seqR: Fast and Comprehensive K-Mer Counting Package

Source: R/package.R

The seqR package provides in-memory, probabilistic, highly-optimized, and multi-threaded implementation of k-mer counting.

Author

Jadwiga Słowik

Examples

# Load exemplary sequences
data(CsgA)

# Counting 1-mers (amino acid composition)
count_kmers(
    CsgA,
    k = 1,
    batch_size = 1)   

#> Single-threaded mode enabled. In order to speed up computations, increase defined batch_size or use a default value
#> 5 x 19 sparse Matrix of class "dgCMatrix"
#>    [[ suppressing 19 column names ‘R’, ‘L’, ‘Y’ ... ]]
#>                                                  
#> [1,] 2 9 4 1 8 5 10 2 2 4 16 29 4 11  9 2 16 3 14
#> [2,] 3 8 5 2 7 6 11 2 2 4 17 22 3 11 10 2 20 1 15
#> [3,] 3 8 5 2 7 6 11 2 2 4 17 22 3 11 10 2 20 1 15
#> [4,] 2 9 4 1 9 5 10 2 1 4 15 30 4 11  9 2 17 4 13
#> [5,] 3 8 5 2 7 6 11 2 2 4 17 22 3 11 10 2 20 1 15


# Counting 1-mers and 2-mers
count_multimers(
    CsgA,
    k_vector = c(1, 2),
    batch_size = 1)

#> Single-threaded mode enabled. In order to speed up computations, increase defined batch_size or use a default value
#> 5 x 144 sparse Matrix of class "dgCMatrix"
#>    [[ suppressing 144 column names ‘R’, ‘L’, ‘Y’ ... ]]
#>                                                                                
#> [1,] 2 9 4 1 8 5 10 2 2 4 16 29 4 11  9 2 16 3 14 1 2 1 1 1 1 2 1 1 1 1 1 3 1 2
#> [2,] 3 8 5 2 7 6 11 2 2 4 17 22 3 11 10 2 20 1 15 1 3 1 . . 3 1 1 1 2 5 2 3 . 2
#> [3,] 3 8 5 2 7 6 11 2 2 4 17 22 3 11 10 2 20 1 15 1 3 1 . . 3 1 1 1 2 5 2 3 . 2
#> [4,] 2 9 4 1 9 5 10 2 1 4 15 30 4 11  9 2 17 4 13 1 2 1 1 1 1 2 1 1 1 1 1 3 1 2
#> [5,] 3 8 5 2 7 6 11 2 2 4 17 22 3 11 10 2 20 1 15 1 3 1 . . 3 1 1 1 2 5 2 3 . 2
#>                                                                                
#> [1,] 1 3 1 3 1 2 7 1 1 1 1 3 1 1 2 2 1 1 12 3 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2
#> [2,] . 2 . 2 1 2 3 . 1 2 . 3 1 . 3 2 2 1  6 4 2 3 1 2 1 . 1 . 1 1 . 1 . 1 1 2 .
#> [3,] . 2 . 2 1 2 3 . 1 2 . 3 1 . 3 2 2 1  6 4 2 3 1 2 1 . 1 . 1 1 . 1 . 1 1 2 .
#> [4,] 1 3 1 3 1 2 6 1 1 2 1 3 2 1 2 2 1 . 13 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
#> [5,] . 2 . 2 1 2 3 . 1 2 . 3 1 . 3 2 2 1  6 4 2 3 1 2 1 . 1 . 1 1 . 1 . 1 1 2 .
#>                                                                               
#> [1,] 1 1 1 1 1 1 2 7 1 1 2 1 1 1 2 1 1 1 1 1 1 2 2 3 1 1 1 1 1 1 1 2 2 1 1 3 1
#> [2,] . 1 . 1 1 1 1 5 . . 2 . . 1 1 1 . . . 1 1 2 1 4 1 1 1 1 . 1 . 2 3 1 1 1 .
#> [3,] . 1 . 1 1 1 1 5 . . 2 . . 1 1 1 . . . 1 1 2 1 4 1 1 1 1 . 1 . 2 3 1 1 1 .
#> [4,] 1 1 . 1 1 1 1 7 1 1 2 1 1 1 2 1 . 1 1 1 1 2 2 3 1 . 1 1 1 1 1 1 2 1 1 3 1
#> [5,] . 1 . 1 1 1 1 5 . . 2 . . 1 1 1 . . . 1 1 2 1 4 1 1 1 1 . 1 . 2 3 1 1 1 .
#>                                                                             
#> [1,] 1 1 2 1 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
#> [2,] . 1 . 1 3 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 . . . .
#> [3,] . 1 . 1 3 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 . . . .
#> [4,] 1 1 2 1 2 . . . . . . . . . . . . . 1 . . . . . . . . . . . . . 1 1 1 1
#> [5,] . 1 . 1 3 1 1 1 1 1 1 1 1 1 1 2 1 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 . . . .