crisprScore: on-target and off-target scoring for CRISPR gRNAs
================
- <a href="#overview" id="toc-overview">Overview</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="#installation-from-github"
id="toc-installation-from-github">Installation from GitHub</a>
- <a href="#getting-started" id="toc-getting-started">Getting started</a>
- <a href="#on-targeting-efficiency-scores"
id="toc-on-targeting-efficiency-scores">On-targeting efficiency
scores</a>
- <a href="#cas9-methods" id="toc-cas9-methods">Cas9 methods</a>
- <a href="#rule-set-1" id="toc-rule-set-1">Rule Set 1</a>
- <a href="#azimuth" id="toc-azimuth">Azimuth</a>
- <a href="#rule-set-3" id="toc-rule-set-3">Rule Set 3</a>
- <a href="#deephf" id="toc-deephf">DeepHF</a>
- <a href="#deepspcas9" id="toc-deepspcas9">DeepSpCas9</a>
- <a href="#crisprscan" id="toc-crisprscan">CRISPRscan</a>
- <a href="#crisprater" id="toc-crisprater">CRISPRater</a>
- <a href="#crisprai" id="toc-crisprai">CRISPRai</a>
- <a href="#cas12a-methods" id="toc-cas12a-methods">Cas12a methods</a>
- <a href="#deepcpf1-score" id="toc-deepcpf1-score">DeepCpf1 score</a>
- <a href="#enpamgb-score" id="toc-enpamgb-score">enPAM+GB score</a>
- <a href="#cas13d-methods" id="toc-cas13d-methods">Cas13d methods</a>
- <a href="#casrxrf" id="toc-casrxrf">CasRxRF</a>
- <a href="#off-target-specificity-scores"
id="toc-off-target-specificity-scores">Off-target specificity scores</a>
- <a href="#mit-score" id="toc-mit-score">MIT score</a>
- <a href="#cfd-score" id="toc-cfd-score">CFD score</a>
- <a href="#indel-prediction-score" id="toc-indel-prediction-score">Indel
prediction score</a>
- <a href="#lindel-score-cas9" id="toc-lindel-score-cas9">Lindel score
(Cas9)</a>
- <a href="#license" id="toc-license">License</a>
- <a href="#reproducibility" id="toc-reproducibility">Reproducibility</a>
- <a href="#references" id="toc-references">References</a>
Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht, Pirunthan
Perampalan
Date: July 1, 2022
# Overview
The `crisprScore` package provides R wrappers of several on-target and
off-target scoring methods for CRISPR guide RNAs (gRNAs). The following
nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d
(CasRx). The available on-target cutting efficiency scoring methods are
RuleSet1, RuleSet3, Azimuth, DeepHF, DeepSpCas9, DeepCpf1, enPAM+GB,
CRISPRscan and CRISPRater. Both the CFD and MIT scoring methods are
available for off-target specificity prediction. The package also
provides a Lindel-derived score to predict the probability of a gRNA to
produce indels inducing a frameshift for the Cas9 nuclease. Note that
DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines.
Our work is described in a recent bioRxiv preprint: [“The crisprVerse: A
comprehensive Bioconductor ecosystem for the design of CRISPR guide RNAs
across nucleases and
technologies”](https://www.biorxiv.org/content/10.1101/2022.04.21.488824v3)
Our main gRNA design package
[crisprDesign](https://github.com/crisprVerse/crisprDesign) utilizes the
`crisprScore` package to add on- and off-target scores to user-designed
gRNAs; check out our [Cas9 gRNA tutorial
page](https://github.com/crisprVerse/Tutorials/tree/master/Design_CRISPRko_Cas9)
to learn how to use `crisprScore` via `crisprDesign`.
# Installation and getting started
## Software requirements
### OS Requirements
This package is supported for macOS, Linux and Windows machines. Some
functionalities are not supported for Windows machines. Packages were
developed and tested on R version 4.2.1.
## Installation from Bioconductor
`crisprScore` 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("crisprScore")
```
The complete documentation for the package can be found
[here](https://bioconductor.org/packages/devel/bioc/manuals/crisprScore/man/crisprScore.pdf).
## Installation from GitHub
Alternatively, the development version of `crisprScore` and its
dependencies can be installed by typing the following commands inside of
an R session:
``` r
install.packages("devtools")
library(devtools)
install_github("crisprVerse/crisprScoreData")
install_github("crisprVerse/crisprScore")
```
When calling one of the scoring methods for the first time after package
installation, the underlying python module and conda environment will be
automatically downloaded and installed without the need for user
intervention. This may take several minutes, but this is a one-time
installation. the first time after package installation.
Note that RStudio users will need to add the following line to their
`.Rprofile` file in order for `crisprScore` to work properly:
``` r
options(reticulate.useImportHook=FALSE)
```
# Getting started
We load `crisprScore` in the usual way:
``` r
library(crisprScore)
```
The `scoringMethodsInfo` data.frame contains a succinct summary of
scoring methods available in `crisprScore`:
``` r
data(scoringMethodsInfo)
print(scoringMethodsInfo)
```
## method nuclease left right type label len
## 1 ruleset1 SpCas9 -24 5 On-target RuleSet1 30
## 2 azimuth SpCas9 -24 5 On-target Azimuth 30
## 3 deephf SpCas9 -20 2 On-target DeepHF 23
## 4 lindel SpCas9 -33 31 On-target Lindel 65
## 5 mit SpCas9 -20 2 Off-target MIT 23
## 6 cfd SpCas9 -20 2 Off-target CFD 23
## 7 deepcpf1 AsCas12a -4 29 On-target DeepCpf1 34
## 8 enpamgb enAsCas12a -4 29 On-target EnPAMGB 34
## 9 crisprscan SpCas9 -26 8 On-target CRISPRscan 35
## 10 casrxrf CasRx NA NA On-target CasRx-RF NA
## 11 crisprai SpCas9 -19 2 On-target CRISPRai 22
## 12 crisprater SpCas9 -20 -1 On-target CRISPRater 20
## 13 deepspcas9 SpCas9 -24 5 On-target DeepSpCas9 30
## 14 ruleset3 SpCas9 -24 5 On-target RuleSet3 30
Each scoring algorithm requires a different contextual nucleotide
sequence. The `left` and `right` columns indicates how many nucleotides
upstream and downstream of the first nucleotide of the PAM sequence are
needed for input, and the `len` column indicates the total number of
nucleotides needed for input. The `crisprDesign` [(GitHub
link)](https://github.com/Jfortin1/crisprDesign) package provides
user-friendly functionalities to extract and score those sequences
automatically via the `addOnTargetScores` function.
# On-targeting efficiency scores
Predicting on-target cutting efficiency is an extensive area of
research, and we try to provide in `crisprScore` the latest
state-of-the-art algorithms as they become available.
## Cas9 methods
Different algorithms require different input nucleotide sequences to
predict cutting efficiency as illustrated in the figure below.
### Rule Set 1
The Rule Set 1 algorithm is one of the first on-target efficiency
methods developed for the Cas9 nuclease (Doench et al. 2014). It
generates a probability (therefore a score between 0 and 1) that a given
sgRNA will cut at its intended target. 4 nucleotides upstream and 3
nucleotides downstream of the PAM sequence are needed for scoring:
``` r
flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
flank3 <- "TTG" #3bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getRuleSet1Scores(input)
```
The Azimuth score described below is an improvement over Rule Set 1 from
the same lab.
### Azimuth
The Azimuth algorithm is an improved version of the popular Rule Set 2
score for the Cas9 nuclease (Doench et al. 2016). It generates a
probability (therefore a score between 0 and 1) that a given sgRNA will
cut at its intended target. 4 nucleotides upstream and 3 nucleotides
downstream of the PAM sequence are needed for scoring:
``` r
flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
flank3 <- "TTG" #3bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getAzimuthScores(input)
```
### Rule Set 3
The Rule Set 3 is an improvement over Rule Set 1 and Rule Set 2/Azimuth
developed for the SpCas9 nuclease, taking into account the type of
tracrRNAs (DeWeirdt et al. 2022). Two types of tracrRNAs are currently
offered:
GTTTTAGAGCTA-----GAAA-----TAGCAAGTTAAAAT... --> Hsu2013 tracrRNA
GTTTAAGAGCTATGCTGGAAACAGCATAGCAAGTTTAAAT... --> Chen2013 tracrRNA
Similar to Rule Set 1 and Azimuth, the input sequence requires 4
nucleotides upstream of the protospacer sequence, the protospacer
sequence itself (20nt spacersequence and PAM sequence), and 3
nucleotides downstream of the PAM sequence:
``` r
flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
flank3 <- "TTG" #3bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getRuleSet3Scores(input, tracrRNA="Hsu2013")
```
A more involved version of the algorithm takes into account gene context
of the target protospacer sequence (Rule Set 3 Target) and will be soon
implemented in `crisprScore`.
### DeepHF
The DeepHF algorithm is an on-target cutting efficiency prediction
algorithm for several variants of the Cas9 nuclease (Wang et al. 2019)
using a recurrent neural network (RNN) framework. Similar to the Azimuth
score, it generates a probability of cutting at the intended on-target.
The algorithm only needs the protospacer and PAM sequences as inputs:
``` r
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
input <- paste0(spacer, pam)
results <- getDeepHFScores(input)
```
Users can specify for which Cas9 they wish to score sgRNAs by using the
argument `enzyme`: “WT” for Wildtype Cas9 (WT-SpCas9), “HF” for
high-fidelity Cas9 (SpCas9-HF), or “ESP” for enhancedCas9 (eSpCas9). For
wildtype Cas9, users can also specify the promoter used for expressing
sgRNAs using the argument `promoter` (“U6” by default). See
`?getDeepHFScores` for more details.
### DeepSpCas9
The DeepSpCas9 algorithm is an on-target cutting efficiency prediction
algorithm for the SpCas9 nuclease (Kim et al. 2019). Similar to the
Azimuth score, it generates a probability of cutting at the intended
on-target. 4 nucleotides upstream of the protospacer sequence, and 3
nucleotides downstream of the PAM sequence are needed in top of the
protospacer sequence for scoring:
``` r
flank5 <- "ACCT" #4bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
flank3 <- "TTG" #3bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getDeepSpCas9Scores(input)
```
``` r
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
input <- paste0(spacer, pam)
results <- getDeepHFScores(input)
```
Users can specify for which Cas9 they wish to score sgRNAs by using the
argument `enzyme`: “WT” for Wildtype Cas9 (WT-SpCas9), “HF” for
high-fidelity Cas9 (SpCas9-HF), or “ESP” for enhancedCas9 (eSpCas9). For
wildtype Cas9, users can also specify the promoter used for expressing
sgRNAs using the argument `promoter` (“U6” by default). See
`?getDeepHFScores` for more details.
### CRISPRscan
The CRISPRscan algorithm, also known as the Moreno-Mateos score, is an
on-target efficiency method for the SpCas9 nuclease developed for sgRNAs
expressed from a T7 promoter, and trained on zebrafish data
(Moreno-Mateos et al. 2015). It generates a probability (therefore a
score between 0 and 1) that a given sgRNA will cut at its intended
target. 6 nucleotides upstream of the protospacer sequence and 6
nucleotides downstream of the PAM sequence are needed for scoring:
``` r
flank5 <- "ACCTAA" #6bp
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
pam <- "AGG" #3bp
flank3 <- "TTGAAT" #6bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getCRISPRscanScores(input)
```
### CRISPRater
The CRISPRater algorithm is an on-target efficiency method for the
SpCas9 nuclease (Labuhn et al. 2018). It generates a probability
(therefore a score between 0 and 1) that a given sgRNA will cut at its
intended target. Only the 20bp spacer sequence is required.
``` r
spacer <- "ATCGATGCTGATGCTAGATA" #20bp
results <- getCRISPRaterScores(spacer)
```
### CRISPRai
The CRISPRai algorithm was developed by the Weissman lab to score SpCas9
gRNAs for CRISPRa and CRISPRi applications (Horlbeck et al. 2016), for
the human genome. The function `getCrispraiScores` requires several
inputs.
First, it requires a data.frame specifying the genomic coordinates of
the transcription starting sites (TSSs). An example of such a data.frame
is provided in the crisprScore package:
``` r
head(tssExampleCrispri)
```
## tss_id gene_symbol promoter transcripts position strand chr
## 1 A1BG_P1 A1BG P1 ENST00000596924 58347625 - chr19
## 2 A1BG_P2 A1BG P2 ENST00000263100 58353463 - chr19
## 3 KRAS_P1 KRAS P1 ENST00000311936 25250929 - chr12
## 4 SMARCA2_P1 SMARCA2 P1 ENST00000357248 2015347 + chr9
## 5 SMARCA2_P2 SMARCA2 P2 ENST00000382194 2017615 + chr9
## 6 SMARCA2_P3 SMARCA2 P3 ENST00000635133 2158470 + chr9
It also requires a data.frame specifying the genomic coordinates of the
gRNA sequences to score. An example of such a data.frame is provided in
the crisprScore package:
``` r
head(sgrnaExampleCrispri)
```
## grna_id tss_id pam_site strand spacer_19mer
## 1 A1BG_P1_1 A1BG_P1 58347601 - CTCCGGGCGACGTGGAGTG
## 2 A1BG_P1_2 A1BG_P1 58347421 - GGGCACCCAGGAGCGGTAG
## 3 A1BG_P1_3 A1BG_P1 58347624 - TCCACGTCGCCCGGAGCTG
## 4 A1BG_P1_4 A1BG_P1 58347583 - GCAGCGCAGGACGGCATCT
## 5 A1BG_P1_5 A1BG_P1 58347548 - AGCAGCTCGAAGGTGACGT
## 6 A1BG_P2_1 A1BG_P2 58353455 - ATGATGGTCGCGCTCACTC
All columns present in `tssExampleCrispri` and `sgrnaExampleCrispri` are
mandatory for `getCrispraiScores` to work.
Two additional arguments are required: `fastaFile`, to specify the path
of the fasta file of the human reference genome, and `chromatinFiles`,
which is a list of length 3 specifying the path of files containing the
chromatin accessibility data needed for the algorithm in hg38
coordinates. The chromatin files can be downloaded from Zenodo
[here](https://zenodo.org/record/6716721#.YrzCfS-cY4d). The fasta file
for the human genome (hg38) can be downloaded directly from here:
<https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz>
One can obtain the CRISPRai scores using the following command:
``` r
results <- getCrispraiScores(tss_df=tssExampleCrispri,
sgrna_df=sgrnaExampleCrispri,
modality="CRISPRi",
fastaFile="your/path/hg38.fa",
chromatinFiles=list(mnase="path/to/mnaseFile.bw",
dnase="path/to/dnaseFile.bw",
faire="oath/to/faireFile.bw"))
```
The function works identically for CRISPRa applications, with modality
replaced by `CRISPRa`.
## Cas12a methods
Different algorithms require different input nucleotide sequences to
predict cutting efficiency as illustrated in the figure below.
### DeepCpf1 score
The DeepCpf1 algorithm is an on-target cutting efficiency prediction
algorithm for the Cas12a nuclease (Kim et al. 2018) using a
convolutional neural network (CNN) framework. It generates a score
between 0 and 1 to quantify the likelihood of Cas12a to cut for a given
sgRNA. 3 nucleotides upstream and 4 nucleotides downstream of the PAM
sequence are needed for scoring:
``` r
flank5 <- "ACC" #3bp
pam <- "TTTT" #4bp
spacer <- "AATCGATGCTGATGCTAGATATT" #23bp
flank3 <- "AAGT" #4bp
input <- paste0(flank5, pam, spacer, flank3)
results <- getDeepCpf1Scores(input)
```
### enPAM+GB score
The enPAM+GB algorithm is an on-target cutting efficiency prediction
algorithm for the enhanced Cas12a (enCas12a) nuclease (DeWeirdt et al.
2020) using a gradient-booster (GB) model. The enCas12a nuclease as an
extended set of active PAM sequences in comparison to the wildtype Cas12
nuclease (Kleinstiver et al. 2019), and the enPAM+GB algorithm takes PAM
activity into account in the calculation of the final score. It
generates a probability (therefore a score between 0 and 1) of a given
sgRNA to cut at the intended target. 3 nucleotides upstream of the PAM
sequence and 4 nucleotides downstream of the protospacer sequence are
needed for scoring:
``` r
flank5 <- "ACC" #3bp
pam <- "TTTT" #4bp
spacer <- "AATCGATGCTGATGCTAGATATT" #23bp
flank3 <- "AAGT" #4bp
input <- paste0(flank5, pam, spacer, flank3)
results <- getEnPAMGBScores(input)
```
## Cas13d methods
### CasRxRF
The CasRxRF method was developed to characterize on-target efficiency of
the RNA-targeting nuclease RfxCas13d, abbreviated as CasRx (Wessels et
al. 2020).
It requires as an input the mRNA sequence targeted by the gRNAs, and
returns as an output on-target efficiency scores for all gRNAs targeting
the mRNA sequence.
As an example, we predict on-target efficiency for gRNAs targeting the
mRNA sequence stored in the file `test.fa`:
``` r
fasta <- file.path(system.file(package="crisprScore"),
"casrxrf/test.fa")
mrnaSequence <- Biostrings::readDNAStringSet(filepath=fasta
format="fasta",
use.names=TRUE)
results <- getCasRxRFScores(mrnaSequence)
```
Note that the function has a default argument `directRepeat` set to
`aacccctaccaactggtcggggtttgaaac`, specifying the direct repeat used in
the CasRx construct (see (Wessels et al. 2020).) The function also has
an argument `binaries` that specifies the file path of the binaries for
three programs necessary by the CasRxRF algorithm:
- `RNAfold`: available as part of the ViennaRNA package
- `RNAplfold`: available as part of the ViennaRNA package
- `RNAhybrid`: available as part of the RNAhybrid package
Those programs can be installed from their respective websites:
[VienneRNA](https://www.tbi.univie.ac.at/RNA/) and
[RNAhybrid](https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/).
If the argument is `NULL`, the binaries are assumed to be available on
the PATH.
# Off-target specificity scores
For CRISPR knockout systems, off-targeting effects can occur when the
CRISPR nuclease tolerates some levels of imperfect complementarity
between gRNA spacer sequences and protospacer sequences of the targeted
genome. Generally, a greater number of mismatches between spacer and
protospacer sequences decreases the likelihood of cleavage by a
nuclease, but the nature of the nucleotide substitution can module the
likelihood as well. Several off-target specificity scores were developed
to predict the likelihood of a nuclease to cut at an unintended
off-target site given a position-specific set of nucleotide mismatches.
We provide in `crisprScore` two popular off-target specificity scoring
methods for CRISPR/Cas9 knockout systems: the MIT score (Hsu et al.
2013) and the cutting frequency determination (CFD) score (Doench et al.
2016).
## MIT score
The MIT score was an early off-target specificity prediction algorithm
developed for the CRISPR/Cas9 system (Hsu et al. 2013). It predicts the
likelihood that the Cas9 nuclease will cut at an off-target site using
position-specific mismatch tolerance weights. It also takes into
consideration the total number of mismatches, as well as the average
distance between mismatches. However, it does not take into account the
nature of the nucleotide substitutions. The exact formula used to
estimate the cutting likelihood is
$$\text{MIT} = \biggl(\prod_{p \in
M}{w_p}\biggr)\times\frac{1}{\frac{19-d}{19}\times4+1}\times\frac{1}{m^2}$$
where $M$ is the set of positions for which there is a mismatch between
the sgRNA spacer sequence and the off-target sequence, $w_p$ is an
experimentally-derived mismatch tolerance weight at position $p$, $d$ is
the average distance between mismatches, and $m$ is the total number of
mismatches. As the number of mismatches increases, the cutting
likelihood decreases. In addition, off-targets with more adjacent
mismatches will have a lower cutting likelihood.
The `getMITScores` function takes as argument a character vector of 20bp
sequences specifying the spacer sequences of sgRNAs (`spacers`
argument), as well as a vector of 20bp sequences representing the
protospacer sequences of the putative off-targets in the targeted genome
(`protospacers` argument). PAM sequences (`pams`) must also be provided.
If only one spacer sequence is provided, it will reused for all provided
protospacers.
The following code will generate MIT scores for 3 off-targets with
respect to the sgRNA `ATCGATGCTGATGCTAGATA`:
``` r
spacer <- "ATCGATGCTGATGCTAGATA"
protospacers <- c("ACCGATGCTGATGCTAGATA",
"ATCGATGCTGATGCTAGATT",
"ATCGATGCTGATGCTAGATA")
pams <- c("AGG", "AGG", "AGA")
getMITScores(spacers=spacer,
protospacers=protospacers,
pams=pams)
```
## spacer protospacer score
## 1 ATCGATGCTGATGCTAGATA ACCGATGCTGATGCTAGATA 0.31500000
## 2 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATT 1.00000000
## 3 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATA 0.06944444
## CFD score
The CFD off-target specificity prediction algorithm was initially
developed for the CRISPR/Cas9 system, and was shown to be superior to
the MIT score (Doench et al. 2016). Unlike the MIT score,
position-specific mismatch weights vary according to the nature of the
nucleotide substitution (e.g. an A-\>G mismatch at position 15 has a
different weight than an A-\>T mismatch at position 15).
Similar to the `getMITScores` function, the `getCFDScores` function
takes as argument a character vector of 20bp sequences specifying the
spacer sequences of sgRNAs (`spacers` argument), as well as a vector of
20bp sequences representing the protospacer sequences of the putative
off-targets in the targeted genome (`protospacers` argument). `pams`
must also be provided. If only one spacer sequence is provided, it will
be used for all provided protospacers.
The following code will generate CFD scores for 3 off-targets with
respect to the sgRNA `ATCGATGCTGATGCTAGATA`:
``` r
spacer <- "ATCGATGCTGATGCTAGATA"
protospacers <- c("ACCGATGCTGATGCTAGATA",
"ATCGATGCTGATGCTAGATT",
"ATCGATGCTGATGCTAGATA")
pams <- c("AGG", "AGG", "AGA")
getCFDScores(spacers=spacer,
protospacers=protospacers,
pams=pams)
```
## spacer protospacer score
## 1 ATCGATGCTGATGCTAGATA ACCGATGCTGATGCTAGATA 0.85714286
## 2 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATT 0.60000000
## 3 ATCGATGCTGATGCTAGATA ATCGATGCTGATGCTAGATA 0.06944444
# Indel prediction score
## Lindel score (Cas9)
Non-homologous end-joining (NHEJ) plays an important role in
double-strand break (DSB) repair of DNA. Error patterns of NHEJ can be
strongly biased by sequence context, and several studies have shown that
microhomology can be used to predict indels resulting from
CRISPR/Cas9-mediated cleavage. Among other useful metrics, the frequency
of frameshift-causing indels can be estimated for a given sgRNA.
Lindel (Chen et al. 2019) is a logistic regression model that was
trained to use local sequence context to predict the distribution of
mutational outcomes. In `crisprScore`, the function `getLindelScores`
return the proportion of “frameshifting” indels estimated by Lindel. By
chance, assuming a random distribution of indel lengths, frameshifting
proportions should be roughly around 0.66. A Lindel score higher than
0.66 indicates a higher than by chance probability that a sgRNA induces
a frameshift mutation.
The Lindel algorithm requires nucleotide context around the protospacer
sequence; the following full sequence is needed: \[13bp upstream
flanking sequence\]\[23bp protospacer sequence\] \[29bp downstream
flanking sequence\], for a total of 65bp. The function `getLindelScores`
takes as inputs such 65bp sequences:
``` r
flank5 <- "ACCTTTTAATCGA" #13bp
spacer <- "TGCTGATGCTAGATATTAAG" #20bp
pam <- "TGG" #3bp
flank3 <- "CTTTTAATCGATGCTGATGCTAGATATTA" #29bp
input <- paste0(flank5, spacer, pam, flank3)
results <- getLindelScores(input)
```
# License
The project as a whole is covered by the MIT license. The code for all
underlying Python packages, with their original licenses, can be found
in `inst/python`. We made sure that all licenses are compatible with the
MIT license and to indicate changes that we have made to the original
code.
# 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] crisprScore_1.3.2 crisprScoreData_1.3.0 ExperimentHub_2.5.0
## [4] AnnotationHub_3.5.1 BiocFileCache_2.5.0 dbplyr_2.2.1
## [7] BiocGenerics_0.43.4
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.9 lattice_0.20-45
## [3] dir.expiry_1.5.1 png_0.1-7
## [5] Biostrings_2.65.3 assertthat_0.2.1
## [7] digest_0.6.29 utf8_1.2.2
## [9] mime_0.12 R6_2.5.1
## [11] GenomeInfoDb_1.33.7 stats4_4.2.1
## [13] RSQLite_2.2.16 evaluate_0.16
## [15] highr_0.9 httr_1.4.4
## [17] pillar_1.8.1 basilisk_1.9.6
## [19] zlibbioc_1.43.0 rlang_1.0.6
## [21] curl_4.3.2 rstudioapi_0.14
## [23] blob_1.2.3 S4Vectors_0.35.3
## [25] Matrix_1.5-3 reticulate_1.26
## [27] rmarkdown_2.16 stringr_1.4.1
## [29] RCurl_1.98-1.8 bit_4.0.4
## [31] shiny_1.7.2 compiler_4.2.1
## [33] httpuv_1.6.5 xfun_0.32
## [35] pkgconfig_2.0.3 htmltools_0.5.3
## [37] tidyselect_1.1.2 KEGGREST_1.37.3
## [39] tibble_3.1.8 GenomeInfoDbData_1.2.8
## [41] interactiveDisplayBase_1.35.0 IRanges_2.31.2
## [43] randomForest_4.7-1.1 fansi_1.0.3
## [45] crayon_1.5.1 dplyr_1.0.10
## [47] later_1.3.0 basilisk.utils_1.9.3
## [49] bitops_1.0-7 rappdirs_0.3.3
## [51] grid_4.2.1 jsonlite_1.8.0
## [53] xtable_1.8-4 lifecycle_1.0.3
## [55] DBI_1.1.3 magrittr_2.0.3
## [57] cli_3.4.0 stringi_1.7.8
## [59] cachem_1.0.6 XVector_0.37.1
## [61] promises_1.2.0.1 ellipsis_0.3.2
## [63] filelock_1.0.2 generics_0.1.3
## [65] vctrs_0.5.1 tools_4.2.1
## [67] bit64_4.0.5 Biobase_2.57.1
## [69] glue_1.6.2 purrr_0.3.4
## [71] BiocVersion_3.16.0 parallel_4.2.1
## [73] fastmap_1.1.0 yaml_2.3.5
## [75] AnnotationDbi_1.59.1 BiocManager_1.30.18
## [77] memoise_2.0.1 knitr_1.40
# References
<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-lindel" class="csl-entry">
Chen, Wei, Aaron McKenna, Jacob Schreiber, Maximilian Haeussler, Yi Yin,
Vikram Agarwal, William Stafford Noble, and Jay Shendure. 2019.
“Massively Parallel Profiling and Predictive Modeling of the Outcomes of
CRISPR/Cas9-Mediated Double-Strand Break Repair.” *Nucleic Acids
Research* 47 (15): 7989–8003.
</div>
<div id="ref-ruleset3" class="csl-entry">
DeWeirdt, Peter C, Abby V McGee, Fengyi Zheng, Ifunanya Nwolah, Mudra
Hegde, and John G Doench. 2022. “Accounting for Small Variations in the
tracrRNA Sequence Improves sgRNA Activity Predictions for CRISPR
Screening.” *bioRxiv*. <https://doi.org/10.1101/2022.06.27.497780>.
</div>
<div id="ref-enpamgb" class="csl-entry">
DeWeirdt, Peter C, Kendall R Sanson, Annabel K Sangree, Mudra Hegde,
Ruth E Hanna, Marissa N Feeley, Audrey L Griffith, et al. 2020.
“Optimization of AsCas12a for Combinatorial Genetic Screens in Human
Cells.” *Nature Biotechnology*, 1–11.
</div>
<div id="ref-azimuth" class="csl-entry">
Doench, John G, Nicolo Fusi, Meagan Sullender, Mudra Hegde, Emma W
Vaimberg, Katherine F Donovan, Ian Smith, et al. 2016. “Optimized sgRNA
Design to Maximize Activity and Minimize Off-Target Effects of
CRISPR-Cas9.” *Nature Biotechnology* 34 (2): 184.
</div>
<div id="ref-ruleset1" class="csl-entry">
Doench, John G, Ella Hartenian, Daniel B Graham, Zuzana Tothova, Mudra
Hegde, Ian Smith, Meagan Sullender, Benjamin L Ebert, Ramnik J Xavier,
and David E Root. 2014. “Rational Design of Highly Active sgRNAs for
CRISPR-Cas9–Mediated Gene Inactivation.” *Nature Biotechnology* 32 (12):
1262–67.
</div>
<div id="ref-crisprai" class="csl-entry">
Horlbeck, Max A, Luke A Gilbert, Jacqueline E Villalta, Britt Adamson,
Ryan A Pak, Yuwen Chen, Alexander P Fields, et al. 2016. “Compact and
Highly Active Next-Generation Libraries for CRISPR-Mediated Gene
Repression and Activation.” *Elife* 5.
</div>
<div id="ref-mit" class="csl-entry">
Hsu, Patrick D, David A Scott, Joshua A Weinstein, F Ann Ran, Silvana
Konermann, Vineeta Agarwala, Yinqing Li, et al. 2013. “DNA Targeting
Specificity of RNA-Guided Cas9 Nucleases.” *Nature Biotechnology* 31
(9): 827.
</div>
<div id="ref-deepspcas9" class="csl-entry">
Kim, Hui Kwon, Younggwang Kim, Sungtae Lee, Seonwoo Min, Jung Yoon Bae,
Jae Woo Choi, Jinman Park, Dongmin Jung, Sungroh Yoon, and Hyongbum
Henry Kim. 2019. “SpCas9 Activity Prediction by DeepSpCas9, a Deep
Learning–Based Model with High Generalization Performance.” *Science
Advances* 5 (11): eaax9249.
</div>
<div id="ref-deepcpf1" class="csl-entry">
Kim, Hui Kwon, Seonwoo Min, Myungjae Song, Soobin Jung, Jae Woo Choi,
Younggwang Kim, Sangeun Lee, Sungroh Yoon, and Hyongbum Henry Kim. 2018.
“Deep Learning Improves Prediction of CRISPR–Cpf1 Guide RNA Activity.”
*Nature Biotechnology* 36 (3): 239.
</div>
<div id="ref-encas12a" class="csl-entry">
Kleinstiver, Benjamin P, Alexander A Sousa, Russell T Walton, Y Esther
Tak, Jonathan Y Hsu, Kendell Clement, Moira M Welch, et al. 2019.
“Engineered CRISPR–Cas12a Variants with Increased Activities and
Improved Targeting Ranges for Gene, Epigenetic and Base Editing.”
*Nature Biotechnology* 37 (3): 276–82.
</div>
<div id="ref-crisprater" class="csl-entry">
Labuhn, Maurice, Felix F Adams, Michelle Ng, Sabine Knoess, Axel
Schambach, Emmanuelle M Charpentier, Adrian Schwarzer, Juan L Mateo,
Jan-Henning Klusmann, and Dirk Heckl. 2018. “Refined sgRNA Efficacy
Prediction Improves Large-and Small-Scale CRISPR–Cas9 Applications.”
*Nucleic Acids Research* 46 (3): 1375–85.
</div>
<div id="ref-crisprscan" class="csl-entry">
Moreno-Mateos, Miguel A, Charles E Vejnar, Jean-Denis Beaudoin, Juan P
Fernandez, Emily K Mis, Mustafa K Khokha, and Antonio J Giraldez. 2015.
“CRISPRscan: Designing Highly Efficient sgRNAs for CRISPR-Cas9 Targeting
in Vivo.” *Nature Methods* 12 (10): 982–88.
</div>
<div id="ref-deepcas9" class="csl-entry">
Wang, Daqi, Chengdong Zhang, Bei Wang, Bin Li, Qiang Wang, Dong Liu,
Hongyan Wang, et al. 2019. “Optimized CRISPR Guide RNA Design for Two
High-Fidelity Cas9 Variants by Deep Learning.” *Nature Communications*
10 (1): 1–14.
</div>
<div id="ref-casrxrf" class="csl-entry">
Wessels, Hans-Hermann, Alejandro Méndez-Mancilla, Xinyi Guo, Mateusz
Legut, Zharko Daniloski, and Neville E Sanjana. 2020. “Massively
Parallel Cas13 Screens Reveal Principles for Guide RNA Design.” *Nature
Biotechnology* 38 (6): 722–27.
</div>
</div>
