices_KO = [gRNA for gRNA in gRNA_indices_mutations if rand(Binomial(1, ϵ_KO)) == 1] + + # which genes are knocked out? + genes_indices_KO = Int.(ceil.(gRNA_indices_KO / g)) + + push!(n_KOs_vec, length(unique((genes_indices_KO)))) + end + + return n_KOs_vec +end + +""" + simulate_Nₓ₃(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO; + iter=500) + +Computes the expected value and the standard deviation of the minimal plant library size for full coverage of all triple combinations of gene knockouts in +a multiplex CRISPR/Cas experiment (E[Nx,3] and σ[Nx,3]) using simulation + +***INPUT*** +x: number of target genes in the experiment +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout +iter: number of CRISPR/Cas experiments that are simulated to obtain E[Nₓ₃] and σ[Nₓ₃] + +***OUTPUT*** +E_Nₓ₃: expecteded value of the plant library size for full coverage of all triple combinations of gene knockouts +sd_Nₓ₃: standard deviation on the plant library size for full coverage of all triple combinations of gene knockouts +""" +function simulate_Nₓ₃(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO; + iter = 500) + + @assert x * g == n_gRNA_total + + Nₓ₃_vec = [] # stores number of plants required for each experiment + + for i in 1:iter + # println("got till here") + X_interactions_count = zeros(x, x, x) # Initialize matrix to count triple interactions + # println(X_interactions_count) + # println(ones(x, x, x)) + Nₓ₃ = 0 + + while X_interactions_count != ones(x, x, x) # check if all triple combinations are present + Nₓ₃ += 1 # count how many plants must be sampled to fill triple interaction matrix + + # sample combinatorial gRNA/Cas9 construct + gRNA_indices_construct = findall((rand(Multinomial(r, p_gRNA_freq))) .!= 0) + + # execute mutations + gRNA_indices_mutations = [gRNA for gRNA in gRNA_indices_construct if rand(Binomial(1, p_gRNA_edit[gRNA])) == 1] + + # effective gene knockout (loss-of-function) ? + gRNA_indices_KO = [gRNA for gRNA in gRNA_indices_mutations if rand(Binomial(1, ϵ_KO)) == 1] + + # which genes are knocked out? + genes_indices_KO = Int.(ceil.(gRNA_indices_KO / g)) + + # which triple combinations are present? + interactions = collect(combinations(genes_indices_KO, 3)) + + # Store represented triple combinations in 3D-matrix + for interaction in interactions + j = interaction[1] + k = interaction[2] + l = interaction[3] + X_interactions_count[j,k,l] = 1 + X_interactions_count[k,j,l] = 1 + X_interactions_count[l,j,k] = 1 + X_interactions_count[l,k,j] = 1 + X_interactions_count[j,l,k] = 1 + X_interactions_count[k,l,j] = 1 + + X_interactions_count[:,l,l] .= 1 + X_interactions_count[:,k,k] .= 1 + X_interactions_count[:,j,j] .= 1 + X_interactions_count[l,:,l] .= 1 + X_interactions_count[k,:,k] .= 1 + X_interactions_count[j,:,j] .= 1 + + X_interactions_count[j,j,:] .= 1 + X_interactions_count[k,k,:] .= 1 + X_interactions_count[l,l,:] .= 1 + end + end + push!(Nₓ₃_vec, Nₓ₃) + end + + # calculate expected value and standard deviation + E_Nₓ₃ = mean(Nₓ₃_vec); sd_Nₓ₃ = std(Nₓ₃_vec) + + return E_Nₓ₃, sd_Nₓ₃ +end + + +""" + BioCCP_Nₓ₃(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + +Computes the expected value and the standard deviation of the minimal plant library size +for full coverage of all triple combinations of gene knockouts in a multiplex CRISPR/Cas experiment (E[Nx,3] and σ[Nx,3]) using BioCCP + +***INPUT*** +x: number of target genes in the experiment +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout + +***OUTPUT*** +E_Nₓ₃: expecteded value of the plant library size for full coverage of all triple combinations of gene knockouts +sd_Nₓ₃: standard deviation on the plant library size for full coverage of all triple combinations of gene knockouts +""" +function BioCCP_Nₓ₃(x, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + + # how many triple combinations of gRNAs + ind_combinations_gRNA = collect(combinations(1:n_gRNA_total, 3)) + n_combinations_gRNA = length(ind_combinations_gRNA) + + # calculate probability and activity of triple gRNA combinations + p_combinations_gRNA_library = zeros(n_combinations_gRNA) + p_combinations_gRNA_act = zeros(n_combinations_gRNA) + for i in 1:n_combinations_gRNA + p_combinations_gRNA_library[i] = p_gRNA_freq[ind_combinations_gRNA[i][1]] * p_gRNA_freq[ind_combinations_gRNA[i][2]] * p_gRNA_freq[ind_combinations_gRNA[i][3]] + p_combinations_gRNA_act[i] = p_gRNA_edit[ind_combinations_gRNA[i][1]] * p_gRNA_edit[ind_combinations_gRNA[i][2]] * p_gRNA_edit[ind_combinations_gRNA[i][3]] + end + + # normalize probability gRNA combinations + p_combinations_gRNA_library /= sum(p_combinations_gRNA_library) + + # select triple gRNA combinations of which each component codes for different gene (goal is to study combinations of knockouts in different genes) + p_combinations_gRNA_library_interest = [] + p_combinations_gRNA_act_interest = [] + ind_combinations_gRNA_interest = [] + for i in 1:n_combinations_gRNA + if ceil(ind_combinations_gRNA[i][1]/g) != ceil(ind_combinations_gRNA[i][2]/g) && ceil(ind_combinations_gRNA[i][1]/g) != ceil(ind_combinations_gRNA[i][3]/g) && ceil(ind_combinations_gRNA[i][3]/g) != ceil(ind_combinations_gRNA[i][2]/g) + push!(p_combinations_gRNA_library_interest, p_combinations_gRNA_library[i]) + push!(p_combinations_gRNA_act_interest, p_combinations_gRNA_act[i]) + push!(ind_combinations_gRNA_interest, ind_combinations_gRNA[i]) + end + end + + n_combinations_gRNA_interest = length(p_combinations_gRNA_library_interest) + p_combinations_gRNA = p_combinations_gRNA_library_interest .* p_combinations_gRNA_act_interest * ϵ_KO^3 + + # sum up probabilities or gRNA combinations for corresponding gene knockout combinations + p_genes_matrix = zeros(x, x, x) + for i in 1:n_combinations_gRNA_interest + gene1 = Int(ceil(ind_combinations_gRNA_interest[i][1]/g)) + gene2 = Int(ceil(ind_combinations_gRNA_interest[i][2]/g)) + gene3 = Int(ceil(ind_combinations_gRNA_interest[i][3]/g)) + p_genes_matrix[gene1, gene2, gene3] += p_combinations_gRNA[i] + end + + combinations_genes = collect(combinations(1:x, 3)) + p_genes = [] + for combination in combinations_genes + push!(p_genes, p_genes_matrix[combination[1], combination[2], combination[3]]) + end + + n_combinations_genes = length(p_genes) + combinations_pp = length(collect(combinations(1:r, 3))) + + # apply BioCCP functions + E_Nₓ₃ = expectation_minsamplesize(n_combinations_genes; p=p_genes, r=combinations_pp, normalize=false) + sd_Nₓ₃ = std_minsamplesize(n_combinations_genes; p=p_genes, r=combinations_pp, normalize=false) + + return E_Nₓ₃, sd_Nₓ₃ +end + +""" + BioCCP_Pₓ₁(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + +Computes the probability of full coverage of all single gene knockouts (Px,1) for an experiment with given plant library size using BioCCP + +***INPUT*** +x: number of target genes in the experiment +N: plant library size +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout + +***OUTPUT*** +Pₓ₁: probability of full coverage of all single gene knockouts + +""" +function BioCCP_Pₓ₁(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + + # prepare input + p_gRNAs = p_gRNA_freq .* p_gRNA_edit * ϵ_KO + p_genes = [sum(p_gRNAs[i:i+g-1]) for i in 1:g:n_gRNA_total] + + # apply BioCCP function + Pₓ₁ = success_probability(x, N; p=p_genes, r=r, normalize=false) + + return Pₓ₁ +end + +""" +BioCCP_γₓ₁(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + +Computes the expected coverage of all single gene knockouts (Px,1) for an experiment with given plant library size using BioCCP + +***INPUT*** +x: number of target genes in the experiment +N: plant library size +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout + +***OUTPUT*** +γₓ₁: expected coverage of all single gene knockouts +""" +function BioCCP_γₓ₁(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + + p_gRNAs = p_gRNA_freq .* p_gRNA_edit * ϵ_KO + p_genes = [sum(p_gRNAs[i:i+g-1]) for i in 1:g:n_gRNA_total] + + # Apply BioCCP function + γₓ₁ = expectation_fraction_collected(x, N; p=p_genes, r=r, normalize=false) + + return γₓ₁ +end + + +""" + BioCCP_Pₓ₂(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + +Computes the probability of full coverage of all pairwise combinations of gene knockouts (Px,2) for an experiment with given plant library size using BioCCP + +***INPUT*** +x: number of target genes in the experiment +N: plant library size +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout + +***OUTPUT*** +Pₓ₂: probability of full coverage of all pairwise combinations of gene knockouts +""" +function BioCCP_Pₓ₂(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + + # how many pairwise combinations of gRNAs + ind_combinations_gRNA = collect(combinations(1:n_gRNA_total, 2)) + n_combinations_gRNA = length(ind_combinations_gRNA) + + # calculate probability and activity of gRNA combinations + p_combinations_gRNA_library = zeros(n_combinations_gRNA) + p_combinations_gRNA_act = zeros(n_combinations_gRNA) + for i in 1:n_combinations_gRNA + p_combinations_gRNA_library[i] = p_gRNA_freq[ind_combinations_gRNA[i][1]] * p_gRNA_freq[ind_combinations_gRNA[i][2]] + p_combinations_gRNA_act[i] = p_gRNA_edit[ind_combinations_gRNA[i][1]] * p_gRNA_edit[ind_combinations_gRNA[i][2]] + end + + # normalize probability gRNA combinations + p_combinations_gRNA_library /= sum(p_combinations_gRNA_library) + + # select pairwise gRNA combinations of which each component codes for different gene (goal is to study combinations of knockouts in different genes) + p_combinations_gRNA_library_interest = [] + p_combinations_gRNA_act_interest = [] + ind_combinations_gRNA_interest = [] + for i in 1:n_combinations_gRNA + if ceil(ind_combinations_gRNA[i][1]/g) != ceil(ind_combinations_gRNA[i][2]/g) + push!(p_combinations_gRNA_library_interest, p_combinations_gRNA_library[i]) + push!(p_combinations_gRNA_act_interest, p_combinations_gRNA_act[i]) + push!(ind_combinations_gRNA_interest, ind_combinations_gRNA[i]) + end + end + + n_combinations_gRNA_interest = length(p_combinations_gRNA_library_interest) + p_combinations_gRNA = p_combinations_gRNA_library_interest .* p_combinations_gRNA_act_interest * ϵ_KO^2 + + # sum up probabilities or gRNA combinations for corresponding gene knockout combinations + p_genes_matrix = zeros(x, x) + for i in 1:n_combinations_gRNA_interest + gene1 = Int(ceil(ind_combinations_gRNA_interest[i][1]/g)) + gene2 = Int(ceil(ind_combinations_gRNA_interest[i][2]/g)) + p_genes_matrix[gene1, gene2] += p_combinations_gRNA[i] + end + + p_genes = collect([p_genes_matrix[i, j] for j in 2:size(p_genes_matrix, 1) for i in 1:j-1]) + n_combinations_genes = length(p_genes) + combinations_pp = length(collect(combinations(1:r, 2))) + + # Apply BioCCP function + Pₓ₂ = success_probability(n_combinations_genes, N; p=p_genes, r=combinations_pp, normalize=false) + + return Pₓ₂ +end + +""" + BioCCP_γₓ₂(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + +Computes the expected coverage of all pairwise combinations of gene knockouts (γx,2) for an experiment with given plant library size using BioCCP + +***INPUT*** +x: number of target genes in the experiment +N: plant library size +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout + +***OUTPUT*** +γₓ₂: expected coverage of all pairwise combinations of gene knockouts +""" +function BioCCP_γₓ₂(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + + # how many pairwise combinations of gRNAs + ind_combinations_gRNA = collect(combinations(1:n_gRNA_total, 2)) + n_combinations_gRNA = length(ind_combinations_gRNA) + + # calculate probability and activity of gRNA combinations + p_combinations_gRNA_library = zeros(n_combinations_gRNA) + p_combinations_gRNA_act = zeros(n_combinations_gRNA) + for i in 1:n_combinations_gRNA + p_combinations_gRNA_library[i] = p_gRNA_freq[ind_combinations_gRNA[i][1]] * p_gRNA_freq[ind_combinations_gRNA[i][2]] + p_combinations_gRNA_act[i] = p_gRNA_edit[ind_combinations_gRNA[i][1]] * p_gRNA_edit[ind_combinations_gRNA[i][2]] + end + + # normalize probability gRNA combinations + p_combinations_gRNA_library /= sum(p_combinations_gRNA_library) + + # select pairwise gRNA combinations of which each component codes for different gene (goal is to study combinations of knockouts in different genes) + p_combinations_gRNA_library_interest = [] + p_combinations_gRNA_act_interest = [] + ind_combinations_gRNA_interest = [] + for i in 1:n_combinations_gRNA + if ceil(ind_combinations_gRNA[i][1]/g) != ceil(ind_combinations_gRNA[i][2]/g) + push!(p_combinations_gRNA_library_interest, p_combinations_gRNA_library[i]) + push!(p_combinations_gRNA_act_interest, p_combinations_gRNA_act[i]) + push!(ind_combinations_gRNA_interest, ind_combinations_gRNA[i]) + end + end + + n_combinations_gRNA_interest = length(p_combinations_gRNA_library_interest) + p_combinations_gRNA = p_combinations_gRNA_library_interest .* p_combinations_gRNA_act_interest * ϵ_KO^2 + + # sum up probabilities or gRNA combinations for corresponding gene knockout combinations + p_genes_matrix = zeros(x, x) + for i in 1:n_combinations_gRNA_interest + gene1 = Int(ceil(ind_combinations_gRNA_interest[i][1]/g)) + gene2 = Int(ceil(ind_combinations_gRNA_interest[i][2]/g)) + p_genes_matrix[gene1, gene2] += p_combinations_gRNA[i] + end + + p_genes = collect([p_genes_matrix[i, j] for j in 2:size(p_genes_matrix, 1) for i in 1:j-1]) + n_combinations_genes = length(p_genes) + combinations_pp = length(collect(combinations(1:r, 2))) + + # Apply BioCCP function + γₓ₂ = expectation_fraction_collected(n_combinations_genes, N; p=p_genes, r=combinations_pp, normalize=false) + + return γₓ₂ +end + + +""" + BioCCP_γₓ₃(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + +Computes the expected coverage of all triple combinations of gene knockouts (γx,3) for an experiment with given plant library size using BioCCP + +***INPUT*** +x: number of target genes in the experiment +N: plant library size +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout + +***OUTPUT*** +γₓ₃: expected coverage of all triple combinations of gene knockouts +""" +function BioCCP_γₓ₃(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + + # how many triple combinations of gRNAs + ind_combinations_gRNA = collect(combinations(1:n_gRNA_total, 3)) + n_combinations_gRNA = length(ind_combinations_gRNA) + + # calculate probability and activity of triple gRNA combinations + p_combinations_gRNA_library = zeros(n_combinations_gRNA) + p_combinations_gRNA_act = zeros(n_combinations_gRNA) + for i in 1:n_combinations_gRNA + p_combinations_gRNA_library[i] = p_gRNA_freq[ind_combinations_gRNA[i][1]] * p_gRNA_freq[ind_combinations_gRNA[i][2]] * p_gRNA_freq[ind_combinations_gRNA[i][3]] + p_combinations_gRNA_act[i] = p_gRNA_edit[ind_combinations_gRNA[i][1]] * p_gRNA_edit[ind_combinations_gRNA[i][2]] * p_gRNA_edit[ind_combinations_gRNA[i][3]] + end + + # normalize probability gRNA combinations + p_combinations_gRNA_library /= sum(p_combinations_gRNA_library) + + # select triple gRNA combinations of which each component codes for different gene (goal is to study combinations of knockouts in different genes) + p_combinations_gRNA_library_interest = [] + p_combinations_gRNA_act_interest = [] + ind_combinations_gRNA_interest = [] + for i in 1:n_combinations_gRNA + if ceil(ind_combinations_gRNA[i][1]/g) != ceil(ind_combinations_gRNA[i][2]/g) && ceil(ind_combinations_gRNA[i][1]/g) != ceil(ind_combinations_gRNA[i][3]/g) && ceil(ind_combinations_gRNA[i][3]/g) != ceil(ind_combinations_gRNA[i][2]/g) + push!(p_combinations_gRNA_library_interest, p_combinations_gRNA_library[i]) + push!(p_combinations_gRNA_act_interest, p_combinations_gRNA_act[i]) + push!(ind_combinations_gRNA_interest, ind_combinations_gRNA[i]) + end + end + + n_combinations_gRNA_interest = length(p_combinations_gRNA_library_interest) + p_combinations_gRNA = p_combinations_gRNA_library_interest .* p_combinations_gRNA_act_interest * ϵ_KO^3 + + # sum up probabilities or gRNA combinations for corresponding gene knockout combinations + p_genes_matrix = zeros(x, x, x) + for i in 1:n_combinations_gRNA_interest + gene1 = Int(ceil(ind_combinations_gRNA_interest[i][1]/g)) + gene2 = Int(ceil(ind_combinations_gRNA_interest[i][2]/g)) + gene3 = Int(ceil(ind_combinations_gRNA_interest[i][3]/g)) + p_genes_matrix[gene1, gene2, gene3] += p_combinations_gRNA[i] + end + + combinations_genes = collect(combinations(1:x, 3)) + p_genes = [] + for combination in combinations_genes + push!(p_genes, p_genes_matrix[combination[1], combination[2], combination[3]]) + end + + n_combinations_genes = length(p_genes) + combinations_pp = length(collect(combinations(1:r, 3))) + + # Apply BioCCP function + γₓ₃ = expectation_fraction_collected(n_combinations_genes, N; p=p_genes, r=combinations_pp, normalize=false) + + return γₓ₃ +end + +""" + BioCCP_Pₓ₃(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + +Computes the probability of full coverage of all triple combinations of gene knockouts (Px,3) for an experiment with given plant library size using BioCCP + +***INPUT*** +x: number of target genes in the experiment +N: plant library size +g: number of gRNAs designed per target gene +r: number of gRNA sequences per combinatorial gRNA/Cas construct +n_gRNA_total: total number of gRNAs in the experiment +p_gRNA_freq: vector with relative frequencies for all gRNAs in the construct library (normalized!) +p_gRNA_edit: vector with genome editing efficiencies for all gRNAs +ϵ_KO: global knockout efficiency; fraction of mutations leading to effective gene knockout + +***OUTPUT*** +Pₓ₃: probability of full coverage of all triple combinations of gene knockouts +""" +function BioCCP_Pₓ₃(x, + N, + g, + r, + n_gRNA_total, + p_gRNA_freq, + p_gRNA_edit, + ϵ_KO) + + # how many triple combinations of gRNAs + ind_combinations_gRNA = collect(combinations(1:n_gRNA_total, 3)) + n_combinations_gRNA = length(ind_combinations_gRNA) + + # calculate probability and activity of triple gRNA combinations + p_combinations_gRNA_library = zeros(n_combinations_gRNA) + p_combinations_gRNA_act = zeros(n_combinations_gRNA) + for i in 1:n_combinations_gRNA + p_combinations_gRNA_library[i] = p_gRNA_freq[ind_combinations_gRNA[i][1]] * p_gRNA_freq[ind_combinations_gRNA[i][2]] * p_gRNA_freq[ind_combinations_gRNA[i][3]] + p_combinations_gRNA_act[i] = p_gRNA_edit[ind_combinations_gRNA[i][1]] * p_gRNA_edit[ind_combinations_gRNA[i][2]] * p_gRNA_edit[ind_combinations_gRNA[i][3]] + end + + # normalize probability gRNA combinations + p_combinations_gRNA_library /= sum(p_combinations_gRNA_library) + + # select triple gRNA combinations of which each component codes for different gene (goal is to study combinations of knockouts in different genes) + p_combinations_gRNA_library_interest = [] + p_combinations_gRNA_act_interest = [] + ind_combinations_gRNA_interest = [] + for i in 1:n_combinations_gRNA + if ceil(ind_combinations_gRNA[i][1]/g) != ceil(ind_combinations_gRNA[i][2]/g) && ceil(ind_combinations_gRNA[i][1]/g) != ceil(ind_combinations_gRNA[i][3]/g) && ceil(ind_combinations_gRNA[i][3]/g) != ceil(ind_combinations_gRNA[i][2]/g) + push!(p_combinations_gRNA_library_interest, p_combinations_gRNA_library[i]) + push!(p_combinations_gRNA_act_interest, p_combinations_gRNA_act[i]) + push!(ind_combinations_gRNA_interest, ind_combinations_gRNA[i]) + end + end + + n_combinations_gRNA_interest = length(p_combinations_gRNA_library_interest) + p_combinations_gRNA = p_combinations_gRNA_library_interest .* p_combinations_gRNA_act_interest * ϵ_KO^3 + + # sum up probabilities or gRNA combinations for corresponding gene knockout combinations + p_genes_matrix = zeros(x, x, x) + for i in 1:n_combinations_gRNA_interest + gene1 = Int(ceil(ind_combinations_gRNA_interest[i][1]/g)) + gene2 = Int(ceil(ind_combinations_gRNA_interest[i][2]/g)) + gene3 = Int(ceil(ind_combinations_gRNA_interest[i][3]/g)) + p_genes_matrix[gene1, gene2, gene3] += p_combinations_gRNA[i] + end + + combinations_genes = collect(combinations(1:x, 3)) + p_genes = [] + for combination in combinations_genes + push!(p_genes, p_genes_matrix[combination[1], combination[2], combination[3]]) + end + + n_combinations_genes = length(p_genes) + combinations_pp = length(collect(combinations(1:r, 3))) + + # Apply BioCCP function + Pₓ₃ = success_probability(n_combinations_genes, N; p=p_genes, r=combinations_pp, normalize=false) + + return Pₓ₃ +end + + \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.rst Thu May 12 17:39:18 2022 +0000 @@ -0,0 +1,4 @@ +MultiplexCrisprDOE +================== +provides simulation- and BioCCP-based approaches for computing the minimal plant library size +that guarantees full combinatorial coverage (and other relevant statistics) for multiplex CRISPR/Cas experiments in plants. \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/main.jl Thu May 12 17:39:18 2022 +0000 @@ -0,0 +1,612 @@ +using Pkg +Pkg.add("Plots"); +Pkg.add("Distributions"); +Pkg.add("LinearAlgebra"); +Pkg.add("Combinatorics"); +Pkg.add("BioCCP"); +Pkg.add("ArgParse"); +Pkg.add("XLSX"); +Pkg.add("DataFrames"); +Pkg.add("Weave"); +Pkg.add("DataStructures"); +Pkg.add("PrettyTables"); + +using Random +using Plots +using Distributions +using LinearAlgebra +using Combinatorics +using BioCCP +using ArgParse +using XLSX +using DataFrames +using Weave +using DataStructures +using PrettyTables + +global current_dir = pwd() +include("MultiplexCrisprDOE.jl"); + +function main(args) + + aps = ArgParseSettings("MultiplexCrisprDOE") + + @add_arg_table! aps begin + "gfd" #, "gRNA_freq_dist" + action = :command # adds a command which will be read from an argument + help = "gRNA/Cas9 frequencies" + "ged" #, "gRNA_edit_dist" + action = :command + help = "gRNA/Cas9 editing efficiencies" + "sim" # simulation + action = :command + help = "simulation-based approaches for computing the minimal plant library size that guarantees full combinatorial coverage (and other relevant statistics)" + "ccp" # bioccp + action = :command + help = "BioCCP-based approaches for computing the minimal plant library size that guarantees full combinatorial coverage (and other relevant statistics)" + end + + @add_arg_table! aps["gfd"] begin # add command arg_table: same as usual, but invoked on s["grna"] + "m" + arg_type = Int + help = "plant library size" + "sd" + arg_type = Int + help = "the standard deviation on the gRNA abundances (in terms of absolute or relative frequency)" + "l" + arg_type = Int + help = "minimal gRNA abundance (in terms of absolute or relative frequency)" + "u" + arg_type = Int + help = "maximal gRNA abundance (in terms of absolute or relative frequency)" + "n" #, "--n_gRNA_total" + arg_type = Int + help = "the total number of gRNAs in the experiment" + "--normalize" + action = :store_true + # arg_type = Bool + # default = true + help = "if provided, the gRNA abundances (absolute frequencies) are converted into relative frequencies" + "--visualize" + action = :store_true + # arg_type = Bool + # default = false + help = "if provided, a histogram of all gRNA abundances is plotted" + "--out_file" + arg_type = String + default = "gRNA_reads" + help = "Output excel file prefix" + end + + @add_arg_table! aps["ged"] begin # add command arg_table: same as usual, but invoked on s["grna"] + "f_act" + arg_type = Float16 + help = "fraction of all gRNAs that is active" + "eps_edit_act" + arg_type = Float16 + help = "Average genome editing efficiency for active gRNAs - mean of the genome editing efficiency distribution for active gRNAs" + "eps_edit_inact" + arg_type = Float16 + help = "Average genome editing efficiency for inactive gRNAs - mean of the genome editing efficiency distribution for inactive gRNAs" + "sd_act" + arg_type = Float16 + help = "standard deviation of the genome editing efficiency distributions for active and inactive gRNAs" + "n_gRNA_total" + arg_type = Int + help = "the total number of gRNAs in the experiment" + "--visualize" + action = :store_true + # arg_type = Bool + # default = false + help = "if provided a histogram of all genome editing efficiency is plotted" + "--out_file" + arg_type = String + default = "gRNA_edit" + help = "Output excel file prefix" + end + + @add_arg_table! aps["sim"] begin + "M" #, "--mode" + # action = :command + # dest_name = "M" + arg_type = Int + range_tester = x -> 1 <= x <= 4 + help = """Select simulation mode (1: simulate_Nₓ₁; 2: simulate_Nₓ₂; 3: simulate_Nₓ₃; 4: simulate_Nₓ₂_countKOs)""" + "x" + arg_type = Int + help = "number of target genes in the experiment" + "g" + arg_type = Int + help = "number of gRNAs designed per target gene" + "r" + arg_type = Int + help = "number of gRNA sequences per combinatorial gRNA/Cas construct" + "t"#, "--n_gRNA_total" + arg_type = Int + help = "total number of gRNAs in the experiment" + "f"#, "--p_gRNA_freq" + arg_type = String #Vector{Float64} + help = "vector with relative frequencies for all gRNAs in the construct library (normalized!)" + "e"#, "--p_gRNA_edit" + arg_type = String #Vector{Float64} + help = "vector with genome editing efficiencies for all gRNAs" + "E"#, "--ϵ_KO" + arg_type=Float16 + help = "global knockout efficiency; fraction of mutations leading to effective gene knockout" + "--i", "--iter" + arg_type = Int + default = 500 + help = "number of CRISPR/Cas experiments that are simulated" + end + + @add_arg_table! aps["ccp"] begin + "M"#, "--mode" + arg_type = Int + range_tester = x -> 1 <= x <= 9 + help = """Select BioCCP mode (1: BioCCP_Nₓ₁; 2: BioCCP_Nₓ₂; 3: BioCCP_Nₓ₃; 4: BioCCP_Pₓ₁; 5: BioCCP_Pₓ₂ ; + 6: BioCCP_Pₓ₃; 7: BioCCP_γₓ₁; 8: BioCCP_γₓ₂; 9: BioCCP_γₓ₃)""" + "x" + arg_type = Int + help = "number of target genes in the experiment" + "N" + arg_type = Int + help = "(Minimum) plant library size" + "--s", "--step" + arg_type = Int + default = 5 + range_tester = x -> 1 <= x <= 10 + help = "Step size for plant library size (optional for calculating expected combinatorial coverage / plant library size)" + "--MN", "--max_pl_size" + arg_type = Int + default = 4000 + help = "Maximum plant library size (optional for calculating expected combinatorial coverage / plant library size)" + "g" + arg_type = Int + help = "number of gRNAs designed per target gene" + "r" + arg_type = Int + help = "number of gRNA sequences per combinatorial gRNA/Cas construct" + "t"#, "--n_gRNA_total" + arg_type = Int + help = "total number of gRNAs in the experiment" + "f"#, "--p_gRNA_freq" + arg_type = String #Vector{Float64} + help = "File containing vector with relative frequencies for all gRNAs in the construct library (normalized!)" + "e"#, "--p_gRNA_edit" + arg_type = String #Vector{Float64} + help = "File containing vector with genome editing efficiencies for all gRNAs" + "E"#, "--ϵ_KO" + arg_type=Float16 + help = "global knockout efficiency; fraction of mutations leading to effective gene knockout" + end + + parsed_args = parse_args(args, aps) + command_args = parsed_args[parsed_args["%COMMAND%"]] + println(command_args) + + tool_info = OrderedDict() + args_info = OrderedDict() + grna_dict = Dict() + out_dict = Dict() + if parsed_args["%COMMAND%"] == "gfd" + tool_info["method"] = "gRNA_ frequency _distribution" + tool_info["description"] = "Generates vector with frequencies in the combinatorial "* + "gRNA/Cas9 construct library for all gRNAs" + tool_info["mode"] = "" + tool_info["mode_description"] = "" + args_info["Plant library size"] = command_args["m"] + args_info["SD on the gRNA abundances"] = command_args["sd"] + args_info["Minimal gRNA abundance"] = command_args["l"] + args_info["Maximal gRNA abundance"] = command_args["u"] + args_info["Total number of gRNAs"] = command_args["n"] + args_info["Convert gRNA abundances to relative frequencies"] = string(command_args["normalize"]) + args_info["Plot gRNA abundances"] = string(command_args["visualize"]) + + m = command_args["m"] + sd = command_args["sd"] + l = command_args["l"] + u = command_args["u"] + n_gRNA_total = command_args["n"] + norm = command_args["normalize"] + viz = command_args["visualize"] + + println(string(norm)) + println(string(viz)) + + p_gRNA_reads = gRNA_frequency_distribution(m, sd, l, u, n_gRNA_total; normalize = norm, visualize = false) + grna_dict["p_gRNA_reads"] = p_gRNA_reads + + # println(p_gRNA_reads) + # write to excel file + fn = command_args["out_file"] * ".xlsx" + labels = ["gRNA_read"] + columns = Vector() + push!(columns, p_gRNA_reads) + XLSX.openxlsx(fn, mode="w") do xf + sheet = xf[1] + XLSX.writetable!(sheet, columns, labels) + end + + out_dict["output file"] = fn + + elseif parsed_args["%COMMAND%"] == "ged" + tool_info["method"] = "gRNA_ edit _distribution" + tool_info["description"] = "Generates vector with genome editing efficiencies "* + "for all the gRNAs in the experiment" + tool_info["mode"] = "" + tool_info["mode_description"] = "" + args_info["Fraction of active gRNAs"] = command_args["f_act"] + args_info["Average genome editing efficiency of active gRNAs"] = command_args["eps_edit_act"] + args_info["Average genome editing efficiency of inactive gRNAs"] = command_args["eps_edit_inact"] + args_info["Standard deviation"] = command_args["sd_act"] + args_info["Total number of gRNAs"] = command_args["n_gRNA_total"] + args_info["Plot genome editing efficiency"] = string(command_args["visualize"]) + + f_act = command_args["f_act"] + eps_edit_act = command_args["eps_edit_act"] + eps_edit_inact = command_args["eps_edit_inact"] + sd_act = command_args["sd_act"] + n_gRNA_total = command_args["n_gRNA_total"] + viz = ["visualize"] + + p_gRNA_edit = gRNA_edit_distribution(f_act, eps_edit_act, eps_edit_inact, sd_act, n_gRNA_total; visualize=false) + grna_dict["p_gRNA_edit"] = p_gRNA_edit + # write to excel file + fn = command_args["out_file"] * ".xlsx" + labels = ["gRNA_edit_efficiency"] + columns = Vector() + push!(columns, p_gRNA_edit) + XLSX.openxlsx(fn, mode="w") do xf + sheet = xf[1] + XLSX.writetable!(sheet, columns, labels) + end + + out_dict["output file"] = fn + + elseif parsed_args["%COMMAND%"] == "sim" || parsed_args["%COMMAND%"] == "ccp" + + filename = command_args["f"] + sheet = 1 + data = DataFrame(XLSX.readtable(filename, sheet)...) + p_gRNA_reads = data[!,"gRNA_read"] + p_gRNA_reads_normalized = p_gRNA_reads/sum(p_gRNA_reads) # normalize + f = p_gRNA_reads_normalized + grna_dict["p_gRNA_reads"] = f + + filename = command_args["e"] + sheet = 1 + data = DataFrame(XLSX.readtable(filename, sheet)...) + p_gRNA_edit = data[!,"gRNA_edit_efficiency"] + e = p_gRNA_edit + grna_dict["p_gRNA_edit"] = e + + x = command_args["x"] + g = command_args["g"] + r = command_args["r"] + t = command_args["t"] # n_gRNA_total + E = command_args["E"] # ϵ_KO # iter = 500 + + args_info["# of target genes in the experiment"] = command_args["x"] + args_info["# of gRNAs designed per target gene"] = command_args["g"] + args_info["# of gRNAs / combi gRNA/Cas construct"] = command_args["r"] + args_info["Total number of gRNAs"] = command_args["t"] + args_info["Relative frequencies for all gRNAs"] = command_args["f"] + args_info["Genome editing efficiencies for all gRNAs"] = command_args["e"] + args_info["Global knockout efficiency"] = command_args["E"] + + if parsed_args["%COMMAND%"] == "sim" + tool_info["method"] = "simulation" + tool_info["description"] = "simulation-based approaches for computing the minimal "* + "plant library size that guarantees full combinatorial "* + "coverage (and other relevant statistics)" + i = command_args["i"] # iter = 500 + args_info["# of simulated experiments"] = command_args["i"] + + if command_args["M"] == 1 + tool_info["mode"] = "simulate_Nx1" + tool_info["mode_description"] = "Computes the expected value and the standard deviation "* + "of the minimal plant library size for full coverage of "* + "all single gene knockouts (E[Nx,1] and σ[Nx,1]) using simulation" + E_sim, sd_sim = simulate_Nₓ₁(x, g, r, t, f, e, E; iter=i) + out_dict["E_sim"] = E_sim + out_dict["sd_sim"] = sd_sim + + elseif command_args["M"] == 2 + tool_info["mode"] = "simulate_Nx2" + tool_info["mode_description"] = "Computes the expected value and the standard deviation of "* + "the minimal plant library size for full coverage of "* + "all pairwise combinations of gene knockouts in a "* + "multiplex CRISPR/Cas experiment (E[Nx,2] and σ[Nx,2]) using simulation" + + E_sim, sd_sim = simulate_Nₓ₂(x, g, r, t, f, e, E; iter=i) + out_dict["E_sim"] = E_sim + out_dict["sd_sim"] = sd_sim + + elseif command_args["M"] == 3 + tool_info["mode"] = "simulate_Nx3" + tool_info["mode_description"] = "Computes the expected value and the standard deviation of "* + "the minimal plant library size for full coverage of "* + "all triple combinations of gene knockouts in a "* + "multiplex CRISPR/Cas experiment (E[Nx,3] and σ[Nx,3]) using simulation" + + E_sim, sd_sim = simulate_Nₓ₃(x, g, r, t, f, e, E; iter=i) + out_dict["E_sim"] = E_sim + out_dict["sd_sim"] = sd_sim + + elseif command_args["M"] == 4 + tool_info["mode"] = "simulate_Nx2_countKOs" + tool_info["mode_description"] = "Counts of the number of knockouts per plant in the experiment" + + n_KOs_vec = simulate_Nₓ₂_countKOs(x, g, r, t, f, e, E; iter=i) + out_dict["n_KOs_vec"] = n_KOs_vec + + # write to excel file + fn = "countKOs.xlsx" + labels = ["countKOs"] + columns = Vector() + push!(columns, n_KOs_vec) + XLSX.openxlsx(fn, mode="w") do xf + sheet = xf[1] + XLSX.writetable!(sheet, columns, labels) + end + + out_dict["output file"] = fn + + end + + elseif parsed_args["%COMMAND%"] == "ccp" + tool_info["method"] = "BioCCP" + tool_info["description"] = "BioCCP-based approaches for computing the minimal "* + "plant library size that guarantees full combinatorial "* + "coverage (and other relevant statistics)" + + N = command_args["N"] + if haskey(command_args,"s") && haskey(command_args,"MN") + s = command_args["s"] + MN = command_args["MN"] + args_info["Step size"] = command_args["s"] + args_info["Maximum Plant library size"] = command_args["MN"] + end + args_info["Plant library size"] = command_args["N"] + + + if command_args["M"] == 1 + tool_info["mode"] = "BioCCP_Nx1" + tool_info["mode_description"] = "Computes the expected value and the standard deviation of "* + "the minimal plant library size for full coverage of all "* + "single gene knockouts (E[Nx,1] and σ[Nx,1]) using BioCCP" + + E_sim, sd_sim = BioCCP_Nₓ₁(x, g, r, t, f, e, E) + out_dict["E_sim"] = E_sim + out_dict["sd_sim"] = sd_sim + + elseif command_args["M"] == 2 + tool_info["mode"] = "BioCCP_Nx2" + tool_info["mode_description"] = "Computes the expected value and the standard deviation of "* + "the minimal plant library size for full coverage of all "* + "pairwise combinations of gene knockouts in a multiplex "* + "CRISPR/Cas experiment (E[Nx,2] and σ[Nx,2]) using BioCCP" + + E_sim, sd_sim = BioCCP_Nₓ₂(x, g, r, t, f, e, E) + out_dict["E_sim"] = E_sim + out_dict["sd_sim"] = sd_sim + + elseif command_args["M"] == 3 + tool_info["mode"] = "BioCCP_Nx3" + tool_info["mode_description"] = "Computes the expected value and the standard deviation of "* + "the minimal plant library size for full coverage of all triple combinations of "* + "gene knockouts in a multiplex CRISPR/Cas experiment (E[Nx,3] and σ[Nx,3]) using BioCCP" + + E_sim, sd_sim = BioCCP_Nₓ₃(x, g, r, t, f, e, E) + out_dict["E_sim"] = E_sim + out_dict["sd_sim"] = sd_sim + + elseif command_args["M"] == 4 + tool_info["mode"] = "BioCCP_Px1" + tool_info["mode_description"] = "Computes the probability of full coverage of "* + "all single gene knockouts (Px,1) for an experiment with given "* + "plant library size using BioCCP" + + if s != nothing && MN != nothing + plant_library_sizes = N:s:MN + else + plant_library_sizes = N + end + PT = [] + global N_95_P = nothing + for N in plant_library_sizes + Pr = BioCCP_Pₓ₁(x, N, g, r, t, f, e, E) + push!(PT, Pr) + if Pr < 0.95 + N_95_P = N + end + end + # P = BioCCP_Pₓ₁(x, N, g, r, t, f, e, E) + out_dict["P_sim"] = PT + out_dict["N_95_P"] = N_95_P + out_dict["pls"] = plant_library_sizes + + elseif command_args["M"] == 5 + tool_info["mode"] = "BioCCP_Px2" + tool_info["mode_description"] = "Computes the probability of full coverage of "* + "all pairwise combinations of gene knockouts (Px,2) "* + "for an experiment with given plant library size using BioCCP" + + if s != nothing && MN != nothing + plant_library_sizes = N:s:MN + else + plant_library_sizes = N + end + PT = [] + global N_95_P = nothing + for N in plant_library_sizes + Pr = BioCCP_Pₓ₂(x, N, g, r, t, f, e, E) + push!(PT, Pr) + if Pr < 0.95 + N_95_P = N + end + end + # P = BioCCP_Pₓ₂(x, N, g, r, t, f, e, E) + out_dict["P_sim"] = PT + out_dict["N_95_P"] = N_95_P + out_dict["pls"] = plant_library_sizes + + elseif command_args["M"] == 6 + tool_info["mode"] = "BioCCP_Px3" + tool_info["mode_description"] = "Computes the probability of full coverage of all "* + "triple combinations of gene knockouts (Px,3) for an experiment "* + "with given plant library size using BioCCP" + + if s != nothing && MN != nothing + plant_library_sizes = N:s:MN + else + plant_library_sizes = N + end + PT = [] + global N_95_P = nothing + for N in plant_library_sizes + Pr = BioCCP_Pₓ₃(x, N, g, r, t, f, e, E) + push!(PT, Pr) + if Pr < 0.95 + N_95_P = N + end + end + # P = BioCCP_Pₓ₃(x, N, g, r, t, f, e, E) + out_dict["P_sim"] = PT + out_dict["N_95_P"] = N_95_P + out_dict["pls"] = plant_library_sizes + + elseif command_args["M"] == 7 + tool_info["mode"] = "BioCCP_γx1" + tool_info["mode_description"] = "Computes the expected coverage of all "* + "single gene knockouts (E[γx,1]) for an experiment "* + "with given plant library size using BioCCP" + if s != nothing && MN != nothing + plant_library_sizes = N:s:MN + else + plant_library_sizes = N + end + exp_cov = [] + global N_95 = nothing + for N in plant_library_sizes + E_cov = BioCCP_γₓ₁(x, N, g, r, t, f, e, E) + push!(exp_cov, E_cov) + if E_cov < 0.95 + N_95 = N + end + end + # E_sim, sd_sim = BioCCP_γₓ₁(x, N, g, r, t, f, e, E) + out_dict["E_cov"] = exp_cov + out_dict["N_95"] = N_95 + out_dict["pls"] = plant_library_sizes + + elseif command_args["M"] == 8 + tool_info["mode"] = "BioCCP_γx2" + tool_info["mode_description"] = "Computes the expected coverage of all "* + "pairwise combinations of gene knockouts (E[γx,2]) for an experiment with "* + "given plant library size using BioCCP" + + if s != nothing && MN != nothing + plant_library_sizes = N:s:MN + else + plant_library_sizes = N + end + exp_cov = [] + global N_95 = nothing + for N in plant_library_sizes + E_cov = BioCCP_γₓ₂(x, N, g, r, t, f, e, E) + push!(exp_cov, E_cov) + if E_cov < 0.95 + N_95 = N + end + end + # E_sim, sd_sim = BioCCP_γₓ₂(x, N, g, r, t, f, e, E) + out_dict["E_cov"] = exp_cov + out_dict["N_95"] = N_95 + out_dict["pls"] = plant_library_sizes + + elseif command_args["M"] == 9 + tool_info["mode"] = "BioCCP_γx3" + tool_info["mode_description"] = "Computes the expected coverage of all "* + "triple combinations of gene knockouts (E[γx,3]) for an experiment with "* + "given plant library size using BioCCP" + + if s != nothing && MN != nothing + plant_library_sizes = N:s:MN + else + plant_library_sizes = N + end + exp_cov = [] + global N_95 = nothing + for N in plant_library_sizes + E_cov = BioCCP_γₓ₃(x, N, g, r, t, f, e, E) + push!(exp_cov, E_cov) + if E_cov < 0.95 + N_95 = N + end + end + # E_sim, sd_sim = BioCCP_γₓ₃(x, N, g, r, t, f, e, E) + out_dict["E_cov"] = exp_cov + out_dict["N_95"] = N_95 + out_dict["pls"] = plant_library_sizes + + end + end + end + + println(parsed_args) + println("Parsed args:") + for (key,val) in parsed_args + println(" $key => $(repr(val))") + end + println() + println("Command: ", parsed_args["%COMMAND%"]) + # h1 = histogram(grna_dict["p_gRNA_reads"], label="", + # xlabel="Number of reads per gRNA", + # linecolor="white", + # normalize=:probability, + # xtickfontsize=10,ytickfontsize=10, + # color=:mediumturquoise, size=(600,350), bins = 25, + # ylabel="Relative frequency", + # title="gRNA frequency distribution") + + # h2 = histogram(grna_dict["p_gRNA_edit"], + # normalize = :probability, + # linecolor = "white", + # label="", + # color=:turquoise4, + # xtickfontsize=10,ytickfontsize=10, xlim = (0, 1), + # xticks=(0:0.1:1), + # bins = 150, + # xlabel="gRNA editing efficiency", + # ylabel="Relative frequency", + # title="gRNA genome editing effiency distribution") + + # p_plot = plot(plant_library_sizes, Pₓ₂, label="Pₓ₂", + # title="Probability of full combinatorial coverage with respect to plant library size", + # xlabel="N", ylabel="Pₓₖ", + # xticks = (0:500:50000, string.(0:500:50000)), + # size=(900,400), color=:turquoise4, linewidth=2) + # hline!([0.95], linestyle=:dash, color=:grey, label="Pₓₖ = 0.95", legend=:bottomright) + + # exp_plot = plot(plant_library_sizes, expected_γₓ₂, + # label="E[γₓ₂]", title="Expected combinatorial coverage w.r.t. plant library size", + # xlabel="N", ylabel="E[γₓₖ]", + # xticks = (0:500:50000, string.(0:500:50000)), + # size=(800,400), color=:turquoise4, linewidth=2) + # hline!([0.95], linestyle=:dash, color=:grey, label="E[γₓₖ] = 0.95", legend=:bottomright) + + + ENV["GKSwstype"]="nul" + weave(string(@__DIR__) * "/report.jmd", + args = (parsed_args = parsed_args, + tool_info = tool_info, + args_info = args_info, + grna_dict = grna_dict, + #h1 = h1, h2 = h2, + output = out_dict); + doctype = "md2html", out_path = :pwd) + ENV["GKSwstype"]="gksqt" +end + +main(ARGS)
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/mcdoe.xml Thu May 12 17:39:18 2022 +0000 @@ -0,0 +1,341 @@ +<tool id="mcdoe" name="MultiplexCrisprDOE - simulation- and BioCCP-based approaches for computing the minimal plant library size" version="0.1.0" python_template_version="3.5"> + <requirements> + <requirement type="package" version="1.7.2">julia</requirement> + </requirements> + <command detect_errors="exit_code"><![CDATA[ + + ## method $t $x $g $r $n_gRNA_total $filename $filename $ϵ_KO --i 10 + + #if $tools.tool_selector == "gfd" + julia '$__tool_directory__/main.jl' gfd $tools.pls $tools.sd $tools.min_gRNA_abundance + $tools.max_gRNA_abundance $tools.n_gRNA_total $tools.normalize + && + mv ./gRNA_reads.xlsx $gRNA_reads + #elif $tools.tool_selector == "ged" + julia '$__tool_directory__/main.jl' ged $tools.f_act $tools.eps_edit_act $tools.eps_edit_inact + $tools.sd_act $tools.n_gRNA_total + && + mv ./gRNA_edit.xlsx $gRNA_edit + #elif $tools.tool_selector == "sim" + julia '$__tool_directory__/main.jl' sim $tools.simulation_mode.simulation_mode_selector $tools.n_target_genes + $tools.n_designed_gRNAs $tools.simulation_mode.n_gRNA_seqs $tools.n_gRNA_total $tools.p_gRNA_freq + $tools.p_gRNA_edit $tools.g_KO --iter $tools.iter + #if $tools.simulation_mode.simulation_mode_selector == "4" + && + mv ./countKOs.xlsx $countKOs + #end if + #elif $tools.tool_selector == "ccp" + julia '$__tool_directory__/main.jl' ccp $tools.bioccp_mode.bioccp_mode_selector $tools.n_target_genes $tools.pls + $tools.n_designed_gRNAs $tools.bioccp_mode.n_gRNA_seqs $tools.n_gRNA_total $tools.p_gRNA_freq + $tools.p_gRNA_edit $tools.g_KO --step $tools.step --MN $tools.max_pls + #end if + && + mv ./report.html $report + + ]]></command> + <inputs> + <conditional name="tools"> + <param name="tool_selector" type="select" label="Select tool for processing the alignment(s)"> + <option value="gfd">Generate vector with frequencies in the combinatorial gRNA/Cas9 construct library for all gRNAs</option> + <option value="ged">Generate vector with genome editing efficiencies for all the gRNAs in the experiment</option> + <option value="sim">Simulation-based approaches for computing the minimal plant library size that guarantees full combinatorial coverage</option> + <option value="ccp">BioCCP-based approaches for computing the minimal plant library size that guarantees full combinatorial coverage</option> + </param> + <when value="gfd"> + <param name="pls" type="integer" value="75" optional="false" /> + <param name="sd" type="integer" value="25" optional="false" /> + <param name="min_gRNA_abundance" value="50" type="integer" optional="false" /> + <param name="max_gRNA_abundance" value="100" type="integer" optional="false" /> + <param name="n_gRNA_total" value="120" type="integer" optional="false" /> + <param name="normalize" type="boolean" truevalue="--normalize" falsevalue="" checked="False" label="Convert gRNA abundances into relative frequencies" /> + </when> + <when value="ged"> + <param name="f_act" type="float" value="0.9" optional="false" /> + <param name="eps_edit_act" type="float" value="0.95" optional="false" /> + <param name="eps_edit_inact" type="float" value="0.1" optional="false" /> + <param name="sd_act" type="float" value="0.01" optional="false" /> + <param name="n_gRNA_total" type="integer" value="120" optional="false" /> + </when> + <when value="sim"> + <conditional name="simulation_mode"> + <param name="simulation_mode_selector" type="select" label="Select simulation mode:"> + <option value="1">simulate_Nₓ₁</option> + <option value="2">simulate_Nₓ₂</option> + <option value="3">simulate_Nₓ₃</option> + <option value="4">simulate_Nₓ₂_countKOs</option> + </param> + <when value="1"> + <param name="n_gRNA_seqs" value="1" min="1" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="2"> + <param name="n_gRNA_seqs" value="2" min="2" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="3"> + <param name="n_gRNA_seqs" value="3" min="3" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="4"> + <param name="n_gRNA_seqs" value="2" min="2" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + </conditional> + <param name="n_target_genes" value="20" label="Number of target genes in the experiment" type="integer" optional="false" /> + <param name="n_designed_gRNAs" value="6" label="Number of gRNAs designed per target gene" type="integer" optional="false" /> + <param name="n_gRNA_total" value="120" label="Total number of gRNAs in the experiment" type="integer" optional="false" /> + <param name="p_gRNA_freq" label="Vector (excel file) with relative frequencies for all gRNAs in the construct library (normalized!)" type="data" format="xlsx" optional="false" /> + <param name="p_gRNA_edit" label="Vector (excel file) with genome editing efficiencies for all gRNAs" type="data" format="xlsx" optional="false" /> + <param name="g_KO" value="0.8" label="Global knockout efficiency; fraction of mutations leading to effective gene knockout" type="float" optional="false" /> + <param name="iter" value="400" label="Number of CRISPR/Cas experiments that are simulated" type="integer" /> + </when> + <when value="ccp"> + <conditional name="bioccp_mode"> + <param name="bioccp_mode_selector" type="select" label="Select BioCCP mode:"> + <option value="1">BioCCP_Nₓ₁</option> + <option value="2">BioCCP_Nₓ₂</option> + <option value="3">BioCCP_Nₓ₃</option> + <option value="4">BioCCP_Pₓ₁</option> + <option value="5">BioCCP_Pₓ₂</option> + <option value="6">BioCCP_Pₓ₃</option> + <option value="7">BioCCP_γₓ₁</option> + <option value="8">BioCCP_γₓ₂</option> + <option value="9">BioCCP_γₓ₃</option> + </param> + <when value="1"> + <param name="n_gRNA_seqs" value="1" min="1" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="2"> + <param name="n_gRNA_seqs" value="2" min="2" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="3"> + <param name="n_gRNA_seqs" value="3" min="3" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="4"> + <param name="n_gRNA_seqs" value="1" min="1" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="5"> + <param name="n_gRNA_seqs" value="2" min="2" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="6"> + <param name="n_gRNA_seqs" value="3" min="3" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="7"> + <param name="n_gRNA_seqs" value="1" min="1" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="8"> + <param name="n_gRNA_seqs" value="2" min="2" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + <when value="9"> + <param name="n_gRNA_seqs" value="3" min="3" label="Number of gRNA sequences per combinatorial gRNA/Cas construct" type="integer" optional="false" /> + </when> + </conditional> + <param name="n_target_genes" value="20" label="Number of target genes in the experiment" type="integer" optional="false" /> + <param name="n_designed_gRNAs" value="6" label="Number of gRNAs designed per target gene" type="integer" optional="false" /> + <param name="n_gRNA_total" value="120" label="Total number of gRNAs in the experiment" type="integer" optional="false" /> + <param name="p_gRNA_freq" label="Vector (excel file) with relative frequencies for all gRNAs in the construct library (normalized!)" type="data" format="xlsx" optional="false" /> + <param name="p_gRNA_edit" label="Vector (excel file) with genome editing efficiencies for all gRNAs" type="data" format="xlsx" optional="false" /> + <param name="g_KO" value="0.8" label="Global knockout efficiency; fraction of mutations leading to effective gene knockout" type="float" optional="false" /> + <param name="pls" value="0" label="(Minimum) plant library size" type="integer" optional="true" /> + <param name="step" value="5" label="Step size for plant library size (optional for calculating expected combinatorial coverage / plant library size)" type="integer" optional="true" /> + <param name="max_pls" value="4000" label="Maximum plant library size (optional for calculating expected combinatorial coverage / plant library size)" type="integer" optional="true" /> + </when> + </conditional> + </inputs> + <outputs> + <data name="report" format="html" label="report" /> + <data name="gRNA_reads" format="xlsx" label="Vector with relative frequencies for all gRNAs in the construct library (normalized!)"> + <filter>tools['tool_selector']=='gfd'</filter> + </data> + <data name="gRNA_edit" format="xlsx" label="Vector with genome editing efficiencies for all gRNAs"> + <filter>tools['tool_selector']=='ged'</filter> + </data> + <data name="countKOs" format="xlsx" label="Vector with counts of the number of knockouts per plant in the experiment" > + <filter>tools['tool_selector']=='sim' and tools['bioccp_mode_selector']=='4'</filter> + </data> + </outputs> + <tests> + <test> + <param name="tool_selector" value="gfd" /> + <param name="pls" value="75"/> + <param name="sd" value="25" /> + <param name="min_gRNA_abundance" value="50" /> + <param name="max_gRNA_abundance" value="100" /> + <param name="n_gRNA_total" value="120" /> + <param name="normalize" value="false" /> + <output name="report" file="test_gfd_report.html" compare="sim_size" /> + <output name="gRNA_reads" file="test_gRNA_reads.xlsx" ftype="xlsx" compare="sim_size"/> + </test> + <test> + <param name="tool_selector" value="ged" /> + <param name="f_act" value="0.9"/> + <param name="eps_edit_act" value="0.95" /> + <param name="eps_edit_inact" value="0.1" /> + <param name="sd_act" value="0.01" /> + <param name="n_gRNA_total" value="120" /> + <output name="report" file="test_ged_report.html" compare="sim_size" /> + <output name="gRNA_edit" file="test_gRNA_edit.xlsx" ftype="xlsx" compare="sim_size"/> + </test> + <test> + <param name="tool_selector" value="sim" /> + <param name="simulation_mode_selector" value="4" /> + <param name="n_gRNA_seqs" value="2" /> + <param name="n_target_genes" value="20" /> + <param name="n_designed_gRNAs" value="6" /> + <param name="n_gRNA_total" value="120" /> + <param name="p_gRNA_freq" value="example_data.xlsx" /> + <param name="p_gRNA_edit" value="example_data.xlsx" /> + <param name="g_KO" value="0.8" /> + <param name="iter" value="10" /> + <output name="report" file="test_sim_report.html" compare="sim_size" /> + <output name="countKOs" file="test_countKOs.xlsx" ftype="xlsx" compare="sim_size"/> + </test> + <test> + <param name="tool_selector" value="ccp" /> + <param name="bioccp_mode_selector" value="9" /> + <param name="n_gRNA_seqs" value="3" /> + <param name="n_target_genes" value="20" /> + <param name="n_designed_gRNAs" value="6" /> + <param name="n_gRNA_total" value="120" /> + <param name="p_gRNA_freq" value="example_data.xlsx" /> + <param name="p_gRNA_edit" value="example_data.xlsx" /> + <param name="g_KO" value="0.8" /> + <param name="pls" value="0" /> + <param name="step" value="5" /> + <param name="max_pls" value="4000" /> + <output name="report" file="test_ccp_report.html" compare="sim_size" /> + </test> + </tests> + <help><![CDATA[ +usage: main.jl [-h] {gfd|ged|sim|ccp} + +MultiplexCrisprDOE + +commands: + gfd gRNA/Cas9 frequencies + ged gRNA/Cas9 editing efficiencies + sim simulation-based approaches for computing the minimal + plant library size that guarantees full combinatorial + coverage (and other relevant statistics) + ccp BioCCP-based approaches for computing the minimal plant + library size that guarantees full combinatorial coverage + (and other relevant statistics) + +usage: main.jl gfd [--normalize] [--visualize] [--out_file OUT_FILE] + [-h] [m] [sd] [l] [u] [n] + +positional arguments: + m plant library size (type: Int64) + sd the standard deviation on the gRNA abundances + (in terms of absolute or relative frequency) + (type: Int64) + l minimal gRNA abundance (in terms of absolute or + relative frequency) (type: Int64) + u maximal gRNA abundance (in terms of absolute or + relative frequency) (type: Int64) + n the total number of gRNAs in the experiment + (type: Int64) + +optional arguments: + --normalize if provided, the gRNA abundances (absolute + frequencies) are converted into relative + frequencies + --visualize if provided, a histogram of all gRNA abundances + is plotted + --out_file OUT_FILE Output excel file prefix (default: + "gRNA_reads") + -h, --help show this help message and exit + + [eps_edit_act] [eps_edit_inact] [sd_act] + [n_gRNA_total] + +positional arguments: + f_act fraction of all gRNAs that is active (type: + Float16) + eps_edit_act Average genome editing efficiency for active + gRNAs - mean of the genome editing efficiency + distribution for active gRNAs (type: Float16) + eps_edit_inact Average genome editing efficiency for inactive + gRNAs - mean of the genome editing efficiency + distribution for inactive gRNAs (type: Float16) + sd_act standard deviation of the genome editing + efficiency distributions for active and + inactive gRNAs (type: Float16) + n_gRNA_total the total number of gRNAs in the experiment + (type: Int64) + +optional arguments: + --visualize if provided a histogram of all genome editing + efficiency is plotted + --out_file OUT_FILE Output excel file prefix (default: "gRNA_edit") + -h, --help show this help message and exit + +usage: main.jl sim [--i I] [-h] [M] [x] [g] [r] [t] [f] [e] [E] + +positional arguments: + M Select simulation mode (1: simulate_Nₓ₁; 2: + simulate_Nₓ₂; 3: simulate_Nₓ₃; 4: + simulate_Nₓ₂_countKOs) (type: Int64) + x number of target genes in the experiment (type: + Int64) + g number of gRNAs designed per target gene (type: + Int64) + r number of gRNA sequences per combinatorial gRNA/Cas + construct (type: Int64) + t total number of gRNAs in the experiment (type: Int64) + f vector with relative frequencies for all gRNAs in the + construct library (normalized!) + e vector with genome editing efficiencies for all gRNAs + E global knockout efficiency; fraction of mutations + leading to effective gene knockout (type: Float16) + +optional arguments: + --i, --iter I number of CRISPR/Cas experiments that are simulated + (type: Int64, default: 500) + -h, --help show this help message and exit + +usage: main.jl ccp [--s S] [--MN MN] [-h] [M] [x] [N] [g] [r] [t] [f] + [e] [E] + +positional arguments: + M Select BioCCP mode (1: BioCCP_Nₓ₁; 2: + BioCCP_Nₓ₂; 3: BioCCP_Nₓ₃; 4: BioCCP_Pₓ₁; 5: + BioCCP_Pₓ₂ ; 6: BioCCP_Pₓ₃; 7: BioCCP_γₓ₁; 8: + BioCCP_γₓ₂; 9: BioCCP_γₓ₃) (type: Int64) + x number of target genes in the experiment + (type: Int64) + N (Minimum) plant library size (type: Int64) + g number of gRNAs designed per target gene + (type: Int64) + r number of gRNA sequences per combinatorial + gRNA/Cas construct (type: Int64) + t total number of gRNAs in the experiment (type: + Int64) + f File containing vector with relative + frequencies for all gRNAs in the construct + library (normalized!) + e File containing vector with genome editing + efficiencies for all gRNAs + E global knockout efficiency; fraction of + mutations leading to effective gene knockout + (type: Float16) + +optional arguments: + --s, --step S Step size for plant library size (optional for + calculating expected combinatorial coverage / + plant library size) (type: Int64, default: 5) + --MN, --max_pl_size MN + Maximum plant library size (optional for + calculating expected combinatorial coverage / + plant library size) (type: Int64, default: + 4000) + -h, --help show this help message and exit + ]]></help> + <citations> + <citation type="bibtex"> +@misc{githubMultiplexCrisprDOE, + author = {LastTODO, FirstTODO}, + year = {TODO}, + title = {MultiplexCrisprDOE}, + publisher = {GitHub}, + journal = {GitHub repository}, + url = {https://github.com/kirstvh/MultiplexCrisprDOE}, +}</citation> + </citations> +</tool> \ No newline at end of file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/report.jmd Thu May 12 17:39:18 2022 +0000 @@ -0,0 +1,102 @@ +--- +title: MultiplexCrisprDOE +--- + +<!-- this setup dependencies, but doesn't appear in the generated document --> +```julia; echo = false; results = "hidden" +using Pkg +"Plots" ∉ keys(Pkg.project().dependencies) && Pkg.add("Plots") +#"DSP" ∉ keys(Pkg.project().dependencies) && Pkg.add("DSP") +#"Images" ∉ keys(Pkg.project().dependencies) && Pkg.add("Images") +"DataStructures" ∉ keys(Pkg.project().dependencies) && Pkg.add("DataStructures") +"PrettyTables" ∉ keys(Pkg.project().dependencies) && Pkg.add("PrettyTables") +"DataFrames" ∉ keys(Pkg.project().dependencies) && Pkg.add("DataFrames") +"Latexify" ∉ keys(Pkg.project().dependencies) && Pkg.add("Latexify") +``` +## Tool + +* **Method:** `j println(WEAVE_ARGS.tool_info["method"])` + +* **Description:** `j println(WEAVE_ARGS.tool_info["description"])` + +* **Mode:** `j println(WEAVE_ARGS.tool_info["mode"])` + +* **Mode description:** `j println(WEAVE_ARGS.tool_info["mode_description"])` + +## Variables + +```julia; echo = false +using DataFrames +using PrettyTables +df = DataFrame("Argument" => collect(keys(WEAVE_ARGS.args_info)), "Value" => collect(values(WEAVE_ARGS.args_info))) +#pt = pretty_table(df, nosubheader=true; alignment=:l) +``` + +```julia; echo = false + using Plots + if haskey(WEAVE_ARGS.grna_dict,"p_gRNA_reads") + h1 = histogram(WEAVE_ARGS.grna_dict["p_gRNA_reads"], label="", + xlabel="Number of reads per gRNA", + linecolor="white", + normalize=:probability, + xtickfontsize=10,ytickfontsize=10, + color=:mediumturquoise, size=(600,350), bins = 25, + ylabel="Relative frequency", + title="gRNA frequency distribution") + display(h1) + end +``` + +```julia; echo = false + using Plots + if haskey(WEAVE_ARGS.grna_dict,"p_gRNA_edit") + h2 = histogram(WEAVE_ARGS.grna_dict["p_gRNA_edit"], + normalize = :probability, + linecolor = "white", + label="", + color=:turquoise4, + xtickfontsize=10,ytickfontsize=10, xlim = (0, 1), + xticks=(0:0.1:1), + bins = 150, + xlabel="gRNA editing efficiency", + ylabel="Relative frequency", + title="gRNA genome editing effiency distribution") + display(h2) + end +``` + +```julia; echo = false + using Plots + if haskey(WEAVE_ARGS.output,"output file") + println("Output written to:") + println(WEAVE_ARGS.output["output file"]) + elseif haskey(WEAVE_ARGS.output,"E_sim") + E_sim = WEAVE_ARGS.output["E_sim"] + sd_sim = WEAVE_ARGS.output["sd_sim"] + k = WEAVE_ARGS.args_info["# of gRNAs / combi gRNA/Cas construct"] + x = WEAVE_ARGS.args_info["# of target genes in the experiment"] + println("**How many plants need to be included in the plant library (on average) to obtain full coverage of all k-combinations of gene knockouts?**") + println("On average, ", Int(ceil(E_sim)), " plants need to be sampled at random to observe all ", k, "-combinations of ", x, " gene knockouts. Standard deviation = ", Int(ceil(sd_sim)), " plants") + elseif haskey(WEAVE_ARGS.output,"P_sim") + p = plot(WEAVE_ARGS.output["pls"], WEAVE_ARGS.output["P_sim"], label="Pₓ₂", + title="Probability of full combinatorial coverage with respect to plant library size", + xlabel="N", ylabel="Pₓₖ", + xticks = (0:500:50000, string.(0:500:50000)), + size=(900,400), color=:turquoise4, linewidth=2) + hline!([0.95], linestyle=:dash, color=:grey, label="Pₓₖ = 0.95", legend=:bottomright) + display(p) + println("At a given number of plants, what is the probability that all pairwise combinations of gene knockouts are observed?") + println("N_95_P: ", WEAVE_ARGS.output["N_95_P"]) + elseif haskey(WEAVE_ARGS.output,"E_cov") + p = plot(WEAVE_ARGS.output["pls"], WEAVE_ARGS.output["E_cov"], + label="E[γₓ₂]", title="Expected combinatorial coverage w.r.t. plant library size", + xlabel="N", ylabel="E[γₓₖ]", + xticks = (0:500:50000, string.(0:500:50000)), + size=(800,400), color=:turquoise4, linewidth=2) + hline!([0.95], linestyle=:dash, color=:grey, label="E[γₓₖ] = 0.95", legend=:bottomright) + display(p) + println("At a given number of plants, what is the expected coverage of pairwise gene knockout combinations?") + println("N_95: ", WEAVE_ARGS.output["N_95"]) + end +``` +
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test_ccp_report.html Thu May 12 17:39:18 2022 +0000 @@ -0,0 +1,707 @@ +<!DOCTYPE html> +<HTML lang = "en"> +<HEAD> + <meta charset="UTF-8"/> + <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes"> + <title>MultiplexCrisprDOE</title> + + + <script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}, + TeX: { equationNumbers: { autoNumber: "AMS" } } + }); + </script> + + <script type="text/javascript" async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> + </script> + + +<style> +pre.hljl { + border: 1px solid #ccc; + margin: 5px; + padding: 5px; + overflow-x: auto; + color: rgb(68,68,68); background-color: rgb(251,251,251); } +pre.hljl > span.hljl-t { } +pre.hljl > span.hljl-w { } +pre.hljl > span.hljl-e { } +pre.hljl > span.hljl-eB { } +pre.hljl > span.hljl-o { } +pre.hljl > span.hljl-k 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+<li><p><strong>Mode description:</strong> Computes the expected coverage of all triple combinations of gene knockouts (E[γx,3]) for an experiment with given plant library size using BioCCP</p> +</li> +</ul> +<h2>Variables</h2> + + +<div class="data-frame"><p>10 rows × 2 columns</p><table class="data-frame"><thead><tr><th></th><th>Argument</th><th>Value</th></tr><tr><th></th><th title="Any">Any</th><th title="Any">Any</th></tr></thead><tbody><tr><th>1</th><td># of target genes in the experiment</td><td>20</td></tr><tr><th>2</th><td># of gRNAs designed per target gene</td><td>6</td></tr><tr><th>3</th><td># of gRNAs / combi gRNA/Cas construct</td><td>3</td></tr><tr><th>4</th><td>Total number of gRNAs</td><td>120</td></tr><tr><th>5</th><td>Relative frequencies for all gRNAs</td><td>example_data.xlsx</td></tr><tr><th>6</th><td>Genome editing efficiencies for all gRNAs</td><td>example_data.xlsx</td></tr><tr><th>7</th><td>Global knockout efficiency</td><td>0.8</td></tr><tr><th>8</th><td>Step size</td><td>5</td></tr><tr><th>9</th><td>Maximum Plant library size</td><td>6000</td></tr><tr><th>10</th><td>Plant library size</td><td>0</td></tr></tbody></table></div> + +<img 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" 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" /> + +<pre class="output"> +At a given number of plants, what is the expected coverage of pairwise gene + knockout combinations? +N_95: 6000 +</pre> + +<img src="data:image/png;base64,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/> + + + <HR/> + <div class="footer"> + <p> + Published from <a href="report.jmd">report.jmd</a> + using <a href="http://github.com/JunoLab/Weave.jl">Weave.jl</a> v0.10.10 on 2022-05-12. + </p> + </div> + </div> + </div> + </div> +</BODY> + +</HTML>
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red +} + +code, +kbd, +pre, +samp { + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; + font-size: 0.9em; +} + + +@media (min-width: 400px) {} +@media (min-width: 550px) {} +@media (min-width: 750px) {} +@media (min-width: 1000px) {} +@media (min-width: 1200px) {} + +h1.title {margin-top : 20px} +img {max-width : 100%} +div.title {text-align: center;} + + </style> +</HEAD> + +<BODY> + <div class ="container"> + <div class = "row"> + <div class = "col-md-12 twelve columns"> + <div class="title"> + <h1 class="title">MultiplexCrisprDOE</h1> + + + </div> + + +<!-- this setup dependencies, but doesn't appear in the generated document --> + + + +<h2>Tool</h2> +<ul> +<li><p><strong>Method:</strong> gRNA_ edit _distribution</p> +</li> +<li><p><strong>Description:</strong> Generates vector with genome editing efficiencies for all the gRNAs in the experiment</p> +</li> +<li><p><strong>Mode:</strong> </p> +</li> +<li><p><strong>Mode description:</strong> </p> +</li> +</ul> +<h2>Variables</h2> + + +<div class="data-frame"><p>6 rows × 2 columns</p><table class="data-frame"><thead><tr><th></th><th>Argument</th><th>Value</th></tr><tr><th></th><th title="Any">Any</th><th title="Any">Any</th></tr></thead><tbody><tr><th>1</th><td>Fraction of active gRNAs</td><td>0.9</td></tr><tr><th>2</th><td>Average genome editing efficiency of active gRNAs</td><td>0.95</td></tr><tr><th>3</th><td>Average genome editing efficiency of inactive gRNAs</td><td>0.1</td></tr><tr><th>4</th><td>Standard deviation</td><td>0.01</td></tr><tr><th>5</th><td>Total number of gRNAs</td><td>120</td></tr><tr><th>6</th><td>Plot genome editing efficiency</td><td>false</td></tr></tbody></table></div> + + +<img 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nzmmUxCyIIFCwghrKfyKHl5edevX/fx8aFSHW3s2LHOzs5RUVFisZgQcuHCBZFING3aNGYfoaenJ3U424L09fWXLFnCLJFvTKlUOnv2bGaXjJub2/jx45tdOHWi+7PPPmOeoeXxeNRPqszX+maOHTsmFos//vhjmX471i+id+/e1N4DxdLSsnfv3kKhMC8vj14aIWTFihWsXaqEEC6XO3fu3Kampv/+97904ZEjR2pqav7973+z9mJS/vjjj8bGxo8++qhNmzZ0oYuLC5USlKDO7sbHxyuvpsRnn32mYs0FCxYwh3cOHjy4Z8+eL168oPfqWpZAIIiMjDQ0NKSOumjU6RPqwgBm+YoVK5j9iyNGjCCMFbXVYRAHC+p6Ruq8FhPVXyrP0dGxffv2EomkoKAgIyPDyMho6NChZmZmSt5i6dKlu3fvPnDgwPLly2UGelBEItHRo0f19PT+9a9/0YUzZsz4/vvvDxw4sGrVKiW91ori79SpE5XbKElJSYSQly9fUusuk6Gh4fPnz5klrq6uMr8UVHandkUJIY8ePSKEyHeuUNkxLS2NWUgdWjHZ2dnV1tbKjPiws7NjvgUV8IMHD2QCrqqqIoTIBMxE7YSSv7ZSJg6HU1NTU1ZWZm9vn56eTgjp0qWLTJ2uXbuePHlS0cLfAHU8wSyRaczXXQOZqFaKioqS+QXMzs4mSltJddRb3Lt3T6Y9y8rK5N9Cpt+RMD4sNRwgOTmZsK05TLNnz/7666/DwsJWrlxJrfm//PILVa5kLiVf6KFDh5TMOHny5E2bNn3xxRcnTpwYPXp0YGDgkCFDVL+KwNjYmBrQoQr58Lp165aQkJCWlqb66CTVZWZmNjU1dejQQWZ3wd3d3dbWtrS0tLCwkDrFSpH5+qjvrqioqMUDezNIYCwaGhoIIfKjwlhHxBFC/vWvf1GX1BBC7t69GxwcPGfOHG9vb+r0ESsjI6PPP/98wYIFX375JXU+XcaFCxeKiopcXFwOHjzILLe2ts7MzIyNjR04cODrxi9TQp3OSk5OpjrtmAwNDWXGOsrv51KnSiQSCTVZW1tL/lq/maiSmpoaVZYmUy7zFpWVlYSQmJgY6jQLk5WVFXOYgAxqxufPn7NegWdlZUWdT6M+ApU1mWQGbb49+Y9P/SjTn/R110Am6sOePn1afhfHyspK+Wg9FVFrzuXLl6mT0jJvIVPY7JpTXV1NCFE+rtLW1nb8+PHHjh27du3a0KFD7927d//+/cGDB8vneCbqC5X/+pr9Qn18fOLj47/88svo6OjNmzdv3rzZ2Nh40aJF33zzjSqX9Nna2qrezorWN5lNpqUoahNCiIODQ2lpaXV1NTOBsW6SUq25gy4SGAvqZ6KwsFCmPD8/v9l5e/fuvXfv3gkTJsyZMyc5OVnJkPfZs2dv3bo1IiKCGkwlY//+/YSQvLw8+YMGQsiBAweUJDAqfvlb1MiUUMeIS5YsobPv26CWRh9D0KidNeY1v2/5Fv/9739f95weNWNwcHBERESz1UpKSmTK5T+UuilaA1W57RD1KWJjY5X/uL8N6i2OHz8uc1L6zVB5Oj8/X/n47Pnz5x87diwsLGzo0KHUjkhoaKgqccp/fap8oV26dDl79mx9ff3t27ejoqL279+/adOmpqamH3/8sdl5X2svQdH6Rm8yMjs3FLpn+nUpahPy16ZqYWHxZktuFegDY0GNnZPfzZcvYTV+/PhBgwZlZGQov+MGn8//5ptvpFKp/AVhRUVFkZGRZmZmly9fjn7VH3/8YWhoeOrUKWpPSkn89K3tKNK/+odp1EmbuLg4VT5Us6izW/In7qlRGy1yIwAq4NjY2DebMT4+nurrUoT6CNT5MSb6DKQSfD5fJBK9bmCKKFoDr127puK8LfW1smrZNYdaWrONPHDgQH9//99//z07O/vkyZM2Njbjxo1TPgu11r3ZF0oxNjYeNmzYxo0bqavxqCugCSFU/2KLfOPy4VEbEb3JODo6ErmUQ50dZVIxJC8vLwMDg+fPn5eWljLLs7Ozy8rKbGxsqLfTFUhgLEaMGOHk5BQdHU2Nj6LcunWLeTtX5b766itCyPr166lzQYp89NFHXbp0iY6OvnXrFrP80KFDIpHogw8+GDFiRNCrRo0aNWrUqNraWnpbkjdx4kQjI6NDhw4x1/Ljx49TnQ20oUOHenh4/Pnnn6wd+0oSJKv33ntPX1//9OnTWVlZdGFZWdnPP/9MhfRaS2M1ZcoUfX398PDwzMxMmZekjNGS8jw8PAIDAwsKCn766Sf5V+lP+u677/L5/KNHjzIPfbKyss6cOdNsbG3atBEIBDI/Cm9s3LhxZmZmp06dYv7Unj17VpWcQQ012rhxo/wdhIVCITV67S1Nnz6dx+Pt3btXvkdNIpHU19e/1tKmTZtGCNmyZUuzrTdv3rympqZx48bV1NTMnDmz2bN5o0ePpvb2cnJy6MIXL16wnrRnkl/57e3teTwe3XpWVlbGxsb5+fkyB0ZvYPfu3cwWu3LlSlJSUtu2bakdEUIIdQEycyOVSqXyd7GiBqrQ91xVxMDA4N13321sbJQ5lKQWOGHChBY5yawxSGAs9PX1d+/ezePxgoOD582bt23btoULF44YMYIaKqaKYcOGBQQEFBYWUtfAKsLlcqnTd8wNjBBCjbairuaRR5UrGeXo4OCwYcMGgUDQr1+/5cuXb9u2bcaMGTNmzKDip1dQPp8fHh6up6c3duzYFStWREZGpqSkREVF7dixo0+fPt99952KH5Z+09WrVwuFwqCgoBMnTqSlpZ07dy4wMLCiomLatGkyAx3fjLu7+4YNG6qqqvr3779x48arV6+mpKScP3/+u+++8/LyunjxopJ59+7da2lpuWzZslmzZp05cyYlJeXatWv79u1755136HGeDg4OK1asqK+vHzZs2Pnz5zMzMyMiIkaMGMHsElCEGgc/YcKEH374ISwsTNGdMFVkaWm5bds2kUgUEBCwZMmSbdu2zZ49e+LEidQYMOU/MUOHDp0zZ05WVlbPnj137doVGxublJT022+/ffrpp66uri1yw3UvL69vv/22rKysb9++W7ZsuXbt2sOHD8+fP//111+3a9eOudunigEDBsydOzc3N7dPnz7h4eEpKSnx8fH79+8PCAiQOWk/ffp0c3Pzhw8fcjicuXPnNrtka2vr1atXNzY2Dh8+/MyZM5mZmb///ntQUFCzBxnTp08fOXLk/v37Y2Njnzx5Eh0dPW7cOJFINGnSJLpOz549i4uLP/zww+3bt4eFhal4ekYej8cbMWLElStXnjx5cuDAAeoKjY0bN9KXUY8dO9bQ0DAsLGzNmjW3bt2KiIgYOXKk/D5cx44djY2Nf/vtt+XLl+/evTssLEy+DmXdunUmJiYbNmz47LPP7t+/f/fu3fnz5//yyy82NjasF7NrtdYbwa/tzp8/Tw+Wc3Jy2rlzJzVAec2aNXQdRXfikEql0dHR1IzURR7M68Bk0IOMqevAqL1se3t7oVDIGlhDQwPVbfD48WMl8f/yyy8uLi7Uktu2bXvy5Elq1D5103HajRs35Me2ubi4HD16lKpA34lDZvl79+4lr14RIpFIvv76a2a3H5fLnT9/fmNjI12HvhOHzNKowfcyFyNTzcu8qFMqlR48eFC+w79jx47Mq7JYPXr0SL7j0MbGZsOGDXQdkUi0YMECZoYYO3ZseHg4ae46sJKSkjFjxtAj15u9E4dMbNu3bydyN0k5evQofV8SNze3gwcPbt26Vb6aPLFYvG7dOplxsFwut2/fvvQVUW9zHRglLCxMfixA586dk5KSqAr0nThk3oI65IqNjaVLRCLRmjVrZI6onJycZC74k/51udKQIUOUtwCzKZYuXcr8QoODg6khiEquA1uyZInMmCAulzt9+nTmBVvp6el9+/al04zMnTjkI1F0Hdjly5fpgy1CiIGBwc6dO2XmPXLkCHMMpL+/PzXwinkdmFQqPX36NHP0o5I7ccTFxcmM+PX392fef0f613Vg5eXlzMKUlBRCyPvvv6+01TWHI9Wa8STaqbi4WCKRODg4cLncr7766ttvvw0LC6NvTlNeXl5ZWWlpacl6CcuzZ8+kUmmbNm0MDAxEIlFOTo6enp6bm5tMtcrKyvLyckKIg4ODiYlJVVVVWVmZsbGxkv3EgoICgUBgb2/PvE5InkQiKSoq0tPTo35opk+ffvjw4ejo6KCgIJlqycnJKSkp9fX1jo6Onp6enTt3prd5qVT67NkzfX19Oh1SqqurS0tLra2tZQbLlZaW3rhx4+XLl9Qt+GTmamxszM/PNzExkRmvmJubKxQKZW7XRjWv/MdsamqKj4/PysoSi8VOTk6+vr7Mi8+Uy8jISEhIqKmpsbGxcXNz69Gjh/zwxSdPnlA3duratWu3bt1qa2tLSkqsrKzoQYBFRUU1NTVubm4yg3SoBhcIBAYGBtQpnby8vIaGBk9PT+pdJBLJ8+fP6Vdp1JdO3e6EWS6VSouKijgcDrUyLFiwYM+ePb///rvMXaZY1dbWxsXFPX/+nM/nOzk5denShZn4qa+Vz+fL3FKosLCwoaHB1dWVeRlZQUFBXV2dh4eHzO3yGhsb4+Pjnz59KpFInJycOnbsyPwBbWhoKCwsNDc3l7lur6SkpLa21tnZWSZjVVRU3Lx5s7Cw0MTEpH379r1795a/mVNISMj+/ftPnDjR7LVcTFlZWbdu3ZJIJF26dOnRo0ddXV1xcTFzsy0pKamqqnJ1daVDqq2tpW6EKBAInJ2du3XrxnogLhQKi4qKhEKhmZkZNZ4wOzubdTOvrq4uLi62tbVlrkX19fUuLi56eno3b9588uSJiYnJ0KFDWTf84uLimJgY6lKTwYMHc7lc1q+PENLQ0EDdgYHacBobG3NycszMzGQWKxKJ4uLiqPsb+Pn5ybd2fn5+Y2Ojh4cHs7ypqSkvL0/5r5MmIYGpqq6urkuXLtnZ2ZmZmar/XGqPoqIiX19fkUhUUFCg/Bo10E5lZWV+fn5VVVV5eXkyKeEf4uXLl+7u7tbW1tnZ2ay3A4Z/GvSBsUtKSvrwww/PnTuXkZHx5MmTiIiIwMDAp0+fTp48WSey14ULF2bPnh0VFfX06dPU1NQjR44EBARUVlYuWbIE2UsnXLlyZcaMGZcuXcrKykpPTz9+/HhAQEBJScm8efP+gdnr2bNnaWlpISEhDQ0Ny5YtQ/aC/9dqJy+1W1JSksz1mFwud+rUqbW1ta0dmkrOnTsn09XP5/OXL1+uqF8NtE1MTIzMKR09Pb0FCxYwOxT/Oegze4MHD2be4xH+4XAKUaGqqqrY2NiioqLq6mo7O7sBAwaofnsYbVBSUhIfH19UVNTQ0ODk5BQQEKAlp61BRWVlZbdv3y4qKqqtrXV0dAwICFBlPOTf0g8//MDhcLy9vUeNGiV/+w/4x0ICAwAAnYQ+MAAA0ElIYAAAoJOQwAAAQCchgQEAgE5CAgMAAJ2EBAYAADpJxxJYaWkp9fx4bfP2T1VQB+pav9aOggWa67WguV6LdjYX0aYHGTPpdHPpWAL75ptvqEeNaJs3fkCqWjU1NQmFwtaOgoV2NpdIJGpqamrtKFhoZ3OJxeIWebpYi9Pa5lL+dMDWop3NJZFIVHleqI4lMAAAAAoSGAAA6CQkMAAA0ElIYAAAoJOQwAAAQCchgQEAgE5CAgMAAJ2k19oBAACATmoQiphPfudwiIFmHzeKBAYAAG9CLJV8dvXP4+mPqcltw4dM79RRkwEggQEAwBuqF4kqBALq/0aRWMPvjj4wAADQSUhgAACgk5DAAABAJyGBAQCATkICAwAAnYQEBgAAOgkJDAAAdBISGAAA6CQkMAAA0ElIYAAAoJOQwAAAQCchgQEAgE5CAgMAAJ2EBAYAADoJCQwAAHQSEhgAAOgkJDAAANBJSGAAAKCTkMAAAEAnIYEBAIBOUnsCq6qqkkgkzVYTi8XV1dXqDgYAAP421JjATpw44eLiYmlpaWlpuWbNGqlUylrt5MmTvvOIHEQAACAASURBVL6+fD7fwsLCzMxszpw5lZWV6osKAAD+HvTUtNy4uLipU6cuW7Zs2bJl165dmzlzprW19fLly+VrGhkZLV68uEuXLubm5gkJCcuWLSsvL4+IiFBTYAAA8PegrgS2ZcuWDh06bNq0icPhTJky5datW5s3b166dCmXK3vM995779H/d+rU6dmzZ5s3b5ZKpRwOR02xAQDA34C6TiHGxsaOHj2aTkKjR48uKirKyspqdsbCwkInJydkLwAAUE4tR2BCobC0tNTV1ZUuof4vKCjw9vZmnWXr1q11dXXJycl37949fPiwOqICAIC/E7UksPLycolEYm5uTpdYWFgQQsrKyhTNcvXq1aqqqrS0tI4dO1KVWQkEgqVLly5dupSanDRpUnh4eMsF/ubq6uq08KixsbGRw+Ho6+u3diCytLO5hEKhRCIRCoWtHYgs7Wwu0V9aOxBZ2tlcYrG4sbFRlSHZGvY2zSXi8ZiTUiJtbGxskS1IIpEYGBg0W00tCcza2prL5TKHxVdVVRFCbGxsFM1y4cIFQkh9ff306dNHjhz59OlTExMT+WqGhobbtm1bsmSJGqJ+K1Kp1NTUtLWjkMXn87UzgWlnc1EJTJXNRsO0s7mo7GVoaNjagcjSzuYSi8V8Pt/Y2Li1A5H1Ns1V29TEnOQQjoGBQYtsQRKJRCwWN1tNLX1gfD7fxsYmPz+fLqH+d3JyUj6jsbHxsmXLiouLk5KS1BEYAAD8bahrEEe/fv0iIyPpyUuXLtnb27dv377ZGXNzcwkhrIdfAAAANHUlsOXLl6empn7xxRelpaURERFhYWHLli3j8XiEkPLychsbmz179lA1P/3004iIiLS0tPT09AMHDixdurRnz55dunRRU2AAAPD3oK7rwAYPHnzgwIGVK1euXbuWulT5008//d+76unRF4QJBIK5c+eWl5cTQiwsLKZOnfrVV1/JXy4GAADApK4ERgiZPn36lClTXr58aWVlxezWs7a2Li4upid37NixY8eOyspKqVRqZWWlvngAAODvRI0JjBDC4/EcHR1VqWlpaanWSAAA4G8GZ+oAAEAnIYEBAIBOQgIDAACdhAQGAAA6CQkMAAB0EhIYAADoJPUOowcAgH8IPS5X+mqJuh8KgAQGAAAtoJuD/fnMp3F5/38b95HtPAPdXJXP8paQwAAAoGXEPH+xM+E+9b+loYG6Exj6wAAAQCchgQEAgE5CAgMAAJ2EBAYAADoJCQwAAHQSEhgAAOgkJDAAANBJSGAAAKCTkMAAAEAnIYEBAIBOQgIDAACdhAQGAAA6CQkMAAB0EhIYAADoJCQwAADQSUhgAACgk5DAAABAJyGBAQCATkICAwAAnYQEBgAAOgkJDAAAdBISGAAA6CQkMAAA0ElIYAAAoJPYE9ihQ4eWLFmSnp6u4WgAAABUxJ7ArK2tDx482LFjx0GDBh05ckQgEGg4LAAAAOXYE9iYMWOKiopOnTplaGg4bdo0R0fH0NDQlJQUDQcHAACgiMI+MENDw4kTJ0ZHR6enp8+bNy8iIqJz5849e/YMCwurr6/XZIgAAADymh/E4evru2HDhrNnz7q6uiYmJoaGhrZr1+6HH36QSCQaiA8AAIBVMwmstrb2l19+6d2798CBA/X09NavX3/z5s0xY8Z89tlnq1ev1kyIAAAA8vQUvZCUlBQWFnbs2LHa2trRo0dfvHhx5MiRXC6XEDJw4EBXV9dTp05t3LhRg6ECAAD8D3sCCw8Pnz17tr29/cKFC+fOnevh4SFTwdfXl8/nqz06AAAABdgTmKur67Fjx8aPH6+vr89aYeLEiRMnTlRnYAAAAMqwJ7ARI0ZoOA4AAIDXwj6IIzU1tWfPngUFBczCysrKvn373r17VyOBAQAAKMOewM6ePcvhcJydnZmFlpaWFhYWp0+f1khgAAAAyrAnsOfPn3fo0EG+3MfH59mzZ2oOCQAAoHnsCYzP5xcXF8uXFxYW6ukpHHkPAACgMewJrF+/fjdu3Lh37x6z8PHjx3/88Uffvn01EhgAAIAy7IdTkyZN2rRp05AhQ+bPn9+rVy8ul5uUlLRr1y5HR8dZs2ZpOEQAAAB57AnMwMAgKipq3rx5P/zwg1QqpQpHjRq1d+9eMzMzDYYHAADATmGHlpOT09mzZ1++fJmRkSGVSr29vR0cHDQZGQAAgBLNjMiws7Ozs7PTTCgAAACqU5bAEhIS0tPTi4qK6LOIhJCAgIB+/fqpPzAAAABl2BNYXV3dBx98EBUVJf/S2rVrkcAAAKDVsSewAwcOxMTE7NixY8KECY6OjhwOR8NhAQAAKMeewFJSUoYPH/7xxx9rOBoAAAAVsV/IbGtri6MuAADQZuwJbNasWYmJiampqRqOBgAAQEXspxArKysHDRrUt2/fyZMne3p6crn/y3MYhQgAANqAPYHFxsb+9ttvhJB9+/bJvIRRiAAAoA3YE9jixYsXL16s4VAAAABUx94HBgAAoOWUJbDExMTNmzcvXLjw9u3bhJDS0tKYmBihUKip2AAAABRSmMDWrFnTq1ev7777bv/+/RkZGYQQDoczZsyYa9euaTA8AAAAduwJ7OrVq+vWrdu6dWtlZWWPHj2oQhsbm8DAwJiYGA2GBwAAwI59EMe5c+cCAwOXLl1KCGFe0dyuXbucnBwNhQYAAKAY+xFYdXV1mzZt5MsbGhrEYrGaQwIAAGgeewJr165dfHy8zHiNhoaGq1ev+vn5aSQwAAAAZdgT2LRp0woKCqZOnfr8+XPqFGJGRsbEiROLi4tnzpyp0QABAADYsPeBubm5nTp1asqUKadOneJyuXfu3GlqajIyMjp8+LCnp6eGQwQAAJCn8InMY8aMyc7OPnXqVFpamlgs9vb2/vDDD52dnTUZHAAAgCIKExghxMbGZv78+RoLBQAAQHW4lRQAAOgk9gS2Y8cOjgLr1q1TfekikSg1NTUjI0MqlSqv+fLly+Tk5JKSkteIHQAA/sHYTyEOGDBgw4YN9KRYLH727NmJEyeGDBkydOhQFRd948aNyZMnl5aWisViPz+/iIgILy8v+WoXL15ctWoV/fDM/v37h4eHd+jQ4TU/CAAA/LOwJ7AePXrQd5CirVy5cuDAgaampqost7y8/IMPPggMDDx69GhdXd2IESMmTJiQlJTEfDYm5enTp2PGjDl06FD79u3T09OnT58+duzY9PR0+ZoAAAC010gS3t7eQ4cO3bt3ryqVjxw5UllZuXv3bkNDQxsbm61btz58+PDGjRvyNRcvXrx+/fpu3bqZmZn16dPnu+++y8jIyMrKUj0wAAD4B3q9oxwul/vy5UtVaiYmJnbu3NnBwYGaHDBggJGRUUJCQrMz1tbWEkLMzMxeKzAAAPinUTWBVVVVHT169LfffuvcubMq9QsKCpycnOhJPT09Ozu7wsJC5XM1NDT8+OOPw4cPZ87LJJVKy8rKsv+CQR8AAP9Y7H1gO3bsWLJkiXx57969FyxYoMpyq6ur3dzcmCVmZmYVFRVKZpFIJKGhoYWFhefOnVNUp6mpadeuXYcPH6Ymg4ODmYNNWlFdXR3ztv1aorGxkcPh6Ovrt3YgsrSzuYRCoUQi0cJHtmpnc4n+0tqByNLO5hKLxY2NjRKJpLUDkfU2zSXi8V4tkB1t3tTU1NTU9AZLlkgkBgYGzVZTaRQiIcTCwsLX1zcgIEDFj2pnZ1dZWcksKS8vd3R0VFRfKpUuXLjw7NmzMTExSu5WZWBg8OWXX7Im19YllUpVHN6iSXw+XzsTmHY2F5XAVNlsNEw7m4vKXoaGhq0diCztbC6xWMzn842NjVs7EFlv01y1sslJNjvo6+u/2e+PRCJR5cknrzEK8bW0adPm6tWr9GRdXV1paamiE4OEkGXLlh05cuTSpUu9evV6m/cFAIB/CHUNVR88eHBWVhZ9ddfFixeFQuGQIUOoydra2rq6Orry6tWr9+3bd+7cuQEDBqgpHgAA+JthPwKLjIw8cOCA8jnt7e1/+uknRa9OnDhx3bp1//rXv3bs2FFbW7to0aJx48b5+/tTrwYFBRkbG1OHaDt37tywYcPo0aPv3r179+5deva2bdu+yQcCAIB/BvYEVlVVlZKS8vjxYwMDA1dXVy6Xm5OTIxAIvLy8zM3NqToCgUDJcvl8/uXLl5cuXTpu3DgDA4MJEyZs2rSJftXf358+dS4QCHr06FFcXPzrr7/SFQYNGoQEBgAASrAnsODg4O+//37ZsmVfffWVhYUFIaS2tnbLli1Hjx69dOmSra2tKot2cXE5ffo060v79u2j/1+5cuXKlStfP3IAAPhHY+8D27dvn6mp6datW6nsRQgxNTX9+uuvvby8tm/frsHwAAAA2LEnsLS0tHbt2smXt2vXLi0tTc0hAQAANI89gTk6Ol65ckXmPhdVVVWRkZF4KDMAAGgD9j6wOXPm7Nq1q2/fvh9//HGnTp24XO6jR4927dpVWFio4p04AAAA1Io9gXl4eFy/fn3evHnLly+nC7t27RoTE+Pn56ep2AAAABRiT2CEkK5du8bHxxcXF2dlZYnF4vbt2+PkIQAAaA+FCYzi4OBAPxIFAABAeyi8lVR9ff0333wTEBDg7Ox8/PhxQkhmZmZoaGh5ebkGwwMAAGDHnsAaGxuHDRu2ceNGZ2dnLpfb2NhICPHw8Dh79uyFCxc0GyEAAAAL9gR29OjRlJSUu3fvnjhxwsPDgyrk8/kDBw68c+eO5qIDAABQgD2BxcfHjxw5slOnToQQ5gPA2rRp0+xTlQEAADSAPYFxOBypVPbZmoSQwsJCExMTNYcEAADQPPYE1qtXr5iYmIKCAmbh8+fPL1682L9/f40EBgAAoAx7ApsyZYqTk1O/fv127txZWVmZnp6+ZcuWPn36ODo6Tp8+XcMhAgAAyGO/DszIyCgmJmbOnDmLFy8mhFAPVh44cODBgwdxChEAALQBewIrLS0Vi8WRkZG5ublpaWkikcjHx8fLy0vDwQEAACjCnsA+/fTT58+fX7161dXV1dXVVcMxAQAANIu9D8ze3p45eh4AAEDbKBzEkZiYiGdXAgCA1mI/hcjhcIYNG9a3b98ZM2b4+PgYGBjQL/Xq1atbt26aCg8AAIAdewK7evXqb7/9RgjZtWuXzEtr165FAgMAgFbHnsBCQ0OnTZvG+pKRkZE64wEAAFDJK31gH330UWxsLCEkJycnLS3Nio2hoWErhQoAAPA/rySw2NjY4uJiQkhkZOSGDRtaKSQAAIDmvZLAnJ2dY2NjWW/jCwAAoFVe6QObPXv23Llzd+/ezeVyhUKhtbW1/Axr1qxZvny5psIDAABg90oCmzNnjq+v761bt65cufLs2bMJEybIz4AhiAAAoA1kRyEOHDhw4MCB1tbWV69eRTcYAABoLfY7ccydO/fEiRMaDgUAAEB17AkMAABAyyGBAQCATkICAwAAnYQEBgAAOgkJDAAAdJKyBHbnzp3169fPmjXr+vXrhJCCgoJff/21qalJQ6EBAAAoxn43ekLIqlWrNm7caGlp2djYGBAQQAgxNDScPn36mTNn3nnnHQ1GCAAAwIL9CCwmJmbjxo07d+4sLy/v3r07VWhtbR0YGBgTE6PB8AAAQCeZ6etXChqZfy1+m132I7Dz588PGTJk0aJFhBAOh0OXt23bNjc3t6VjAACAv5uuDvaR2c9+z8ikJsd6t5/k66PHbcmBF+wJrKamxtnZWb68vr5eLBa34NsDAMDfVerLl78+zqD+b2dlOcnXp2WXz54M27dvf/v2bZnxGg0NDVevXu3YsWPLRgAAAPAG2BPYtGnTioqKJk+enJWVxeFwpFJpWlraBx988PLly3//+98aDhEAAEAe+ylEV1fX06dPT5482cvLixBy+/ZtkUhkYmJy7Ngxd3d3zUYIAADAQuEw+lGjRmVnZ58+fTo1NVUsFnt7e0+aNMnR0VGTwQEAACiiMIERQqysrObMmaOxUAAAAFTH3gcWFhY2adKk8+fPi0QiDQcEAACgCvYE5unpefv27ffee8/T0/Pzzz/PyMjQcFgAAADKsSew4cOHv3jx4ubNm6NHj965c2eHDh169uy5ffv20tJSDccHAADASuFF0Vwud+DAgT///HN+fn54eLixsfGyZctcXV2PHDmiyfgAAABYNX9XD3Nz81mzZp04cWLu3LkCgeDFixcaCAsAAEA5ZaMQCSECgeDMmTOHDh2KioricrnvvvvuyJEjNRMZAACAEgoTWGJi4qFDh44dO1ZaWurn57du3bqZM2c6ODhoMjgAAABF2BPYnj17FixYYGNjM3ny5JkzZ9JPVAEAANAS7AmsQ4cOERERY8aM0dfX13BAAAAAqmBPYEOGDNFwHAAAAK/llQR26dIloVD47rvvZmdnJycns87QsWNHb29vjcQGAACg0CsJbNasWdXV1bW1tRcuXFiyZAnrDGvXrv3Pf/6jkdgAAAAUeiWB3bp1SyKREEKmT5+uaLi8ra2tJuICAABQ6pUE1rZtW+ofS0tLS0vL1ogHAABAJQrvRv/RRx/Jl7/33ntbt25Vc0gAAADNY09gAoGgtrZWvryqqqqpqUnNIQEAADSv+Xsh0iorKzMzM/FQZgAA0Aay14G1adOmoaGhsbFRKBRaW1szX6qurjYwMBgxYoQGwwMAAGAnm8BmzZolFAoTExOfPXs2YcIE5kv29vbjxo1zdnbWYHgAAADsZBPYd999RwiJjIxMTk5etWpVa4QEAADQPPZbSY0aNWrUqFEaDgUAAEB1yp4HlpmZmZycnJOTIxQK6cKAgIB+/fqpPzAAAABlFCawxYsX//TTT1KpVKZ87dq1SGAAANDq2IfRR0ZG7ty584cffrh37x4hpLa2Nj4+fuTIkePHj0fHGAAAaAP2BBYXF9e/f/9ly5ZZWFgQQoyMjPr06XP27NnHjx8fOHBAsxECAACwYE9ghYWFXl5ehBBTU1NCSFVVFSFEX19/2LBhV69e1WR8AAAArNgTmL29/cuXLwkhDg4O+vr6iYmJVHleXh6Hw9FcdAAAAAqwJ7B+/frFx8cLBAIulztx4sS5c+f+8MMPixYtOnPmzNChQzUcIgAAgDz2UYhBQUHr16+vrKx0dHTcuHHj1KlTV69ezefzFy5cOHPmTM1GCAAAwII9gRkZGc2dO5f6v02bNteuXWtqauLxeDweT4OxAQAAKKTsQmYmfX39N1h6bm5uYmKiqanpoEGDDAwMlNQUi8V5eXmEEHd39zd4IwAA+Kd5JYEdOnSorq5O+Qx9+vTp3r27Kovetm3bypUr27Zt+/LlS0tLy8uXL1MjG2UUFhZOmjQpKSmprq7O2dk5Pz9f9egBAOAf65UEtmrVqsLCQuUzrF27VpUElpiYuHz58o0bN65cubKmpiYgIGDatGnx8fGslXv06DF37tzIyMgbN26oHjoAAPyTvZLAMjMzJRKJ8hmUnwmkhYeH29nZffLJJ4QQMzOzL774Yvz48cnJyV26dJGp6eTktG3bNkJIcnLyawQOAAD/bK8kMBMTk5ZabmpqakBAAJf7/8P0AwMDCSEpKSnyCQwAAOANKBvE8fz58+vXr2dnZ7///vs9evSorKx8+vRp165dVRmLWFBQ4O/vT09aW1vr6+s3e36yWRKJ5MmTJzExMdSko6Njp06d3nKZAACgixQmsO3bt69YsUIsFvN4vLZt2/bo0aOhoaFv374xMTGDBw9udrkNDQ0yJxsNDAyaHSHSLJFIdO3atUePHlGTgwcP9vDweMtltoi6ujotvEdJY2Mjh8N5sxGkaqWdzSUUCiUSCfPhQVpCO5tL9JfWDkSWdjaXWCxubGxsto9G896muUSyBzOyTy+RIWxqEqi2wkgkElW6q9gT2O3bt5ctW7Z69erVq1ePHDmSKnRycgoICLh8+bIqCczBwaGiooKebGpqqqmpcXR0VCV0JfT19RcuXLhkyZK3XE6Lk0ql1H0jtQqfz9fOBKadzUUlMBV7eTVJO5uLyl6GhoatHYgs7WwusVjM5/ONjY1bOxBZb9NctU1NrxY0kwj5+vpGqq0wEolELBY3W409gUVERPTp02fdunWEEGZy9vLyevHihSpv7+Li8uTJE3qS+t/V1VWVeQEAAJrFfi/EsrKy9u3by5eLxWKBQKDKct955507d+5kZ2dTk8ePHzcxMRk0aBA1mZqaqmIiBAAAYMWewNzd3RMTE2VO1wqFwj///NPHx0eV5c6aNcvLy2vMmDGHDx9eu3btxo0b16xZY25uTr06YsSIL7/8kq68bNmy0NDQ6OjoysrK0NDQ0NDQ1NTUN/1EAADwj8CewCZPnpyVlbVo0aLq6mrqFGJZWVlISEh2dva0adNUWa6RkdH169cHDx68ZcuWqKiovXv3fvbZZ/Sr48eP79+/Pz1ZWVlZUVHh4+MTHBxcUVFRUVGhhT3DAACgVdj7wLy9vcPDw+fMmbNv3z4ej5eenh4SEsLlcnfv3u3r66vioh0cHPbs2cP60s6dO5mTeMozAAC8LoXD6KdNmzZgwIDDhw+npqaKRCIfH5/p06f7+flpMjgAANAeNU1NZto0qlnZhcxt27b96quvmCXp6enFxcVDhgxRc1QAAKB1JFLp+N/O/paRSU0+mjurdeNh7wMTi8Uyl3M+evRo8uTJ/v7+cXFxGgkMAABAGdkElpqaOmjQIFNTUyMjo1GjRpWUlNTW1i5fvrxz585nzpxZtGhRSEhIqwQKAADA9MopRJFI9N577+Xm5gYFBfF4vOjo6JCQkNLS0sTExPnz569evdrJyam1AgUAAGB6JYElJCQ8e/bs9OnT48ePJ4RER0ePGDHC0tIyPj5exYdYAgAAaMYrCezFixf6+vrjxo2jJocPH25razt58mRkLwAA0Dav9IFVVVWZm5vTD/EihFhaWrZp00bjUQEAADSDfRQiAACAlpNNYKWlpRyGrKyszz77jFlC3aIeAACgdb3SB+br6zt37lzlM3Tr1k2d8QAAAKjklQQ2aNAg+oknAAAA2gx9YAAAoJOQwAAAQCchgQEAgE5CAgMAAJ2EBAYAADoJCQwAAHQSEhgAAOgkJDAAANBJSGAAAKCTkMAAAEAnIYEBAIBOQgIDAACdhAQGAAA6CQkMAAB0EhIYAADoJCQwAADQSUhgAACgk5DAAABAJyGBAQCATkICAwAAnYQEBgAAOgkJDAAAdBISGAAA6CQkMAAA0ElIYAAAoJOQwAAAQCchgQEAgE5CAgMAAJ2EBAYAADoJCQwAAHQSEhgAAOgkJDAAANBJSGAAAKCTkMAAAEAnIYEBAIBOQgIDAACdhAQGAAA6CQkMAAB0EhJYy+DxeK0dAgDAP4teawegq+qEwpu5edT/PA430M2F+apAJL6ek0NPjvD04HI4Go0PAODvDgnsDQklklEnI6j/TfX5ZUsXMV810OONPhkh/WuyYeVSQz00NQBAS8IpRAAA0ElIYAAAoJOQwAAAQCchgQEAgE5CAgMAAJ2EoXEtwIDHqxMKqxqb6BJbY6NWjAcA4J8ACawF6HG5xny+4aYf6RLJ6hWtGA8AwD8BTiECAIBOQgIDAACdhAQGAAA6CQkMAAB0EhIYAADoJIxCBAAAdjVNTcxJiVSqqGaznExN6oUi5mM5jPT09LhvdRCFBAYAAOzEEumSmKu3/np01LUpH77xonysrU8+erzh9h1qcmonv1X9+iCBAQCAuhTW1mZXVlH/i9/iCIwQUilopBdV1iB428jQBwYAADoKCQwAAHQSEhgAAOgkJDAAANBJGMShUG2TUEr+12NpwOPp83itGA8AwN+GoZ6eQCQSSSR0iTGfz2OOslcBEphCdULhl3/eov53MDFZ0bcXEhgAQIvo7eSYXlp2KCWNmuzbxnlCB28zff3XWogaE1hdXd3Zs2fz8vI6dOgwevRoPT2F7yUUCi9cuPDkyRN3d/f333/fyEgrHqZV1dgY9uAh9b+PjfWKvr1aNx4AgL+Tx2Xl9G9sg0g0oYP36y5BXX1gRUVF3bp1+/TTT2/evDl9+vThw4cLBOyj/uvq6gYPHjx79uxbt24tX768Z8+epaWlaooKAAD+NtSVwFasWCEQCO7fv3/+/PmbN2/evXt3+/btrDU3btyYmpoaGxt7/vz5hISEioqKzz//XE1RAQDA34ZaElhDQ0NERERISIi9vT0hxN/ff8yYMYcPH2atfOTIkbFjx3bo0IEQ4uzsPGPGjBMnTgiFQnUEBgAAfxtqSWAvXrwQCAT9+/enS/r37//kyRMJY8AJpbGx8fnz5zI1a2pqCgoK1BEYAAD8bahlEAeVfmxtbekSW1tboVBYWlpKHZPRioqKpFKpTE1qCe7u7vJLFovFsbGxBgYG1GT79u2HDBmijo/A4XAsDAzmdu1MTTqYmOhxuMwErM/l0a+aGxjwOBx6kjK3WxfpX/cN0+Ny5ZO3BnA4HA6Ho+StORyOlHFzsxacVE5PT69VGqRZMs3VWu0jg8/na2dzcV9dsVurfWRoT3MxP5RUKuXxeC3VXC1I+cYoJdLR7dp6WlhQkwY83nBPD9u/xtmZ8PmDXV30/7ohr6WhQf82zo0iETXpYGLcy8mJ/mF0MTflcAg92dbKslEkoif7tnGWSqVisZialEgkqnxetSQw6gQgjzHonBqC2PTqnflfqyZFIpHk5uYmJCTQJQMHDmyxuBk4HI6pHm/zkAC6hM8hzAblEinz1YamJpnJTYGD6EmxWMxh3HRZrWvqay1N/iVmicyryidZSxThcrmt9Uv3WgtvrfZ5m5g1tv5IpVL5V9E+TDIvKV+7Wqt9lG+MJnp6s/w70pM8Lneyr89kXx9qUo/H+8C7/Qfe7elXg9t5jm7r8f+vcrkOxsZD3VzoV93MzPo6OdKTUilh/mwacDh055FEIuGpcNmSWhKYk5MTIaS8vJwuKS0t5XK5CQcBWwAAIABJREFUjo6OqtQkhLRp04Z1yXw+/6OPPlqyZEmLxyzPQOmrPB7PkM+nJ2tqaszNzNQd0usSiUQSiUT/NS+t0ID6+nr6MFp7SCQSiUTCZ3ytWkI7m0sqlYpEIjSXisRisUgk0rmNkUeIvuIroAghRD0rgEQioY/GlFBLH5ibmxuXy01JSaFLUlNTXVxc5C8FMzExsbW1lanJ5/OdnZ3VERgAAPxtqCWBWVpaDh48+NChQ9TxYHl5+ZkzZ8aOHUu9WlxcfPr0afpir/fffz8iIqKqqooQ0tjYePTo0REjRhgbG6sjMAAA+NtQ13VgmzdvTk9PDw4OXr9+/dChQ3k83urVq6mXkpKSJk6cmJb2/3cQ+fLLL5uamoKCgjZs2DBy5Mjs7Oz169crWmxcXNzt27fVFPMbq6ur8/X1be0oWHz//fc7duxo7ShYuLu7q6lH+m3s2LFj3bp1rR0Fi44dO1ZXV7d2FLIOHDhAb9RapU+fPlo4jPnXX39dvHhxa0fBYtiwYRkZGa0dhazIyMgZM2Y0W01dCaxHjx737t3z8vK6c+fOO++8c+/ePboDrH379l988YWHhwc16ebmlpCQMHjw4Pj4eH9//4SEBH9/f0WLFYlEWniJmFQqraysbO0oWDQ2NjY0NLR2FCy0s7kEAoGi+8W0rqqqKi3M91rbXNXV1VoyCpGpsbGxvr6+taNgUVNTo0pvk4Y1NTXV1dU1W02N90L08/PbtWuXfHn79u2//fZbZomrq+uWLVvUFwkAAPz94HlgAACgk3TscSqOjo737t0bPnx4awfyCrFYzOfztS0qQkhubi6Px/vzzz9bOxBZpqamI0aMaO0oZBUUFIhEoqSkpNYORBaXyx03bpwql8VoUnFxcX19vRau9gKBYMqUKdo2YL20tLSyslILm6usrGzu3Lla8gwQWkVFRZcuXZqtpq6ru9VEKBTeuHGjtaMAAAD16tOnj1lzF9fqWAIDAACgoA8MAAB0EhIYAADoJCQwAADQSUhgAACgk7Q3gdXU1Kh4nX99fb3GLnGXSqUVFRVaeOG6QCCoqalRpWZdXZ3orwf2qBvVXKrcFqGpqUmTd0uqra3VzrtIVFZWauzbUd1rbWK1tbUau/9LZWWlFt6ap6Ghoba2VpWaIpFIxZotoqKiQsW1Sztvz1FXVyezamljAktMTOzatau5ubmpqenEiRMrKioU1Xz27FlgYKCJiYmpqemwYcNycnLUGlh4eLijo6O1tbW1tbXMzUSYMjIyPvnkk8DAwHbt2i1atEitIRFCKisrJ02aZGpqam5u3rlzZ+bD0pjy8vJmzZrl6OhoampqbGwcEBCgqGZL2bNnj729vbW1tY2NzcaNGxVV+/nnn9u2bWtgYGBhYWFhYbF48WK17o4kJyf36NHDzMzM1NR03LhxZWVlyutLpdKJEye2a9fu999/V19UhJBTp065uLhYWVlZWFh8+umnirJ+ZGRku1cdO3ZMfVG91ib266+/+vj4mJmZGRsbe3t7Z2VlqS+ws2fPuru7U821ZMkSRb/LCxcubCcnPj5eTVEVFxcHBwebmpqamZn17ds3PT1dUc2UlJTAwEAjIyMzMzMnJ6etW7eqKSTK8ePHnZ2dra2tLS0tV69erWjtkkql27Ztc3R0NDc3t7S0XLlypfrSWG5u7qpVq4KCgtq1a/evf/1LeeVHjx717dvX1NTU1NQ0ODi4qKjofxFrlfLycjs7u3feeefZs2fx8fFOTk7vv/8+a02hUNixY8cuXbqkp6enpaV16tSpa9euIpFITYFFRUVxOJyvvvqqsLAwPDycy+Xu2bOHteaJEyf69u27cOFCd3f3CRMmqCke2gcffODg4HD79u1nz56NGjXK1ta2rKxMvtqVK1cGDRr03//+9/79+zExMb169bKxsSkoKFBTVOfOnSOEbNiwobCwcM+ePRwO5+DBg6w1jx8/Hh4eHh8fn5qaunfvXiMjo7lz56opqurqaicnpyFDhjx9+vTevXsuLi4jR45UPsu+ffuo+3Yqir9F3LlzR09Pb+nSpQUFBadOneLz+evWrWOteerUKULIyZMnT/0lKytLTVG91iZ29OhRLpe7ePHi2NjYhISEn3766enTp2oKLDk5WV9ff/78+QUFBWfOnDE0NFyzZg1rzbi4uFMMQ4YMsbS0rK+vV1NgAQEBnp6eSUlJmZmZAwYMcHd3Z32vxsZGV1fXPn36JCYm5uXlff/994SQ48ePqymq2NhYHo+3cuXKwsLCY8eO6enpbd68mbXm3r17eTze7t27c3JyIiIiLCwsvvrqKzVFFR0d3bNnz9DQUD8/v4CAACU16+vrPTw8+vXrl5mZ+eDBg7Zt2w4aNIh6SesS2JYtW/T09EpKSqjJo0ePUulXvia1R3zv3j1qkrpL/cWLF9UUWFBQUJ8+fejJadOmeXp6Kp+lf//+6k5g1G2k6d/WsrIyPp/PunaKxWLmJLV3rL4f5QEDBgQGBtKT48eP9/PzU2XG+fPnu7i4qCmqn376icvl5uXlUZOnT58mhCQlJSmqn5+fb2tre+HCBXUnsEmTJnl7e1OPUZdKpUuWLLGxsREKhfI1qQQm822qCbWJJSQkUJPUJvbHH3/I16yvr7exsZk/f74GopJKpTNnznRzc6NT6erVq83MzBoaGpTP1djYaG9vv2DBAjVFFRsbSwi5cOECNZmdnU0I2b9/v3zN5ORkmZb08fEJCQlRU2Djxo3r2LEjvXbNnz/fwcGBdUfE19f3ww8/pCe/+eYbVRr2LY0dO1Z5Ajtw4AAhJDMzk5qMjIwkhNy6dUsqlWrdKcS4uLhevXrZ2dlRk6NHjyaEUGuGfE1HR8cePXpQk3369LGzs2Ot2VKBUcFQgoODnz17lp+fr6a3UxH1m0IHZm1t3a9fP9ZG4HJf+a7V+shasVh8586d4OBguiQ4ODg9PV3J2WBaQUGBoudxv724uLguXbrQyx85ciSPx1OyzixYsGDGjBk9e/ZUUzzMwEaNGsXhcKjJ4ODgsrKyR48eKaqfnZ394MEDdfcaUptY9+7dqck+ffrY2tqyNld0dHRZWdnChQuLioqSkpI0EBj13VGTwcHBNTU1Dx8+VD7X+fPnS0pKZs2apaaobt++bWBgEBQURE16enr6+fmxNpe9vb2enh59HqyxsbGiokKtq/3o0aOZa1dxcTHrCd7s7OxOnTrRk/7+/jU1NQ8ePFBTYCqKi4vz9vZu3749NTl06FAjIyOqYbXuXogFBQVubm70pKWlpZmZGevTfQoKClxcXOhvhcPhtGnTRk3PAaqqqqqvr3dxcaFLXF1diZp/bVWRn59vaGhoa2tLl7i4uGRmZjY7486dO83NzdV0Z7aSkhKRSMTaXFZWVvL1hULh1q1bBQJBfHz806dPT548qY6oqACoSCgmJibW1taFhYWslQ8fPvzgwYMjR46o8liHtyGRSIqLi1mbS9Gjhby8vAghHA7n/fffDw8Pt7a2Vkdg+fn5Km5iKSkp+vr6+/fv3759O5/Pb2pqCgkJ2bNnj5ru30ht+/Qk9X+z2354eLi/vz+9y6uOqOzs7Ji7hq6urqxROTo6btmy5fPPP3/+/Lmdnd2vv/7q5eW1dOlSdUQlFApfvnzJunb5+PjIVHZycmJ2c7548YIQ0uq76TKbrb6+voODA7XZat0RWHl5ucz9rywsLFh72llr0g96bllUAObm5nQJ9X+zQwDUrby8nBkVUa0Rzp8/v2XLli1btjg5OakjqtdtLrFYHBMTExMTc/v2bXd3dxMTE3VERV5nnXn58uUnn3wSFhZmamqqpmBo1dXVQqFQxeby9/e/du1adXV1RUXFwYMHY2JiVHnu35tRvblqamqampqioqKePn1aV1d37Nix8PDwn3/+WR1RNTY21tbWyjeX8tU+Pz8/Kipqzpw56giJ8lq/SD169HB2dt6/f39YWFhycnJQUFCz9/17M9QwYBXXrkmTJh09epS6/ffjx49//PFHQkhJSYk6AlOdkobVugRmbW0tc/6hqqqKeYTxBjXfno2NDSGE+XZVVVWEEDW9nepYG4E+AcvqypUrkyZNWr16tfo25tdtLkNDw+jo6Js3b+bm5kql0uDgYDUNJVd9nVmwYMGoUaM0c8t8c3NzPp+vYnN16NAhMDDQzMzM0tJy2rRpq1ev/uOP/2vvzMOiqt4HfmaThnVgHJYhBIaSRQWRfAQfxCUBl4o0ITEsRM1celwCRVwoRYTKLS0NkFUMHiKjYokMBBK0MAlHFgFJGMFlGJZZHNb7++P8vue5zQwjWtfGPJ+/7j3nzL3vPXPvfe85510KKDLF5nK5Y+wueMtFR0fb2trS6fQ333zT19c3KyuLCqn09PQMDQ3JgsFt7Q9jamoqg8F4qLXb32Hsd9fNmzd9fX0XL17c1tZWW1t7+fLlEydO7Nq1iyKp6HT6GO+uvXv3vv7667NnzzYyMnJ1dX3jjTcAAE/gG047WjpW5xQYn88nj1h7e3ulUqnGgYKVlRV5eE4QxO3btykaUpiYmOjr65MFg9sUnW7s8Pl8pVJJ/pjS3gkVFRUBAQEbNmyIiYmhTio4xa/eXSgr92gYGRlt2rSpubm5paWFCsFU7i6FQiGRSDR2V35+/k8//QStrmfMmAEAiIiI8PT0pEIqOp1uYWHxeHfX5MmTR0ZG/vzzTyoEU3/EOjo6NEoFJ9JRmnW4LRKJqJAKCqbeXXw+f7T2BEGkpqYuWbKE0i9OKysrsVjc39+PSkQi0Wh3l1Kp3L59O5yedXR0XLp06TfffEOFVEwmk8fjjfHuMjAwyMzMFIvFpaWlnZ2d8ANutHnsJ4bK3z04OHjv3j0ov84pME9Pz19//RW9kYuKigAAM2fOVG/p5eXV2dmJsjdVV1ffv39fY8t/SjBo/QIpLCy0tbXV8sw8GeArFQnW09NTVVU1WidUVVUtXrw4LCyM6vzXDAZj+vTpBQUFqKSwsNDJyWksSzVwCp6iWURPT8+amhq06FVcXDw8PKyxu86cOXPs2LG4uLi4uLjdu3cDAIKCgij6RoaCFRUVEf9LDVFQUGBqaurk5PTQH1ZWVjIYDIoWYuEjhtbwtTxiXl5eNBqN7PZ0/fp1BwcHKqSCpysqKkLOTAUFBYaGhlresxcuXGhubl69ejVF8iCp+vv7S0tL4W5bW1tdXZ3G7oITYj09Paiku7ubuoGO+rvL3Nwc2USow+VyX3rpJS6X+8UXXzg7O48lLxeleHl5NTY2QqtOAMCFCxcUCsX/dyyl9pGPgVgs5nK5ixYtamtru3LlirW19eLFi1HtjBkzQkJC4PbAwICTk9O0adOampoaGxvd3NwmT55MnR9YYWEhjUaLiYkRi8VpaWkMBuP48eOwqq2tjcPhpKSkwN3e3l7odOLo6Ojp6Qm3BwYGKBLstddes7Kyqq6ubm9vf/XVV83MzO7fvw+roqOjuVwu3K6rqzM2NnZxcSG7ENXW1lIkFfyc/PTTT8VicWJiIp1OP336NKxqbGzkcDjZ2dlwd/369efOnRMKhbW1tdC0ZOHChRRJ1dPTY2Fh4evre+vWrZqaGltb2/nz56PaWbNmBQYGqv8KWotRakYP9VBERMS9e/e++eYbPT29ffv2waq+vj4Oh3PkyBG4GxUVlZWVVVtbe/Xq1f3797NYrNDQUIqkUn/EpkyZgh6xN99809vbGzUOCQmZMGFCaWlpR0fHwYMHAQB5eXkUCXb16lUWi/X+++/fvXs3Pz+fzWZHRkbCqqGhIQ6Ho+JFFxIS8vzzz1P3coCMjIzMnDnTwcFBKBS2trb6+Pg8//zzMpkM1m7atGnixIlwu7W1VU9Pz8/PTygUdnZ2Qu+rnTt3UiRYeXk5nU6Pioq6f/9+Tk7OuHHjDh48CKskEgmHwzlx4gTc/f3339PT04VCYUVFRWhoKIvF+umnnyiSqr+/H76Fpk+f7uzsDLelUimsdXJy2rhxI9yWy+U2Njbe3t6tra1CofDFF1/08vKCXgE6p8AIgrh8+fKkSZMAAHQ6/fXXXye75c6aNSssLAztQm9BqJZ9fHxaW1spFezLL7+EqzuGhoZ79uxBfhXt7e2WlpYZGRlwt7Gx0VSN3t5eiqSSSCQoXa+Li8ulS5dQ1f79+62treF2Xl6eulQfffQRRVIRBHHs2DE45DI2No6JiUHlTU1NlpaWX3/9NdxduXKliYkJ/BN5PN6OHTuo6yuCIK5cueLq6grvrldeeQV5HBIEMX/+/BUrVqj/5N69e6ampllZWdRJRRDE2bNn4RQrm83eunUrettKpVJLS8vPPvsM7m7cuBEtyFtaWoaHh1PqpkN+xGbPnk1+xEJCQl5++WW0K5VKV65cCfMg8/l8JDBF5ObmwnHnc889t2HDBvSBODQ0ZGlpGR8fTxaMz+eT70Dq6Ojo8PPzgxODHh4e165dQ1Vbt26dMmUK2i0pKZk2bRp0buFwOFFRUf39/dQJlp6ebmFhAQDQ19cPDw9HfoQSicTS0vLUqVNwt6KiAs3zu7q6FhcXUydSV1eX+hsJOb+7ublt2bIFNb527Ro0H6XRaH5+frdv34blupvQsquri81m6+vrP7Rlb28vjUZTMcajiOHhYfjNwmKxnsDpxo5CoXjw4AHUr7rD2Luru7ubxWI9seViiUSip6dHnbnj4zEyMtLV1WViYgLVgBa6u7uZTCZFdmvqjP0RUyqVDx480Ogs8Y9DEIRYLDY2NqbUqfExkMlkg4ODY+mE/v5+hULxZLrrke4uBoPxZN6oj4T6W0J3FRgGg8FgMFrQOSMODAaDwWDGAlZgGAwGg3kqwQoMg8FgME8lWIFhMBgM5qkEKzAMBoPBPJVgBYbBYDCYpxKswDCYf5I7d+7Ex8fDPBQAgMzMzIyMDC3tExMTc3Nzn4hoqhAEkZ+ff/jw4fj4eKlUCgCQy+WZmZnx8fGJiYkAgE8//fTnn39+6HEuX768b98+6nLPYzCjgRUY5qlBIBDQaDQajUan0/l8/pw5c1Tin0ZERNBoNIFAMDAwgArb29tpNNrhw4dVjnbgwAEajUZOUvqPIBKJIiMjUbbA9PR0mE8WANDW1hYZGakSp/jkyZMwyfKTJzQ09I033sjLyzt//jx0QHZ3d9+yZUtxcXFlZSUA4ODBgz/++ONDj1NVVRUdHU1RAgEMRgs6l9ASg9HC5MmT9+7dCwC4ffv2F198sWzZsry8vFdffZXcprW1NTk5+b333tNyHIIgUlNTORxOcXGxSCQip/v7Z9m+fTuKOdvR0REfH+/v70+OchsTE/PEAmqQkUqlGRkZR44c2bx5MyzJy8tramq6du0aysl78uRJLSFfEQsWLOByuboWmwbzLIAVGEa3kMvlDQ0NHA5HYyxzCwuLwMBAuL1kyRJ7e/vs7GwVBbZo0aJ9+/a9/fbbWuKQlZeXNzc3nz17dtWqVenp6VFRUQ8VrKOjo7Oz08TERP2dPjw8LBQKGQyGi4uLStWcOXO0HxaFziMzMjJSX1/PYrEcHBxUkhqPjIzU1dUNDw87Ozs/NCYQAIAgiIaGBoVCYWdnR4401tHRQRCEra0tuQQAQC5ZunQpDNZH5saNG1KplMfjoczpEydOdHBwUGnZ09PT0tLy3HPPOTs7qx8EANDZ2Xn37l0HBwd1/T08PNzQ0NDf329tbQ0j+GEwmqEuViMG86gkJSWh+IQ+Pj779u0DAPT19cFae3t7cvRYgiA4HA45IHp4eDgAoKamhk6nx8XFwUKYn+XQoUPkH77zzjt8Pn9oaGjJkiUCgQDFZdaISCTy9fVFj4yHh0dTUxOqvXz5MnrpOzg4JCcnAwDOnz8Pa/38/ObOnUsQxA8//KDy6MFIqe7u7kFBQbAxnEvMzc1FOtLGxoYcEPb69esoDbyFhUV2djYAIDk5eTTJ8/PzkWwMBmPdunUwYuzWrVvJkvj7+6vkIoHBcM3MzCIiItDRCgsLyV8VEydOhBGHYd5epVIJm8lkstDQUKR3bW1tL1y4QP7Ltm3b9vbbb0OtxmQyUWR0SFJSkrm5OToLTEZx7NgxOp2uEq17/vz5rq6uWv44zH8erMAwukJlZSVM5tve3i4Wi3ft2gWV2WgK7I8//gAArFmzBpVABTY0NLRy5UoOhwPzGKgrMKlUamhouH37doIgzp07BwAoLy8fTSqFQuHs7Ozo6FhaWiqTya5cueLm5ubo6AjDn3d3d1tYWEyePLmmpqavry8hIYHD4WhUYHK5HBprZGZmtrS0tLS0KBQKQpMCs7GxyczMvHPnTllZmZWVFUr4olQqYYLNS5cuyeXy7777DoZjH02BVVZWMpnM4ODg5ubm3t7es2fP6uvrw6sWi8XFxcUAgFOnTrW0tHR0dLS3t3/44YcAgNra2paWlu7ubuKvCqyyspLFYs2aNau6ulomkzU2Nh44cECjAgsICOByuTk5Ob29vc3Nza+88oqxsbFIJIK1HA7HyMho9erVTU1Nt27dCgoKYjKZ6IMAGrwsX768vr5eJpNdvXr18OHDBEH09PQYGBjs2bMHXV1LSwudTv/8889H++MwzwJYgWF0hZCQEB6PJ5fLUQlM10lWYC+88AJMMrlly5bx48fz+fzm5mbUHikwmG8JJlhSV2DQxA7mQhsYGODxeKtWrRpNqqSkJDiqQyUwh2pBQQFBECdPngQAVFdXo9q1a9dqVGAEQVRVVQEASkpKyMdXV2BHjx5Ftfv372cwGFBZfvvttwCAwsJCVAsTk46mwPz9/V1cXAYHB1FJVFSUkZERHG7C5JPnzp1DtcePHyf3NvFXBbZw4UIej6cx0w1Zgf3666/gr7nTenp6jIyM0DCLw+FMmTIFpfOAyUW//PJLuGtvb+/u7o5qyaxZs8bKygqlTYmMjNTX14eKFvPMgtfAMLoCTF9LXriaP3/+pUuXyG1u376dkJAwNDR0584dGo2Wn5+vcanMzs5u7dq1R48e3bBhg3ptSkrKtGnT4KQZi8Vavnx5cnLysWPHNBpTlJWVGRkZ3bhx48aNG7CEIAgmk1lfX79w4UKhUGhubg4zFUEWLlwIFeRj4+Pjg7YdHByGh4c7OzsnTJhw/fp1Op0+b948VPvyyy+PdhCCIMrKynx8fOAQEzIwMCCVSkUikY2NzaNKVVFRsWTJkoem2Lhw4QIA4MGDBzk5OaiQx+PV19ejXW9vb7QqZmlpaWBgABPGt7e3t7a2xsbGalwz27hxY1JSUkFBQUBAwODgYFpaWnBwMBzvYp5ZsALD6ApdXV3I/g2inidp5syZ58+fBwCIxeLAwMBly5bV1dVZWVmpH23v3r1paWmxsbE7d+4klzc2NlZWVs6dOzc+Ph6WyGQyuVyek5MTFhamUaqhoSHUGOLm5gYNKO7evTt+/HhyFXn95vEg61EmkwkAgBbqEomEzWaTDTdgvlCNKBQKpVIpFApVJPfw8JDJZI8q0tDQkEwmG4s9hUQiAQCoqHBTU1OUsxQAoKIFWSwWvMDe3l4AwGhnmTp16owZMxISEgICAr799tvOzs5169Y96oVg/mNgBYbRFWxtbdvb28klyB1YnfHjx585c8bR0XHbtm1fffWVegMej7d58+b4+HhktQhJSUlhMBi3bt1KSEhAhQYGBsnJyRoVGJfLHTdu3G+//aZuKwgAsLa2LikpIZfAOTEqsLW1lcvlEokE6S0t/aOvr89msxcsWHD69Om/f2omk2lqaioSiR7aEho65uTk2NvbP+pZ4KeAlrOsX78+LCzs1q1biYmJbm5u06dPf9RTYP5jYEdmjK7g5eVVUVHR1NQEd2UyGbSyGw1ra+uNGzdmZ2fX1NRobBAeHm5sbBwdHY1KhoaGMjIy/Pz8Wv7K3r17L1682NDQoH4QPz+/3t5eFY9phLu7u0QigfNmEC1hNeB812MMgCBeXl4AALJC0qKcaDSar6/v999/39XV9XinU8HX1zc/P//evXvam0GvgNTU1Mc4haWlpaur65kzZwYHBzU2CAoK4nA4UVFRP//8s3Y/P8wzAlZgGF3hgw8+MDc3nzNnzieffHLixAlvb28ejwcA0Dj0gURERBgaGkJre3VMTEx27NhRUVGBSoqKijo6Ot566y2VlsHBwXQ6PT09Xf0gy5cv9/b2DgsLO3To0O+///7HH3/k5OQsW7bs+vXrAICgoCAHB4eVK1d+9913QqFwz549cIZTI7a2tlwu95NPPsnIyMjJybl7967W/lDlpZdeCg4O3rlz5+bNm0+fPr1ixYorV66A0fsnNjZWqVTOmzcvNze3oaHh4sWLx48fDwoKeqSTIj766CMAwJw5c86dO1dfX19SUrJhwwb18FGurq5r166NjY0NDw+/dOlSXV3dDz/88O67744xXNbHH3988+ZNX1/f4uLi+vr6goKCHTt2oFo2mx0aGgrNKVesWPF4F4L5L4EVGEZX4PF4v/zyy+zZsxMTEzMyMt57773g4GADAwPkGWZjY6Oy3DV+/Pjw8HBo+Q0AMDMzEwgE5AabNm3y8PAQCATIut3JySkgIEDl1DY2NkuXLq2oqCAIQqWKyWQWFRWtWrXqwIEDHh4eU6dOXbduHYvFgsqVzWYXFRVNmDAhICBgypQp5eXlqampAoGAzWaji4ItYeOcnBw2mx0TExMZGQnHmhYWFsi/2NDQUCAQkENaqJSkpKTs3r27tLT00KFDZmZmcAQ22qLRpEmTLl68aGVlFRwc7Ozs7O3tffz4ceTvNW7cOIFAgPoWAGBiYiIQCMgGFHw+H1lJODk5lZWV8Xi8ZcuWubi4+Pv737x5E+pOIyMja2trpEdPnjx54MCB7OxsLy+vSZMmBQUFiUQi5NlmZ2ensnRnZ2eHFjv9/f3z8/N7enqgCWVgYGBfXx+58erVqwEAwcHBDzUnwTwL0NSfWAxGR3jttdf+/PPP2traf1sQAAADEZY8AAABSElEQVQYGRm5f/8+m83W+Ors6upiMplkU4UnQFpaWmhoaENDA/Ju1ohSqezu7jYzM9PT0/v7J5XL5X19febm5iohQtSBfngqRi5jpK+vT6FQWFhYqIwvs7KygoODq6uryZafmGcWrMAwOkRaWtq8efNsbGykUumpU6d27NgRFxe3ffv2f1suXaGkpMTIyMjd3R1ayb/zzjvW1tbQ9epZoKenx9vb29zcXMVwBvPMgq0QMTpEQkJCaGgom81+8OABk8lcv379tm3b/m2hdIirV69GREQwGAwajTY4ODhjxgyNFpj/STw9Paurq42NjbOysv5tWTC6Ah6BYXSLpqamzs5OGo02adIkLX5Ozyxisbi5uVkul9vZ2Wl04v6vUlVVNTg46Obm9oTnaTG6DFZgGAwGg3kqwVaIGAwGg3kqwQoMg8FgME8l/weAUSxWp2R2fwAAAABJRU5ErkJggg==" /> + +<pre class="output"> +Output written to: +gRNA_edit.xlsx +</pre> + + + + <HR/> + <div class="footer"> + <p> + Published from <a href="report.jmd">report.jmd</a> + using <a href="http://github.com/JunoLab/Weave.jl">Weave.jl</a> v0.10.10 on 2022-05-12. + </p> + </div> + </div> + </div> + </div> +</BODY> + +</HTML>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/test_gfd_report.html Thu May 12 17:39:18 2022 +0000 @@ -0,0 +1,704 @@ +<!DOCTYPE html> +<HTML lang = "en"> +<HEAD> + <meta charset="UTF-8"/> + <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes"> + <title>MultiplexCrisprDOE</title> + + + <script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: {inlineMath: [['$','$'], ['\\(','\\)']]}, + TeX: { equationNumbers: { autoNumber: "AMS" } } + }); + </script> + + <script type="text/javascript" async src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> + </script> + + +<style> +pre.hljl { + border: 1px solid #ccc; + margin: 5px; + padding: 5px; + overflow-x: auto; + color: rgb(68,68,68); background-color: rgb(251,251,251); } +pre.hljl > span.hljl-t { } +pre.hljl > span.hljl-w { } +pre.hljl > span.hljl-e { } +pre.hljl > span.hljl-eB { } +pre.hljl > span.hljl-o { } +pre.hljl > span.hljl-k { color: rgb(148,91,176); font-weight: bold; } +pre.hljl > span.hljl-kc { color: rgb(59,151,46); font-style: italic; } +pre.hljl > span.hljl-kd { color: rgb(214,102,97); font-style: italic; } +pre.hljl > span.hljl-kn { color: rgb(148,91,176); font-weight: bold; } +pre.hljl > span.hljl-kp { color: rgb(148,91,176); font-weight: bold; } +pre.hljl > span.hljl-kr { color: rgb(148,91,176); font-weight: bold; } +pre.hljl > span.hljl-kt { color: rgb(148,91,176); font-weight: bold; } +pre.hljl > span.hljl-n { } +pre.hljl > span.hljl-na { } +pre.hljl > span.hljl-nb { } +pre.hljl > span.hljl-nbp { } +pre.hljl > span.hljl-nc { } +pre.hljl > span.hljl-ncB { } +pre.hljl > span.hljl-nd { color: rgb(214,102,97); } +pre.hljl > span.hljl-ne { } +pre.hljl > span.hljl-neB { } +pre.hljl > span.hljl-nf { color: rgb(66,102,213); } +pre.hljl > span.hljl-nfm { color: rgb(66,102,213); } +pre.hljl > span.hljl-np { } +pre.hljl > span.hljl-nl { } +pre.hljl > span.hljl-nn { } +pre.hljl > span.hljl-no { } +pre.hljl > span.hljl-nt { } +pre.hljl > span.hljl-nv { } +pre.hljl > span.hljl-nvc { } +pre.hljl > span.hljl-nvg { } +pre.hljl > span.hljl-nvi { } +pre.hljl > span.hljl-nvm { } +pre.hljl > span.hljl-l { } +pre.hljl > span.hljl-ld { color: rgb(148,91,176); font-style: italic; } +pre.hljl > span.hljl-s { color: rgb(201,61,57); } +pre.hljl > span.hljl-sa { color: rgb(201,61,57); } +pre.hljl > span.hljl-sb { color: rgb(201,61,57); } +pre.hljl > span.hljl-sc { color: rgb(201,61,57); } +pre.hljl > span.hljl-sd { color: rgb(201,61,57); } +pre.hljl > span.hljl-sdB { color: rgb(201,61,57); } +pre.hljl > span.hljl-sdC { color: rgb(201,61,57); } +pre.hljl > span.hljl-se { color: rgb(59,151,46); } +pre.hljl > span.hljl-sh { color: rgb(201,61,57); } +pre.hljl > span.hljl-si { } +pre.hljl > span.hljl-so { color: rgb(201,61,57); } +pre.hljl > span.hljl-sr { color: rgb(201,61,57); } +pre.hljl > span.hljl-ss { color: rgb(201,61,57); } +pre.hljl > span.hljl-ssB { color: rgb(201,61,57); } +pre.hljl > span.hljl-nB { color: rgb(59,151,46); } +pre.hljl > span.hljl-nbB { color: rgb(59,151,46); } +pre.hljl > span.hljl-nfB { color: rgb(59,151,46); } +pre.hljl > span.hljl-nh { color: rgb(59,151,46); } +pre.hljl > span.hljl-ni { color: rgb(59,151,46); } +pre.hljl > span.hljl-nil { color: rgb(59,151,46); } +pre.hljl > span.hljl-noB { color: rgb(59,151,46); } +pre.hljl > span.hljl-oB { color: rgb(102,102,102); font-weight: bold; } +pre.hljl > span.hljl-ow { color: rgb(102,102,102); font-weight: bold; } +pre.hljl > span.hljl-p { } +pre.hljl > span.hljl-c { color: rgb(153,153,119); font-style: italic; } +pre.hljl > span.hljl-ch { color: rgb(153,153,119); font-style: italic; } +pre.hljl > span.hljl-cm { color: rgb(153,153,119); font-style: italic; } +pre.hljl > span.hljl-cp { color: rgb(153,153,119); font-style: italic; } +pre.hljl > span.hljl-cpB { color: rgb(153,153,119); font-style: italic; } +pre.hljl > span.hljl-cs { color: rgb(153,153,119); font-style: italic; } +pre.hljl > span.hljl-csB { color: 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red +} + +code, +kbd, +pre, +samp { + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; + font-size: 0.9em; +} + + +@media (min-width: 400px) {} +@media (min-width: 550px) {} +@media (min-width: 750px) {} +@media (min-width: 1000px) {} +@media (min-width: 1200px) {} + +h1.title {margin-top : 20px} +img {max-width : 100%} +div.title {text-align: center;} + + </style> +</HEAD> + +<BODY> + <div class ="container"> + <div class = "row"> + <div class = "col-md-12 twelve columns"> + <div class="title"> + <h1 class="title">MultiplexCrisprDOE</h1> + + + </div> + + +<!-- this setup dependencies, but doesn't appear in the generated document --> + + + +<h2>Tool</h2> +<ul> +<li><p><strong>Method:</strong> gRNA_ frequency _distribution</p> +</li> +<li><p><strong>Description:</strong> Generates vector with frequencies in the combinatorial gRNA/Cas9 construct library for all gRNAs</p> +</li> +<li><p><strong>Mode:</strong> </p> +</li> +<li><p><strong>Mode description:</strong> </p> +</li> +</ul> +<h2>Variables</h2> + + +<div class="data-frame"><p>7 rows × 2 columns</p><table class="data-frame"><thead><tr><th></th><th>Argument</th><th>Value</th></tr><tr><th></th><th title="Any">Any</th><th title="Any">Any</th></tr></thead><tbody><tr><th>1</th><td>Plant library size</td><td>75</td></tr><tr><th>2</th><td>SD on the gRNA abundances</td><td>25</td></tr><tr><th>3</th><td>Minimal gRNA abundance</td><td>50</td></tr><tr><th>4</th><td>Maximal gRNA abundance</td><td>100</td></tr><tr><th>5</th><td>Total number of gRNAs</td><td>120</td></tr><tr><th>6</th><td>Convert gRNA abundances to relative frequencies</td><td>false</td></tr><tr><th>7</th><td>Plot gRNA abundances</td><td>false</td></tr></tbody></table></div> + +<img 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" /> + + +<pre class="output"> +Output written to: +gRNA_reads.xlsx +</pre> + + + + <HR/> + <div class="footer"> + <p> + Published from <a href="report.jmd">report.jmd</a> + using <a href="http://github.com/JunoLab/Weave.jl">Weave.jl</a> v0.10.10 on 2022-05-11. + </p> + </div> + </div> + </div> + </div> +</BODY> + +</HTML>
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red +} + +code, +kbd, +pre, +samp { + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; + font-size: 0.9em; +} + + +@media (min-width: 400px) {} +@media (min-width: 550px) {} +@media (min-width: 750px) {} +@media (min-width: 1000px) {} +@media (min-width: 1200px) {} + +h1.title {margin-top : 20px} +img {max-width : 100%} +div.title {text-align: center;} + + </style> +</HEAD> + +<BODY> + <div class ="container"> + <div class = "row"> + <div class = "col-md-12 twelve columns"> + <div class="title"> + <h1 class="title">MultiplexCrisprDOE</h1> + + + </div> + + +<!-- this setup dependencies, but doesn't appear in the generated document --> + + + +<h2>Tool</h2> +<ul> +<li><p><strong>Method:</strong> simulation</p> +</li> +<li><p><strong>Description:</strong> simulation-based approaches for computing the minimal plant library size that guarantees full combinatorial coverage (and other relevant statistics)</p> +</li> +<li><p><strong>Mode:</strong> simulate<em>Nx2</em>countKOs</p> +</li> +<li><p><strong>Mode description:</strong> Counts of the number of knockouts per plant in the experiment</p> +</li> +</ul> +<h2>Variables</h2> + + +<div class="data-frame"><p>8 rows × 2 columns</p><table class="data-frame"><thead><tr><th></th><th>Argument</th><th>Value</th></tr><tr><th></th><th title="Any">Any</th><th title="Any">Any</th></tr></thead><tbody><tr><th>1</th><td># of target genes in the experiment</td><td>20</td></tr><tr><th>2</th><td># of gRNAs designed per target gene</td><td>6</td></tr><tr><th>3</th><td># of gRNAs / combi gRNA/Cas construct</td><td>2</td></tr><tr><th>4</th><td>Total number of gRNAs</td><td>120</td></tr><tr><th>5</th><td>Relative frequencies for all gRNAs</td><td>example_data.xlsx</td></tr><tr><th>6</th><td>Genome editing efficiencies for all gRNAs</td><td>example_data.xlsx</td></tr><tr><th>7</th><td>Global knockout efficiency</td><td>0.8</td></tr><tr><th>8</th><td># of simulated experiments</td><td>10</td></tr></tbody></table></div> + +<img 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" 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" /> + +<pre class="output"> +Output written to: +countKOs.xlsx +</pre> + + + + <HR/> + <div class="footer"> + <p> + Published from <a href="report.jmd">report.jmd</a> + using <a href="http://github.com/JunoLab/Weave.jl">Weave.jl</a> v0.10.10 on 2022-05-12. + </p> + </div> + </div> + </div> + </div> +</BODY> + +</HTML>