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\usepackage{hyperref}
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\author{Lihua Julie Zhu, Michael Brodsky}
\begin{document}
\SweaveOpts{concordance=TRUE}
\title{CRISPRseek user's guide}

\maketitle

\tableofcontents
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Introduction}
CRISPR-Cas9 nucleases and their derivatives have rapidly become widely used 
tools for both genome modification and regulation of gene expression. They can 
create genetic changes with high efficiency in human stem cells, in model 
organisms such as mice and Drosophila and in a wide variety of other organisms. 
CRISPR-Cas9 nucleases create double strand DNA breaks that can facilitate a 
variety of genome modifications including short insertions and/or deletions 
(indels) or specific sequence changes introduced by homology directed repair 
with a DNA donor molecule. The high activity and relative ease of construction 
has made CRISPR-Cas9 nucleases a popular replacement for related technologies 
such as zinc finger nucleases and TALENs. Derivatives of CRISPR-Cas9 complexes 
include nickases, which only cleave one DNA strand, and gene expression 
regulators, which lack any DNA cleavage activity but can increase or decrease 
gene transcription. CRISPR-Cas9 nucleases are composed of an RNA-protein 
complex that can target a variable sequence (guide RNA, abbreviated as "gRNA")
that is directly adjacent to a constant motif (the "PAM" sequence). In the 
most widely use version from the species Streptococcus pyogenes, the gRNA 
is composed of a variable region of 20 bases and the preferred PAM sequence 
is an adjacent 3 base sequence of NGG (or NAG with lower activity). One 
potential limitation for CRISPR-Cas9 nucleases is that they can cleave at 
some sequences that do not precisely match the sequence targeted by the 
gRNA sequence. Thus, an important consideration for the design and application
of CRISPR-Cas9 nucleases is the identification of gRNA regions with low 
rates of off-target cleavage. 

Several computational analyses can assist with the construction and application 
of CRISPR-Cas9 nucleases with high on-target and low off-target cleavage. 
First, gRNA sequences can be evaluated for possible off-target sequences in 
the target genome. Second, sequences flanking possible off-target sequences 
can be reported to assist in the experimental analysis of off-target cleavage 
and to determine if these sequences are within critical regions for gene 
function such as exons. Third, specific arrangements of target sequences can 
be selected; one alternate approach to lower off-target rates is to introduce 
pairs of CRISPR-Cas9 nickases, which will only create double strand DNA breaks 
at genomic regions where the two sites have the proper spacing and orientation. 
Finally, in some applications, it is useful to use restriction enzyme sequences 
that overlap CRISPR-Cas9 target sites in order to monitor cleavage events.
We developed \Biocpkg{CRISPRseek} package that identifies candidate CRISPR-Cas9 
target sequences within a given input sequence using a variety of 
experimentally useful constraints and also reports and ranks potential 
off-target sequences for each recovered target sequence. CRISPRseek will 
automatically find potential target sequences that are/are not present as pairs 
that can be used as double nickases or that have/don't have overlapping 
restriction enzyme cut site(s). It will then search genome-wide for off-targets 
with a user defined maximum number of mismatches, calculate the score of each 
off-target based on mismatches in the off-target and a penalty weight 
matrix, filter off-targets with user-defined criteria, and annotate off-targets 
with flanking sequences, and whether located in exon or not. Several reports 
are generated including a summary report with gRNAs ranked by total topN 
off-target score, restriction enzyme cut sites and  possible paired gRNAs. 
Detailed paired gRNAs information, restriction enzyme cut sites, and 
off-target sequences and scores are stored in separate files in the output 
directory specified by the user. In total, four tab delimited files are 
generated in the output directory: OfftargetAnalysis.xls (off-target details), 
Summary.xls (gRNA summary), REcutDetails.xls (restriction enzyme cut sites 
of each gRNA), and pairedgRNAs.xls (potential paired gRNAs). These 
reports provide a comprehensive set of information to identify, select and 
utilize Streptococcus pyogenes CRISPR-Cas9 nucleases and their derivatives. 
The package can also be readily modified to accept different gRNA and PAM 
sequence requirements for CRISPR-Cas9 complexes from other bacterial species 
that can be used to target alternative genomic sequences. The package can also 
be modified to incorporate improved weight matrices or scoring.method for 
scoring off-target sequences as new experimental and computational results
become available for CRISPR-Cas9 nucleases for Streptococcus pyogenes and 
other species.
\section{Examples of using CRISPRseek}
In this guide, we will illustrate five different gRNA search scenarios with a 
human sequence. First load \Biocpkg{CRISPRseek}, 
\Biocannopkg{BSgenome.Hsapiens.UCSC.hg19} and 
\Biocannopkg{TxDb.Hsapiens.UCSC.hg19.knownGene}. Then specify the sequence 
file path as inputFilePath, a fasta/fastq file containing a genomic sequence, 
restriction enzyme pattern file as REpatternFile and output directory as 
outputDir. Once the analysis is done, analysis results will be saved in the 
output directory.

To find BSgenome of other species, please refer to available.genomes in the
\Biocpkg{BSgenome} package. For example, 
\Biocannopkg{BSgenome.Hsapiens.UCSC.hg19} for hg19, 
\Biocannopkg{BSgenome.Mmusculus.UCSC.mm10} for mm10, 
\Biocannopkg{BSgenome.Celegans.UCSC.ce6} for ce6, 
\Biocannopkg{BSgenome.Rnorvegicus.UCSC.rn5} for rn5, \linebreak 
\Biocannopkg{BSgenome.Drerio.UCSC.danRer7} for Zv9, 
and \Biocannopkg{BSgenome.Dmelanogaster.UCSC.dm3} for dm3

To create and use TxDb objects, please refer to the 
\Biocpkg{GenomicFeatures} package. For a list of existing TxDb objects, 
please search for annotation package starting with Txdb at \linebreak 
http://www.bioconductor.org/packages/release/BiocViews.html, 
such as \Biocannopkg{TxDb.Rnorvegicus.UCSC.rn5.refGene} for rat, 
\Biocannopkg{TxDb.Mmusculus.UCSC.mm10.knownGene} for mouse, 
\Biocannopkg{TxDb.Hsapiens.UCSC.hg19.knownGene} for human, 
\Biocannopkg{TxDb.Dmelanogaster.UCSC.dm3.ensGene} for Drosophila and 
\Biocannopkg{TxDb.Celegans.UCSC.ce6.ensGene} for C.elegans

\Biocpkg{org.Hs.eg.db} is the gene ID mapping package for human.
For a list of existing OrgDb packages, 
please search for OrgDb at \linebreak 
http://www.bioconductor.org/packages/release/BiocViews.html, such as
\Biocannopkg{org.Rn.eg.db} for rat
\Biocannopkg{org.Mm.eg.db} for mouse 
\Biocannopkg{org.Dm.eg.db} for Drosophila
\Biocannopkg{org.Ce.eg.db} for C.elegans

\begin{scriptsize}
<<>>=
library(CRISPRseek)
library(BSgenome.Hsapiens.UCSC.hg19)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
outputDir <- getwd()
inputFilePath <- system.file('extdata', 'inputseq.fa', package = 'CRISPRseek')
REpatternFile <- system.file('extdata', 'NEBenzymes.fa', package = 'CRISPRseek')
@
\end{scriptsize}


\subsection{Scenario 1: Finding paired gRNAs with restriction enzyme cut 
site(s)}
Paired gRNAs in proper spacing and orientation give more specificity and 
gRNAs overlap with restriction enzyme cut sites facilitates cleavage 
monitoring. Calling the function \Rfunction{offTargetAnalysis} with 
findPairedgRNAOnly=TRUE and findgRNAsWithREcutOnly=TRUE results in 
searching, scoring and annotating gRNAs that are in paired configuration 
and at least one of the pairs overlap a restriction enzyme cut site. To be 
considered as a pair, gap between forward gRNA and the corresponding reverse 
gRNA needs to be (min.gap, max.gap) inclusive and the reverse gRNA must 
sit before the forward gRNA. The default (min.gap, max.gap) is (0,20). 
Please note that chromToSearch is set to chrX here for speed purpose, usually 
you would set it to all, which is the default. In order for a gRNA to be 
considered overlap with restriction enzyme cut site, the enzyme cut pattern 
must overlap with one of the gRNA positions specified in 
overlap.gRNA.positions, default position 17 and 18. Please note that 
max.mismatch allowed for off-target finding is set to 4 by default, set it 
to a larger number will significantly slow down the search. org.Hs.egSYMBOL is 
entrezID to gene symbol mapping in org.Hs.eg.db package for human. For detailed 
parameter settings using function \Rfunction{offTargetAnalysis}, please type 
help(offTargetAnalysis)

\begin{scriptsize}
<<>>=
results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = TRUE, 
REpatternFile = REpatternFile, findPairedgRNAOnly = TRUE, 
BSgenomeName = Hsapiens, chromToSearch ="chrX", min.gap = 0, max.gap = 20,
txdb = TxDb.Hsapiens.UCSC.hg19.knownGene, orgAnn = org.Hs.egSYMBOL,
max.mismatch = 0,overlap.gRNA.positions = c(17, 18), 
outputDir = outputDir,overwrite = TRUE)
@
\end{scriptsize}

\subsection{Scenario 2: Finding paired gRNAs with/without restriction enzyme 
cut site(s)}
Calling the function \Rfunction{offTargetAnalysis} with 
findPairedgRNAOnly = TRUE and findgRNAsWithREcutOnly = FALSE results in 
searching, scoring and annotating gRNAs that are in paired configuration 
without requiring overlap any restriction enzyme cut site. The gRNAs will 
be annotated with restriction enzyme cut sites for users to review later.

\begin{scriptsize}
<<>>=
results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = FALSE,
REpatternFile = REpatternFile,findPairedgRNAOnly = TRUE, 
BSgenomeName = Hsapiens, chromToSearch = "chrX",
txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
orgAnn = org.Hs.egSYMBOL, 
max.mismatch = 1, outputDir = outputDir, overwrite = TRUE)
@
\end{scriptsize}

\subsection{Scenario 3: Finding all gRNAs with restriction enzyme cut site(s)}
Calling the function \Rfunction{offTargetAnalysis} with 
findPairedgRNAOnly=FALSE and findgRNAsWithREcutOnly = TRUE results in 
searching, scoring and annotating all gRNAs (paired and not paired) overlap 
restriction enzyme cut site(s) and off-targets. The gRNAs will be annotated 
with paired information for users to review later.

\begin{scriptsize}
<<>>=
results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = TRUE,
REpatternFile = REpatternFile, findPairedgRNAOnly = FALSE, 
BSgenomeName = Hsapiens, chromToSearch = "chrX", 
txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
orgAnn = org.Hs.egSYMBOL,
max.mismatch = 1, outputDir = outputDir, overwrite = TRUE)
@
\end{scriptsize}

\subsection{Scenario 4: Finding all gRNAs}
Calling the function \Rfunction{offTargetAnalysis} with 
findPairedgRNAOnly = FALSE and findgRNAsWithREcutOnly = FALSE results in 
searching, scoring and annotating all gRNAs and off-targets. The gRNAs will 
be annotated with paired information and restriction enzyme cut sites for users 
to review later.  Please note that this search will be the slowest among all 
type of searches aforementioned.

\begin{scriptsize}
<<>>=
results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = FALSE,
REpatternFile = REpatternFile,findPairedgRNAOnly = FALSE, 
BSgenomeName = Hsapiens, chromToSearch = "chrX", 
txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
orgAnn = org.Hs.egSYMBOL,
max.mismatch = 1, outputDir = outputDir, overwrite = TRUE)
@
\end{scriptsize}

\subsection{Scenario 5: Target and off-target analysis for user specified 
gRNAs}
Calling the function \Rfunction{offTargetAnalysis} with findgRNAs = FALSE 
results in target and off-target searching, scoring and annotating for the 
input gRNAs. The gRNAs will be annotated with restriction enzyme cut sites 
for users to review later. However, paired information will not be available. 

\begin{scriptsize}
<<>>=
gRNAFilePath <- system.file('extdata', 'testHsap_GATA1_ex2_gRNA1.fa',
package = 'CRISPRseek')
results <- offTargetAnalysis(inputFilePath = gRNAFilePath, 
findgRNAsWithREcutOnly = FALSE, REpatternFile = REpatternFile,
findPairedgRNAOnly = FALSE, findgRNAs = FALSE,
BSgenomeName = Hsapiens, chromToSearch = 'chrX', 
txdb = TxDb.Hsapiens.UCSC.hg19.knownGene, 
orgAnn = org.Hs.egSYMBOL,
max.mismatch = 1, outputDir = outputDir, overwrite = TRUE)
@
\end{scriptsize}

\subsection{Scenario 6. Quick gRNA finding  without target or off-target
analysis}
Calling the function \Rfunction{offTargetAnalysis} with chromToSearch = "" 
results in quick gRNA search without performing on-target and off-target
analysis. Parameters findgRNAsWithREcutOnly and findPairedgRNAOnly can
be tuned to indicate whether searching for gRNAs overlap restriction enzyme
cut sites or not, and whether searching for gRNAs in paired configuration
or not. 
\begin{scriptsize}
<<>>=
results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = TRUE,
REpatternFile = REpatternFile,findPairedgRNAOnly = TRUE,
chromToSearch = "", outputDir = outputDir, overwrite = TRUE)
@
\end{scriptsize}

\subsection{Scenario 7. Quick gRNA finding  with gRNA efficacy prediction 
}
Calling the function \Rfunction{offTargetAnalysis} with max.mismatch = 0 
results in quick gRNA search with gRNA efficacy prediction 
without off-target analysis. Parameter rule.set can be set to CRISPRscan published in 2015 or
Root\_RuleSet2\_2016 to calculate efficacy using the rule set 2 published in 2016.
By default, gRNA efficacy is predicted using the rule set 1 published in 2014.
To use the rule set 2, first install python 2.7, then install the python 
packages: scikit-learn 0.16.1, pickle, pandas, numpy nd scipy. In a R session, type the following script to use python 2.7 since rule set 2 is implemented in python 2.7.
Sys.setenv(PATH = paste("~/anaconda2/bin", Sys.getenv("PATH"), sep=":")) 
system("python --version") should output Python 2.7.15.
In addition, parameters findgRNAsWithREcutOnly and findPairedgRNAOnly 
can be tuned to indicate whether searching for gRNAs
overlap restriction enzyme cut sites or not, and whether searching for 
gRNAs in paired configuration or not.
\begin{scriptsize}
<<>>=
results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = TRUE,
annotateExon = FALSE,findPairedgRNAOnly = TRUE, chromToSearch = "chrX",
max.mismatch = 0, BSgenomeName = Hsapiens, outputDir = outputDir, overwrite = TRUE)
@
\end{scriptsize}

Here is an example on how to use CRISPRscan to calculate efficacy. Please remember 
to reset baseBeforegRNA, baseAfterPAM, rule.set, and featureWeightMatrixFile.
\begin{scriptsize}
<<>>=
results <- offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly = TRUE,
    annotateExon = FALSE,findPairedgRNAOnly = TRUE, chromToSearch = "chrX",
    max.mismatch = 0, BSgenomeName = Hsapiens, 
    rule.set = "CRISPRscan",
    baseBeforegRNA = 6,
    baseAfterPAM = 6,
    featureWeightMatrixFile =  system.file("extdata", "Morenos-Mateo.csv",
            package = "CRISPRseek"),
    outputDir = outputDir, overwrite = TRUE)
@
\end{scriptsize}

\subsection{Scenario 8. Find potential gRNAs preferentially targeting one of two
alleles without running time-consuming off-target analysis on all possible gRNAs.}
Below is an example to search for all gRNAs that target at least one of the 
alleles. Two files are provided containing sequences that differ by a single 
nucleotide polymorphism (SNP). The results are saved in file 
scoresFor2InputSequences.xls in outputDir directory. 
\begin{scriptsize}
<<>>=
inputFile1Path <- system.file("extdata", "rs362331C.fa",  package = "CRISPRseek")
inputFile2Path <- system.file("extdata", "rs362331T.fa",  package = "CRISPRseek") 
REpatternFile <- system.file("extdata", "NEBenzymes.fa", package = "CRISPRseek")
seqs <- compare2Sequences(inputFile1Path, inputFile2Path, 
    outputDir = outputDir , REpatternFile = REpatternFile, 
    overwrite = TRUE)
seqs
@
\end{scriptsize}
rs362331C.fa and rs362331T.fa are the names of the two input files. The output 
file will list all of the possible gRNA sequences for each of the two input 
sequences and provide a cleavage score for each of the two input sequences. 
To preferentially target one allele, select gRNA sequences that have the lowest 
score for the other allele. Selected gRNAs can then be examined for off-target 
sequences as described in Step 6.

\subsection{Scenario 9. gRNA search and offTarget analysis of super long input 
sequence (longer than 200kb)
}
Calling the function \Rfunction{offTargetAnalysis} with annotatePaired = FALSE,
enable.multicore = TRUE and set n.cores.max will improve the performance. 
We also suggest split the super long sequence into smaller chunks and 
perform offTarget analysis for each subsequence separately 
(Thank Alex Williams for sharing this use case at
https://support.bioconductor.org/p/72994/). In addition, please remember to use
repeat masked sequence as input.
\begin{scriptsize}
<<>>=
results <- offTargetAnalysis(inputFilePath, annotatePaired = FALSE, 
    chromToSearch = "chrX",
    enable.multicore = TRUE, n.cores.max = 10, annotateExon = FALSE,
    max.mismatch = 0, BSgenomeName = Hsapiens, 
    outputDir = outputDir, overwrite = TRUE)
@
\end{scriptsize}
\subsection{Scenario 10. Output cutting frerquency determiniation (CFD)
 score for offtargets}
Calling the function \Rfunction{offTargetAnalysis} with scoring.method
set to CFDscore and PAM.pattern as NNG or NGA  will output CFD score 
using the algorithm by Doench et al., 2016, which models the 
effects of both mismatch position and mimatch type on cutting
frequency. By default, scoring.method is set to Hsu-Zhang,
 which only models the effect of mismatch position.
\begin{scriptsize}
<<>>=
results <- offTargetAnalysis(inputFilePath, annotatePaired = FALSE,
    scoring.method = "CFDscore",
    PAM.pattern  = "NNG$|NGN$",
    chromToSearch = "chrX",
    annotateExon = FALSE,
    max.mismatch = 2, BSgenomeName = Hsapiens,
    outputDir = outputDir, overwrite = TRUE)
@
\end{scriptsize}
\subsection{Scenario 11. Design gRNAs for base editing systems}
Cytosine or adenine base editors (CBEs or ABEs) can introduce 
specific DNA C-to-T or A-to-G alterations.
Calling the function \Rfunction{offTargetAnalysis} with 
baseEditing set to TRUE, and targetBase, editingWindow and 
editingWindow.offtarget set appropriately. By default, targetBase
is set to C, editingWindow to 4 to 8 and editingWindow.offtargets to 
4 to 8 for the CBE system developed in the Liu Laboratory.
\begin{scriptsize}
<<>>=
offTargetAnalysis(inputFilePath, findgRNAsWithREcutOnly =  FALSE,
                 findPairedgRNAOnly = FALSE,
                 annotatePaired = FALSE,
                 BSgenomeName = Hsapiens, chromToSearch = "chrX",
                 txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
                 orgAnn = org.Hs.egSYMBOL, max.mismatch = 4,
                 outputDir = outputDir, overwrite = TRUE, PAM.location = "5prime",
                 PAM = "TGT", PAM.pattern = "^T[A|G]N", allowed.mismatch.PAM = 2,
                 subPAM.position = c(1,2), baseEditing = TRUE,
                 editingWindow = 10:20, targetBase = "A")
@
\end{scriptsize}
\subsection{Scenario 12. Design gRNAs and pegRNAs for the Prime Editor}
Recently, the Liu Laboratory developed the Prime Editor (PE), 
which is more versatile and flexible with high efficacy and without 
the need to make a DSB or providing donor template. It can be used to 
make all possible 12 single base changes, 1-44 bp insertions,
or 1-80 bp deletions. This editing system can be programmed to 
correct about 89 percent of human pathagenic variants. To design 
gRNAs and pegRNAs for PE, you need to set primeEditing to TRUE, set 
seq.length.changed, bp.after.target.end, PBS.length (default 13),
RT.template.length (default 8 to 28), min.gap (default 40),
max.gap (default 90), findPairedgRNAOnly (needs to be set to TRUE),
paired.orientation (needs to be set to "PAMin"), target.start, target.end,
and correct.seq accordingly. 

Please type help(offTargetAnalysis) for detailed
description of these parameters and examples for designing PE.

\subsection{Scenario 13. Predict indels and their frequencies for Cas9 genome editing systems}
Calling the function \Rfunction{offTargetAnalysis} with predIndelFreq set to TRUE.
The first command outputs the gRNA name,gRNAPlusPAM sequence,
offtarget sequence, gene name, predicted offtarget score, genomic location,
and predicted efficacy. The second command outputs the indel locations and frequencies for the
first target site. The summary file contains Shannon entropy and the fraction of frameshift of
mutational outcomes for each target. Currently, only the Lindel method by Chen et al., 2019
is available. Please type help(predictRelativeFreqIndels) for more details.

\begin{scriptsize}
<<>>=
resultsIndelF <- offTargetAnalysis(
                 inputFilePath, findgRNAsWithREcutOnly =  FALSE,
                 findPairedgRNAOnly = FALSE,
                 annotatePaired = FALSE,
                 BSgenomeName = Hsapiens, chromToSearch = "chrX",
                 txdb = TxDb.Hsapiens.UCSC.hg19.knownGene,
                 orgAnn = org.Hs.egSYMBOL, max.mismatch = 1,
                 outputDir = outputDir, overwrite = TRUE,
                 scoring.method = "CFDscore",
                 PAM.pattern  = "NNG$|NGN$",
                 predIndelFreq = TRUE)

 if(exists("resultsIndelF")) 
 {
     print(head(resultsIndelF$indelFreq[[1]]))
     mapply(write.table, resultsIndelF$indelFreq, file=paste0(names(resultsIndelF$indelFreq), '.xls'), 
        sep = "\t", row.names = FALSE)
  }
@
\end{scriptsize}

\section{References}
\begin{thebibliography}{5}
\bibitem[Lihua Julie Zhu, 2015]{Zhu, 2015} Lihua Julie Zhu. Overview of guide 
RNA design tools for CRISPR-Cas9 genome editing technology. Frontiers in Biology.
August 2015, Volume 10, Issue 4, pp 289-296
\bibitem[Mali P. et al., 2013]{Mali et al., 2013} Mali P. et al., CAS9 
transcriptional activators for target specificity screening and paired nickases 
for cooperative genome engineering. Nat Biotechnol. 2013. 31(9):833-8
\bibitem[Hsu, P.D et al., 2013]{Hsu et al., 2013} Hsu, P.D. et al., DNA 
targeting specificity of rNA-guided Cas9 nucleases. Nat Biotechnol. 
2013. 31:827-834.
\bibitem[Doench JG, Hartenian E, Graham DB, Tothova Z, Hegde M, Smith I, 
Sullender M, Ebert BL, Xavier RJ, Root DE]{Doench et al., 2014} 
Doench et al., 2014 Rational design of highly active sgRNAs for 
CRISPR-Cas9-mediated gene inactivation. 
Nat Biotechnol. 2014 Sep 3. doi: 10.1038 nbt.3026
\bibitem[Lihua Julie Zhu, Benjamin R. Holmes, Neil Aronin and Michael Brodsky]
{Zhu et al., 2014} Lihua Julie Zhu, Benjamin R. Holmes, Neil Aronin and Michael
Brodsky. CRISPRseek: a Bioconductor package to identify target-specific guide
RNAs for CRISPR-Cas9 genome-editing systems. Plos One Sept 23rd 2014
\bibitem[Moreno-Mateos, M., Vejnar, C., Beaudoin, J. et al., 2015]
{Moreno-Mateos, M., Vejnar, C., Beaudoin, J. et al., 2015}
Moreno-Mateos, M., Vejnar, C., Beaudoin, J. et al. CRISPRscan: 
designing highly efficient sgRNAs for CRISPR-Cas9 targeting 
in vivo. Nat Methods 12, 982–988 (2015) doi:10.1038/nmeth.3543
\bibitem[Doench JG et al., 2016]{Doench et al., 2016} Doench et al. Optimized sgRNA
design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol.
2016 Jan 18. doi:10.1038/nbt.3437
\bibitem[Komor et al., 2016]{Komor et al., 2016}Komor, A. C., Kim, Y. B., Packer, M. S., 
Zuris, J. A. and Liu, D. R. Programmable editing of a target base in 
genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).
\bibitem[Gaudelli et al., 2017]{Gaudelli et al., 2017}Gaudelli, N. M. et al. 
Programmable base editing of A or T to G or C in genomic DNA without 
DNA cleavage. Nature 551, 464–471 (2017).
\bibitem[Kim et al., 2018]{Kim et al., 2018}Kim et al., Deep learning improves prediction of CRISPR–Cpf1
guide RNA activityNat Biotechnol 36, 239–241 (2018).
https://doi.org/10.1038/nbt.4061
\bibitem[Chen et al., 2019]{Che et al., 2019}Wei Chen, Aaron McKenna, Jacob Schreiber et al.,
Massively parallel profiling and predictive modeling of the 
outcomes of CRISPR/Cas9-mediated double-strand break repair, 
Nucleic Acids Research, Volume 47, Issue 15, 05 September 2019, 
Pages 7989–8003, https://doi.org/10.1093/nar/gkz487.
\end{thebibliography}
\section{Session Info}
<<>>=
sessionInfo()
@
\end{document}
