Why SSW?

I first learned the Smith-Waterman algorithm during a biomathematics summer school course taught by Professor Michael Waterman in 2014. Later, I realized how fundamental this algorithm is across bioinformatics. However, its computational intensity can be a bottleneck in large-scale genomic analyses. This is where the SSW C library comes into play.

The SSW C library accelerates Smith-Waterman alignment by leveraging SIMD (Single Instruction, Multiple Data) instructions, which allow for efficient vectorization on modern CPUs. Besides the speedup, SSW provides detailed alignment information, including suboptimal alignment scores, which makes it useful for tasks ranging from short-read alignment to protein database search. Importantly, it is a C library—one can easily integrate it into their bioinformatics workflows without introducing unnecessary dependencies.

With ssw-r, we bridge the gap between high-performance SSW C library and the rich ecosystem of R’s bioinformatics tools (for example, Bioconductor). This potentially enables faster sequence analyses in many tasks.

Examples

Let’s look at the two main functions of ssw-r: align() and force_align(). For more examples, check out the vignette.

library("ssw")

First, align a short query sequence to a longer reference:

(x <- "GATTACA" |> align(reference = "CGGCTCTTGATTACAGGGTCT"))

CIGAR start index 8: 7M
optimal_score: 14
sub-optimal_score: 0
target_begin: 8	target_end: 14
query_begin: 0
query_end: 6

Target:        8    GATTACA    14
                    |||||||
Query:         0    GATTACA    6

x$alignment$optimal_score
#> [1] 14
x$alignment$sub_optimal_score
#> [1] 0

This example aligns a 7-base query to a 21-base reference. The match is identified with the alignment details, including the CIGAR string and the optimal alignment score.

In cases where we want to enforce strict alignment conditions, such as avoiding gaps, we can use the force_align() function.

(y <- "ACTG" |> force_align(reference = "TTTTCTGCCCCCACG"))

CIGAR start index 4: 3M
optimal_score: 6
sub-optimal_score: 0
target_begin: 4	target_end: 6
query_begin: 1
query_end: 3

Target:        4    CTG    6
                    |||
Query:         1    CTG    3

To view the full alignment results without truncation, use formatter():

(y |> formatter())
#> [[1]]
#> [1] "TTTTCTGCCCCCACG"
#>
#> [[2]]
#> [1] "   ACTG"

You can also pretty-print the formatted results directly:

y |> formatter(print = TRUE)
#> TTTTCTGCCCCCACG
#>    ACTG

Acknowledgments

I would like to thank Mengyao Zhao for creating the original SSW library and Nick Conway for maintaining the Python interface, ssw-py. If you use ssw-r in your work, please consider citing these foundational projects.

References