MinSNPs Workflow

library(minSNPs)
#> The minSNPs version loaded is: 0.2.0
library(BiocParallel) # optional, but needed for parallel processing

Reading & processing input for further analysis

read_fasta is provided as a way to read fasta file, equivalent function, e.g., from Biostrings and read.fasta from seqinr can be used.

isolates_from_default <- read_fasta(
  system.file("extdata", "Chlamydia_mapped.fasta", package = "minSNPs"))
processed_from_default <- process_allele(isolates_from_default)
#> Ignored samples: 
#>  
#> Ignored  0  positions

Subsequent analyses can use output from process_allele.

Identifying SNPs with high Simpson’s index

high_d_snps <- find_optimised_snps(seqc = processed_from_default,
  metric = "simpson", number_of_result = 1, max_depth = 1,
  included_positions = c(), excluded_positions = c())

Identifying SNPs discriminating a group of interest

discriminating_snps <- find_optimised_snps(seqc = processed_from_default,
  metric = "percent", number_of_result = 1, max_depth = 1,
  included_positions = c(), excluded_positions = c(),
  goi = c("A_D213", "H_S1432"))

Displaying/saving result

cat("High D SNPs\n")
#> High D SNPs
output_result(high_d_snps)
#> Result - 1
#> Position(s)  Score
#> "1988"   0.734415584415584
#> 
#> Groups
#> T    "A_D213, H_S1432, Ia_SotoGIa3, Ia_SotoGIa1, C_UW1, A_7249, A_HAR-13, C_Aus10, H_R31975, A_2497, L3_404, K_SotoGK1, A_363, J_6276, A_5291, C_TW3"
#> G    "B_Aus3, L1_440, Ba_Aus25, B_TZ1A828, Ba_Apache2, D_UW-3, Ds_2923, L1_SA16, B_Har36, D_SotoGD6, D_SotoGD5, B_Jali20, D_SotoGD1"
#> A    "F_SW5, L2b_CV204, L2_LST, L2b_UCH-1, L2b_795, L1_224, G_11074, F_70, L2b_C1, G_SotoGG1, G_11222, L2b_8200, F_SW4, G_9301, L2b_C2, G_9768, L2_434, F_SotoGF3, L1_115, L2b_UCH-2"
#> C    "E_150, E_Bour, E_SotoGE4, E_SW2, E_SW3, E_SotoGE8, E_11023"
#> 
#> 
#> Additional details
#> Metric:   simpson
#> Excluded Positions:   ""
#> Excluded Positions From process_allele:   ""
#> Included Positions:   ""
#> Group of interest:    ""
#> All analysed sequences:   "A_D213, H_S1432, Ia_SotoGIa3, B_Aus3, Ia_SotoGIa1, C_UW1, F_SW5, L2b_CV204, L1_440, A_7249, Ba_Aus25, L2_LST, B_TZ1A828, A_HAR-13, E_150, E_Bour, C_Aus10, H_R31975, E_SotoGE4, L2b_UCH-1, L2b_795, A_2497, Ba_Apache2, E_SW2, L3_404, L1_224, D_UW-3, G_11074, Ds_2923, F_70, K_SotoGK1, E_SW3, L2b_C1, E_SotoGE8, G_SotoGG1, G_11222, A_363, L1_SA16, L2b_8200, J_6276, F_SW4, G_9301, L2b_C2, A_5291, G_9768, L2_434, F_SotoGF3, C_TW3, E_11023, L1_115, B_Har36, L2b_UCH-2, D_SotoGD6, D_SotoGD5, B_Jali20, D_SotoGD1"
cat("SNPws discriminating against A_D213, H_S1432\n")
#> SNPws discriminating against A_D213, H_S1432
output_result(discriminating_snps)
#> Result - 1
#> Position(s)  Score
#> "1806"   0.944444444444444
#> 
#> Groups
#> *target* - G "A_D213, H_S1432, C_UW1, C_Aus10, C_TW3"
#> C    "Ia_SotoGIa3, Ia_SotoGIa1, A_7249, A_HAR-13, A_2497, D_UW-3, Ds_2923, K_SotoGK1, A_363, J_6276, A_5291, D_SotoGD6, D_SotoGD5, D_SotoGD1"
#> A    "B_Aus3, F_SW5, L2b_CV204, L1_440, Ba_Aus25, L2_LST, B_TZ1A828, E_150, E_Bour, H_R31975, E_SotoGE4, L2b_UCH-1, L2b_795, Ba_Apache2, E_SW2, L3_404, L1_224, G_11074, F_70, E_SW3, L2b_C1, E_SotoGE8, G_SotoGG1, G_11222, L1_SA16, L2b_8200, F_SW4, G_9301, L2b_C2, G_9768, L2_434, F_SotoGF3, E_11023, L1_115, B_Har36, L2b_UCH-2, B_Jali20"
#> Residuals:   "C_UW1 (G), C_Aus10 (G), C_TW3 (G)"
#> 
#> 
#> Additional details
#> Metric:   percent
#> Excluded Positions:   ""
#> Excluded Positions From process_allele:   ""
#> Included Positions:   ""
#> Group of interest:    "A_D213, H_S1432"
#> All analysed sequences:   "A_D213, H_S1432, Ia_SotoGIa3, B_Aus3, Ia_SotoGIa1, C_UW1, F_SW5, L2b_CV204, L1_440, A_7249, Ba_Aus25, L2_LST, B_TZ1A828, A_HAR-13, E_150, E_Bour, C_Aus10, H_R31975, E_SotoGE4, L2b_UCH-1, L2b_795, A_2497, Ba_Apache2, E_SW2, L3_404, L1_224, D_UW-3, G_11074, Ds_2923, F_70, K_SotoGK1, E_SW3, L2b_C1, E_SotoGE8, G_SotoGG1, G_11222, A_363, L1_SA16, L2b_8200, J_6276, F_SW4, G_9301, L2b_C2, A_5291, G_9768, L2_434, F_SotoGF3, C_TW3, E_11023, L1_115, B_Har36, L2b_UCH-2, D_SotoGD6, D_SotoGD5, B_Jali20, D_SotoGD1"

output_result(high_d_snps, view = "csv",
  file_name = "high_d_snps.csv")
output_result(discriminating_snps, view = "csv",
  file_name = "discriminating_snps.csv")