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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")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.