README.md

In seqR: Fast and Comprehensive K-Mer Counting Package

seqR - fast and comprehensive k-mer counting package

About

seqR is an R package for fast k-mer counting. It provides

• highly optimized (the core algorithm is written in C++)
• in-memory
• probabilistic (with configurable dimensionality of a hash value used for storing k-mers internally),
• multi-threaded (with a configurable size of the batch of sequences (batch_size) to process in a single step. If batch_size equals 1, the multi-threaded mode is disabled, which potentially causes a longer computation time)

implementation that supports

• various variants of k-mers (contiguous, gapped, and positional counterparts)
• all biological sequences (e.g., nucleic acids and proteins)

Moreover, the result optimizes memory consumption by the application of sparse matrices (see package Matrix), compatible with machine learning packages such as ranger and xgboost.

How to…

How to install

To install seqR from CRAN:

install.packages("seqR")

Alternatively, if you want to use the latest development version:

# install.packages("devtools")
devtools::install_github("slowikj/seqR")

How to use

The package provides two functions that facilitate k-mer counting

• count_kmers (used for counting k-mers of one type)
• count_multimers (a wrapper of count_kmers, used for counting k-mers of many types in a single invocation of the function)

and one function used for custom processing of k-mer matrices:

• rbind_columnwise (a helper function used for merging several k-mer matrices that do not have same sets of columns)

To learn more, see features overview vignette and reference.

Examples

counting 5-mers

count_kmers(sequences=c("AAAAAVVAVFF", "DFGSADFGSA"),
            k=5)
#> 2 x 12 sparse Matrix of class "dgCMatrix"
#>    [[ suppressing 12 column names 'A.A.A.A.A_0.0.0.0', 'A.V.V.A.V_0.0.0.0', 'V.V.A.V.F_0.0.0.0' ... ]]
#>                             
#> [1,] 1 1 1 1 1 1 1 . . . . .
#> [2,] . . . . . . . 1 1 1 2 1

counting gapped 5-mers with gaps (0, 1, 0, 2) (XX_XX__X)

count_kmers(sequences=c("AAAAAVVAVFF", "DFGSADFGSA"),
            kmer_gaps=c(0, 1, 0, 2))
#> 2 x 7 sparse Matrix of class "dgCMatrix"
#>      A.A.A.A.A_0.1.0.2 A.A.V.V.F_0.1.0.2 A.A.V.A.F_0.1.0.2 A.A.A.V.V_0.1.0.2
#> [1,]                 1                 1                 1                 1
#> [2,]                 .                 .                 .                 .
#>      G.S.D.F.A_0.1.0.2 F.G.A.D.S_0.1.0.2 D.F.S.A.G_0.1.0.2
#> [1,]                 .                 .                 .
#> [2,]                 1                 1                 1

counting 1-mers and 2-mers

data(CsgA)

CsgA[1L:2]
#> $`sp|P28307|CSGA_ECOLI Major curlin subunit OS=Escherichia coli (strain K12) OX=83333 GN=csgA PE=1 SV=3`
#>   [1] "M" "K" "L" "L" "K" "V" "A" "A" "I" "A" "A" "I" "V" "F" "S" "G" "S" "A"
#>  [19] "L" "A" "G" "V" "V" "P" "Q" "Y" "G" "G" "G" "G" "N" "H" "G" "G" "G" "G"
#>  [37] "N" "N" "S" "G" "P" "N" "S" "E" "L" "N" "I" "Y" "Q" "Y" "G" "G" "G" "N"
#>  [55] "S" "A" "L" "A" "L" "Q" "T" "D" "A" "R" "N" "S" "D" "L" "T" "I" "T" "Q"
#>  [73] "H" "G" "G" "G" "N" "G" "A" "D" "V" "G" "Q" "G" "S" "D" "D" "S" "S" "I"
#>  [91] "D" "L" "T" "Q" "R" "G" "F" "G" "N" "S" "A" "T" "L" "D" "Q" "W" "N" "G"
#> [109] "K" "N" "S" "E" "M" "T" "V" "K" "Q" "F" "G" "G" "G" "N" "G" "A" "A" "V"
#> [127] "D" "Q" "T" "A" "S" "N" "S" "S" "V" "N" "V" "T" "Q" "V" "G" "F" "G" "N"
#> [145] "N" "A" "T" "A" "H" "Q" "Y"
#> 
#> $`sp|P0A1E7|CSGA_SALEN Major curlin subunit OS=Salmonella enteritidis OX=149539 GN=csgA PE=1 SV=1`
#>   [1] "M" "K" "L" "L" "K" "V" "A" "A" "F" "A" "A" "I" "V" "V" "S" "G" "S" "A"
#>  [19] "L" "A" "G" "V" "V" "P" "Q" "W" "G" "G" "G" "G" "N" "H" "N" "G" "G" "G"
#>  [37] "N" "S" "S" "G" "P" "D" "S" "T" "L" "S" "I" "Y" "Q" "Y" "G" "S" "A" "N"
#>  [55] "A" "A" "L" "A" "L" "Q" "S" "D" "A" "R" "K" "S" "E" "T" "T" "I" "T" "Q"
#>  [73] "S" "G" "Y" "G" "N" "G" "A" "D" "V" "G" "Q" "G" "A" "D" "N" "S" "T" "I"
#>  [91] "E" "L" "T" "Q" "N" "G" "F" "R" "N" "N" "A" "T" "I" "D" "Q" "W" "N" "A"
#> [109] "K" "N" "S" "D" "I" "T" "V" "G" "Q" "Y" "G" "G" "N" "N" "A" "A" "L" "V"
#> [127] "N" "Q" "T" "A" "S" "D" "S" "S" "V" "M" "V" "R" "Q" "V" "G" "F" "G" "N"
#> [145] "N" "A" "T" "A" "N" "Q" "Y"

count_multimers(sequences=CsgA,
                k_vector = c(1, 2))
#> 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 . . . .

How to cite

For citation type:

citation("seqR")

or use:

Jadwiga Słowik and Michał Burdukiewicz (2021). seqR: fast and comprehensive k-mer counting package. R package version 1.0.0.

Benchmarks

The seqR package has been compared with other existing k-mer counting R packages: biogram, kmer, seqinr, and biostrings.

All benchmark experiments have been performed using Intel Core i7-6700HQ 2.60GHz 8 cores, using the microbenchmark R package.

Contiguous k-mers

Changing k

The input consists of one DNA sequence of length 3 000.

Changing the number of sequences

Each DNA sequence has 3 000 elements, contiguous 5-mer counting.

Gapped k-mers

Changing the first contiguous part of a k-mer

The input consists of one DNA sequence of length 1 000 000. Gapped 5-mers counting with base gaps (1, 0, 0, 1).

Changing the first gap size

The input consists of one DNA sequence of length 100 000. Gapped 5-mers counting with base gaps (1, 0, 0, 1).

seqR documentation built on Oct. 6, 2021, 1:10 a.m.