Date: 2025-11-04

# crisprBowtie: alignment of gRNA spacer sequences using bowtie

Jean-Philippe Fortin1*

1Department of Data Science and Statistical Computing, gRED, Genentech

*fortin946@gmail.com

#### 2025-11-04

# 1 Overview of crisprBowtie

`crisprBowtie` provides two main functions to align short DNA sequences to a reference genome using the short read aligner bowtie (Langmead et al. 2009) and return the alignments as R objects: `runBowtie` and `runCrisprBowtie`. It utilizes the Bioconductor package `Rbowtie` to access the Bowtie program in a platform-independent manner. This means that users do not need to install Bowtie prior to using `crisprBowtie`.

The latter function (`runCrisprBowtie`) is specifically designed to map and annotate CRISPR guide RNA (gRNA) spacer sequences using CRISPR nuclease objects and CRISPR genomic arithmetics defined in the Bioconductor package crisprBase. This enables a fast and accurate on-target and off-target search of gRNA spacer sequences for virtually any type of CRISPR nucleases. It also provides an off-target search engine for our main gRNA design package crisprDesign of the crisprVerse ecosystem. See the `addSpacerAlignments` function in `crisprDesign` for more details.

# 2 Installation and getting started

## 2.1 Software requirements

### 2.1.1 OS Requirements

This package is supported for macOS, Linux and Windows machines. Package was developed and tested on R version 4.2.

## 2.2 Installation from Bioconductor

`crisprBowtie` can be installed from from the Bioconductor devel branch using the following commands in a fresh R session:

    if (!require("BiocManager", quietly = TRUE))
        install.packages("BiocManager")

    BiocManager::install(version="devel")
    BiocManager::install("crisprBowtie")

# 3 Building a bowtie index

To use `runBowtie` or `runCrisprBowtie`, users need to first build a Bowtie genome index. For a given genome, this step has to be done only once. The `Rbowtie` package conveniently provides the function `bowtie_build` to build a Bowtie index from any custom genome from a FASTA file.

As an example, we build a Bowtie index for a small portion of the human chromosome 1 (`chr1.fa` file provided in the `crisprBowtie` package) and save the index file as `myIndex` to a temporary directory:

    library(Rbowtie)
    fasta <- file.path(find.package("crisprBowtie"), "example/chr1.fa")
    tempDir <- tempdir()
    Rbowtie::bowtie_build(fasta,
                          outdir=tempDir,
                          force=TRUE,
                          prefix="myIndex")

To learn how to create a Bowtie index for a complete genome or transcriptome, please visit our tutorial page.

# 4 Alignment using `runCrisprBowtie`

As an example, we align 6 spacer sequences (of length 20bp) to the custom genome built above, allowing a maximum of 3 mismatches between the spacer and protospacer sequences.

We specify that the search is for the wildtype Cas9 (SpCas9) nuclease by providing the `CrisprNuclease` object `SpCas9` available through the `crisprBase` package. The argument `canonical=FALSE` specifies that non-canonical PAM sequences are also considered (NAG and NGA for SpCas9). The function `getAvailableCrisprNucleases` in `crisprBase` returns a character vector of available `crisprNuclease` objects found in `crisprBase`.

    library(crisprBowtie)
    data(SpCas9, package="crisprBase")
    crisprNuclease <- SpCas9
    spacers <- c("TCCGCGGGCGACAATGGCAT",
                 "TGATCCCGCGCTCCCCGATG",
                 "CCGGGAGCCGGGGCTGGACG",
                 "CCACCCTCAGGTGTGCGGCC",
                 "CGGAGGGCTGCAGAAAGCCT",
                 "GGTGATGGCGCGGGCCGGGC")
    runCrisprBowtie(spacers,
                    crisprNuclease=crisprNuclease,
                    n_mismatches=3,
                    canonical=FALSE,
                    bowtie_index=file.path(tempDir, "myIndex"))

    ## [runCrisprBowtie] Searching for SpCas9 protospacers

    ##                 spacer          protospacer pam  chr pam_site strand
    ## 1 CCACCCTCAGGTGTGCGGCC CCACCCTCAGGTGTGCGGCC TGG chr1      679      +
    ## 2 CCGGGAGCCGGGGCTGGACG CCGGGAGCCGGGGCTGGACG GAG chr1      466      +
    ## 3 CGGAGGGCTGCAGAAAGCCT CGGAGGGCTGCAGAAAGCCT TGG chr1      706      +
    ## 4 GGTGATGGCGCGGGCCGGGC GGTGATGGCGCGGGCCGGGC CGG chr1      831      +
    ## 5 TGATCCCGCGCTCCCCGATG TGATCCCGCGCTCCCCGATG CAG chr1      341      +
    ##   n_mismatches canonical
    ## 1            0      TRUE
    ## 2            0     FALSE
    ## 3            0      TRUE
    ## 4            0      TRUE
    ## 5            0     FALSE

# 5 Applications beyond CRISPR

The function `runBowtie` is similar to `runCrisprBowtie`, but does not impose constraints on PAM sequences. It can be used to search for any short read sequence in a genome.

## 5.1 Example using RNAi (siRNA design)

Seed-related off-targets caused by mismatch tolerance outside of the seed region is a well-studied and characterized problem observed in RNA interference (RNA) experiments. `runBowtie` can be used to map shRNA/siRNA seed sequences to reference genomes to predict putative off-targets:

    seeds <- c("GTAAAGGT", "AAGGATTG")
    runBowtie(seeds,
              n_mismatches=2,
              bowtie_index=file.path(tempDir, "myIndex"))

    ##      query   target  chr pos strand n_mismatches
    ## 1 AAGGATTG AAAGAATG chr1 163      -            2
    ## 2 AAGGATTG AAGCCTTG chr1 700      +            2
    ## 3 AAGGATTG AAGGCTTT chr1 699      -            2
    ## 4 AAGGATTG CAGGCTTG chr1 905      -            2
    ## 5 GTAAAGGT GGGAAGGT chr1 724      +            2

# 6 Reproducibility

    sessionInfo()

    ## R Under development (unstable) (2025-10-20 r88955)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_GB              LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: America/New_York
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] crisprBowtie_1.15.0 Rbowtie_1.51.0      BiocStyle_2.39.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] SummarizedExperiment_1.41.0 rjson_0.2.23
    ##  [3] xfun_0.54                   bslib_0.9.0
    ##  [5] Biobase_2.71.0              lattice_0.22-7
    ##  [7] tzdb_0.5.0                  vctrs_0.6.5
    ##  [9] tools_4.6.0                 bitops_1.0-9
    ## [11] generics_0.1.4              stats4_4.6.0
    ## [13] curl_7.0.0                  parallel_4.6.0
    ## [15] tibble_3.3.0                pkgconfig_2.0.3
    ## [17] Matrix_1.7-4                BSgenome_1.79.1
    ## [19] S4Vectors_0.49.0            cigarillo_1.1.0
    ## [21] lifecycle_1.0.4             compiler_4.6.0
    ## [23] stringr_1.6.0               Rsamtools_2.27.0
    ## [25] Biostrings_2.79.1           Seqinfo_1.1.0
    ## [27] codetools_0.2-20            htmltools_0.5.8.1
    ## [29] sass_0.4.10                 RCurl_1.98-1.17
    ## [31] yaml_2.3.10                 pillar_1.11.1
    ## [33] crayon_1.5.3                jquerylib_0.1.4
    ## [35] BiocParallel_1.45.0         cachem_1.1.0
    ## [37] DelayedArray_0.37.0         abind_1.4-8
    ## [39] tidyselect_1.2.1            digest_0.6.37
    ## [41] stringi_1.8.7               restfulr_0.0.16
    ## [43] bookdown_0.45               fastmap_1.2.0
    ## [45] grid_4.6.0                  archive_1.1.12
    ## [47] cli_3.6.5                   SparseArray_1.11.1
    ## [49] magrittr_2.0.4              S4Arrays_1.11.0
    ## [51] XML_3.99-0.19               withr_3.0.2
    ## [53] readr_2.1.5                 bit64_4.6.0-1
    ## [55] rmarkdown_2.30              XVector_0.51.0
    ## [57] httr_1.4.7                  matrixStats_1.5.0
    ## [59] bit_4.6.0                   hms_1.1.4
    ## [61] evaluate_1.0.5              knitr_1.50
    ## [63] GenomicRanges_1.63.0        IRanges_2.45.0
    ## [65] BiocIO_1.21.0               rtracklayer_1.71.0
    ## [67] rlang_1.1.6                 glue_1.8.0
    ## [69] crisprBase_1.15.0           BiocManager_1.30.26
    ## [71] BiocGenerics_0.57.0         vroom_1.6.6
    ## [73] jsonlite_2.0.0              R6_2.6.1
    ## [75] MatrixGenerics_1.23.0       GenomicAlignments_1.47.0

# References

Langmead, Ben, Cole Trapnell, Mihai Pop, and Steven L. Salzberg. 2009. “Ultrafast and Memory-Efficient Alignment of Short Dna Sequences to the Human Genome.” Genome Biology 10 (3): R25. https://doi.org/10.1186/gb-2009-10-3-r25.