crisprBowtie: alignment of gRNA spacer sequences using bowtie
================
- <a href="#overview-of-crisprbowtie"
id="toc-overview-of-crisprbowtie">Overview of crisprBowtie</a>
- <a href="#installation-and-getting-started"
id="toc-installation-and-getting-started">Installation and getting
started</a>
- <a href="#software-requirements" id="toc-software-requirements">Software
requirements</a>
- <a href="#os-requirements" id="toc-os-requirements">OS Requirements</a>
- <a href="#installation-from-bioconductor"
id="toc-installation-from-bioconductor">Installation from
Bioconductor</a>
- <a href="#building-a-bowtie-index"
id="toc-building-a-bowtie-index">Building a bowtie index</a>
- <a href="#alignment-using-runcrisprbowtie"
id="toc-alignment-using-runcrisprbowtie">Alignment using
<code>runCrisprBowtie</code></a>
- <a href="#applications-beyond-crispr"
id="toc-applications-beyond-crispr">Applications beyond CRISPR</a>
- <a href="#example-using-rnai-sirna-design"
id="toc-example-using-rnai-sirna-design">Example using RNAi (siRNA
design)</a>
- <a href="#reproducibility" id="toc-reproducibility">Reproducibility</a>
- <a href="#references" id="toc-references">References</a>
Authors: Jean-Philippe Fortin
Date: July 13, 2022
# 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](https://github.com/crisprVerse/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](https://github.com/crisprVerse/crisprDesign) of the
[crisprVerse](https://github.com/crisprVerse) ecosystem. See the
`addSpacerAlignments` function in `crisprDesign` for more details.
# Installation and getting started
## Software requirements
### OS Requirements
This package is supported for macOS, Linux and Windows machines. Package
was developed and tested on R version 4.2.1.
## Installation from Bioconductor
`crisprBowtie` can be installed from from the Bioconductor devel branch
using the following commands in a fresh R session:
``` r
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version="devel")
BiocManager::install("crisprBowtie")
```
The complete documentation for the package can be found
[here](https://bioconductor.org/packages/devel/bioc/manuals/crisprBowtie/man/crisprBowtie.pdf).
# 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:
``` r
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](https://github.com/crisprVerse/Tutorials/tree/master/Building_Genome_Indices).
# 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`.
``` r
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
# 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.
## 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:
``` r
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
# Reproducibility
``` r
sessionInfo()
```
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] crisprBowtie_1.1.1 Rbowtie_1.37.0
##
## loaded via a namespace (and not attached):
## [1] SummarizedExperiment_1.27.2 tidyselect_1.1.2
## [3] xfun_0.32 purrr_0.3.4
## [5] lattice_0.20-45 vctrs_0.4.1
## [7] htmltools_0.5.3 stats4_4.2.1
## [9] rtracklayer_1.57.0 yaml_2.3.5
## [11] utf8_1.2.2 XML_3.99-0.10
## [13] rlang_1.0.5 pillar_1.8.1
## [15] glue_1.6.2 BiocParallel_1.31.12
## [17] bit64_4.0.5 BiocGenerics_0.43.4
## [19] matrixStats_0.62.0 GenomeInfoDbData_1.2.8
## [21] lifecycle_1.0.1 stringr_1.4.1
## [23] zlibbioc_1.43.0 MatrixGenerics_1.9.1
## [25] Biostrings_2.65.3 codetools_0.2-18
## [27] evaluate_0.16 restfulr_0.0.15
## [29] Biobase_2.57.1 knitr_1.40
## [31] tzdb_0.3.0 IRanges_2.31.2
## [33] fastmap_1.1.0 GenomeInfoDb_1.33.7
## [35] parallel_4.2.1 fansi_1.0.3
## [37] crisprBase_1.1.8 readr_2.1.2
## [39] BSgenome_1.65.2 DelayedArray_0.23.1
## [41] S4Vectors_0.35.3 vroom_1.5.7
## [43] XVector_0.37.1 bit_4.0.4
## [45] Rsamtools_2.13.4 rjson_0.2.21
## [47] hms_1.1.2 digest_0.6.29
## [49] stringi_1.7.8 BiocIO_1.7.1
## [51] GenomicRanges_1.49.1 grid_4.2.1
## [53] cli_3.4.0 tools_4.2.1
## [55] bitops_1.0-7 magrittr_2.0.3
## [57] RCurl_1.98-1.8 tibble_3.1.8
## [59] crayon_1.5.1 pkgconfig_2.0.3
## [61] ellipsis_0.3.2 Matrix_1.4-1
## [63] rmarkdown_2.16 rstudioapi_0.14
## [65] R6_2.5.1 GenomicAlignments_1.33.1
## [67] compiler_4.2.1
# References
<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-langmead2009bowtie" class="csl-entry">
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>.
</div>
</div>
