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Introduction to crisprDesign
Authors: Jean-Philippe Fortin, Aaron Lun, Luke Hoberecht
Date: July 1, 2022
Introduction
crisprDesignis the core package of the crisprVerse ecosystem, and plays the role of a one-stop shop for designing and annotating CRISPR guide RNA (gRNA) sequences. This includes the characterization of on-targets and off-targets using different aligners, on- and off-target scoring, gene context annotation, SNP annotation, sequence feature characterization, repeat annotation, and many more.The software was developed to be as applicable and generalizable as possible.
It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout (CRISPRko), CRISPR activation (CRISPRa), CRISPR interference (CRISPRi), CRISPR base editing (CRISPRbe), and CRISPR knockdown (CRISPRkd) (see Kampmann (2018) for a review of CRISPR modalities).
It utilizes the
crisprBasepackage to enable gRNA design for any CRISPR nuclease and base editor via theCrisprNucleaseandBaseEditorclasses, respectively. Nucleases that are commonly used in the field are provided, including DNA-targeting nucleases (e.g. SpCas9, AsCas12a) and RNA-targeting nucleases (e.g. CasRx (RfxCas13d)).crisprDesignis fully developed to work with the genome of any organism, and can also be used to design gRNAs targeting custom DNA sequences.Finally, more specialized gRNA design functionalities are also available, including design for optical pooled screening (OPS), paired gRNA design, and gRNA filtering and ranking functionalities.
This vignette is meant to be an overview of the main features included in the package, using toy examples for the sake of time (the vignette has to compile within a few minutes, as required by Bioconductor). For detailed and comprehensive tutorials, please visit our crisprVerse tutorials page.
Installation
crisprDesigncan be installed from from the Bioconductor devel branch using the following commands in a fresh R session:Users interested in contributing to
crisprDesignmight want to look at the following CRISPR-related package dependencies:bowtieBWAYou can contribute to the package by submitting pull requests to our GitHub repo.
The complete documentation for the package can be found here.
Terminology
CRISPR nucleases are examples of RNA-guided endonucleases. They require two binding components for cleavage. First, the nuclease needs to recognize a constant nucleotide motif in the target DNA called the protospacer adjacent motif (PAM) sequence. Second, the gRNA, which guides the nuclease to the target sequence, needs to bind to a complementary sequence adjacent to the PAM sequence, called the protospacer sequence. The latter can be thought of as a variable binding motif that can be specified by designing corresponding gRNA sequences.
The spacer sequence is used in the gRNA construct to guide the CRISPR nuclease to the target protospacer sequence in the host genome.
For DNA-targeting nucleases, the nucleotide sequence of the spacer and protospacer are identical. For RNA-targeting nucleases, they are the reverse complement of each other.
While a gRNA spacer sequence may not always uniquely target the host genome (i.e. it may map to multiple protospacers in the host genome), we can, for a given reference genome, uniquely identify a protospacer sequence with a combination of 3 attributes:
chr: chromosome namestrand: forward (+) or reverse (-)pam_site: genomic coordinate of the first nucleotide of the nuclease-specific PAM sequence (e.g. for SpCas9, the “N” in the NGG PAM sequence; for AsCas12a, the first “T” of the TTTV PAM sequence)For CRISPRko, we use an additional genomic coordinate, called
cut_site, to represent where the double-stranded break (DSB) occurs. For SpCas9, the cut site (blunt-ended dsDNA break) is located 4nt upstream of the pam_site (PAM-proximal editing). For AsCas12a, the 5nt 5’ overhang dsDNA break will cause a cut 19nt after the PAM sequence on the targeted strand, and 23nt after the PAM sequence on the opposite strand (PAM-distal editing).CRISPRko design
We will illustrate the main functionalities of
crisprDesignby performing a common task: designing gRNAs to knock out a coding gene. In our example, we will design gRNAs for the wildtype SpCas9 nuclease, with spacers having a length of 20nt.Nuclease specification
The
crisprBasepackage provides functionalities to create objects that store information about CRISPR nucleases, and functions to interact with those objects (see thecrisprBasevignette). It also provides commonly-used CRISPR nucleases. Let’s look at theSpCas9nuclease object:The three motifs (NGG, NAG and NGA) represent the recognized PAM sequences by SpCas9, and the weights indicate a recognition score. The canonical PAM sequence NGG is fully recognized (weight of 1), while the two non-canonical PAM sequences NAG and NGA are much less tolerated.
The spacer sequence is located on the 5-prime end with respect to the PAM sequence, and the default spacer sequence length is 20 nucleotides. If necessary, we can change the spacer length using the function
crisprBase::spacerLength. Let’s see what the protospacer construct looks like by usingprototypeSequence:Target DNA specification
As an example, we will design gRNAs that knockout the human gene IQSEC3 by finding all protospacer sequences located in the coding region (CDS) of IQSEC3.
To do so, we need to create a
GRangesobject that defines the genomic coordinates of the CDS of IQSEC3 in a reference genome.The toy dataset
grListExampleobject incrisprDesigncontains gene coordinates in hg38 for exons of all human IQSEC3 isoforms, and was obtained by converting an EnsemblTxDbobject into aGRangesListobject using theTxDb2GRangesListconvenience function incrisprDesign.The
queryTxObjectfunction allows us to query such objects for a specific gene and feature. Here, we obtain aGRangesobject containing the CDS coordinates of IQSEC3:We will only consider the first exon to speed up design:
Designing spacer sequences
findSpacersis the main function to obtain a list of all possible spacer sequences targeting protospacers located in the target DNA sequence(s). If aGRangesobject is provided as input, aBSgenomeobject (object containing sequences of a reference genome) will need to be provided as well:This returns a
GuideSetobject that stores genomic coordinates for all spacer sequences found in the regions provided bygr. TheGuideSetobject is an extension of aGenomicRangesobject that stores additional information about gRNAs.For the subsequent sections, we will only work with a random subset of 20 spacer sequences:
Several accessor functions are provided to extract information about the spacer sequences:
The genomic locations stored in the IRanges represent the PAM site locations in the reference genome.
Sequence features characterization
There are specific spacer sequence features, independent of the genomic context of the protospacer sequence, that can reduce or even eliminate gRNA activity:
Use the function
addSequenceFeaturesto adds these spacer sequence characteristics to theGuideSetobject:Off-target search
In order to select gRNAs that are most specific to our target of interest, it is important to avoid gRNAs that target additional loci in the genome with either perfect sequence complementarity (multiple on-targets), or imperfect complementarity through tolerated mismatches (off-targets).
For instance, both the SpCas9 and AsCas12a nucleases can be tolerant to mismatches between the gRNA spacer sequence (RNA) and the protospacer sequence (DNA), thereby making it critical to characterize off-targets to minimize the introduction of double-stranded breaks (DSBs) beyond our intended target.
The
addSpacerAlignmentsfunction appends a list of putative on- and off-targets to aGuideSetobject using one of three methods. The first method uses the fast aligner bowtie (Langmead et al. 2009) via thecrisprBowtiepackage to map spacer sequences to a specified reference genome. This can be done by specifyingaligner="bowtie"inaddSpacerAlignments.The second method uses the fast aligner BWA via the
crisprBwapackage to map spacer sequences to a specified reference genome. This can be done by specifyingaligner="bwa"inaddSpacerAlignments. Note that this is not available for Windows machines.The third method uses the package
Biostringsto search for similar sequences in a set of DNA coordinates sequences, usually provided through aBSGenomeobject. This can be done by specifyingaligner="biostrings"inaddSpacerAlignments. This is extremely slow, but can be useful when searching for off-targets in custom short DNA sequences.We can control the alignment parameters and output using several function arguments.
n_mismatchessets the maximum number of permitted gRNA:DNA mismatches (up to 3 mismatches).n_max_alignmentsspecifies the maximum number of alignments for a given gRNA spacer sequence (1000 by default). Then_max_alignmentsparameter may be overruled by settingall_Possible_alignments=TRUE, which returns all possible alignments.canonical=TRUEfilters out protospacer sequences that do not have a canonical PAM sequence.Finally, the
txObjectargument inaddSpacerAlignmentsusedallows users to provide aTxDbobject, or aTxDbobject converted in aGRangesListusing theTxDb2GRangesListfunction, to annotate genomic alignments with a gene model annotation. This is useful to understand whether or not off-targets are located in the CDS of another gene, for instance.For the sake of time, we will search here for on- and off-targets located in the beginning of the human chr12 where the gene IQSEC3 is located. We will the bowtie method, with a maximum of 1 mismatch.
First, we need to build a bowtie index sequence using the fasta file provided in
crisprDesign. We use theRBowtiepackage to build the index:For genome-wide off-target search, users will need to create a bowtie index on the whole genome. This is explained in this tutorial.
Finally, we also need to specify a
BSgenomeobject storing DNA sequences of the human reference genome:We are now ready to search for on- and off-targets:
Let’s look at what was added to the
GuideSet:A few columns were added to the
GuideSetobject to summarize the number of on- and off-targets for each spacer sequence, taking into account genomic context:To look at the individual on- and off-targets and their context, use the
alignmentsfunction to retrieve a table of all genomic alignments stored in theGuideSetobject:The functions
onTargetsandoffTargetswill return on-target alignments (no mismatch) and off-target alignment (with at least one mismatch), respectively. See?addSpacerAlignmentsfor more details about the different options.Iterative spacer alignments
gRNAs that align to hundreds of different locations are highly unspecific and undesirable. This can also cause
addSpacerAlignmentsto be slow. To mitigate this, we provideaddSpacerAlignmentsIterative, an iterative version ofaddSpacerAlignmentsthat curtails alignment searches for gRNAs having more hits than the user-defined threshold (see?addSpacerAlignmentsIterative).Faster alignment by removing repeat elements
To remove protospacer sequences located in repeats or low-complexity DNA sequences (regions identified by RepeatMasker), which are usually not of interest due to their low specificity, we provide the convenience function
removeRepeats:Off-target scoring
After retrieving a list of putative off-targets and on-targets for a given spacer sequence, we can use
addOffTargetScoresto predict the likelihood of the nuclease to cut at the off-targets based on mismatch tolerance. Currently, only off-target scoring for the SpCas9 nuclease are available (MIT and CFD algorithms):Note that this will only work after calling
addSpacerAlignments, as it requires a list of off-targets for each gRNA entry. The returnedGuideSetobject has now the additional columnsscore_mitandscore_cfdrepresenting the gRNA-level aggregated off-target specificity scores. The off-target table also contains a cutting likelihood score for each gRNA and off-target pair:On-target scoring
addOnTargetScoresadds scores from all on-target efficiency algorithms available in the R packagecrisprScoreand appends them to theGuideSet. By default, scores for all available methods for a given nuclease will be computed. Here, for the sake of time, let’s add only the CRISPRater score:See the
crisprScorevignette for a full description of the different scores.Restriction enzymes
Restriction enzymes are usually involved in the gRNA library synthesis process. Removing gRNAs that contain specific restriction sites is often necessary. We provide the function
addRestrictionEnzymesto indicate whether or not gRNAs contain restriction sites for a user-defined set of enzymes:When no enzymes are specified, the function adds annotation for the following default enzymes: EcoRI, KpnI, BsmBI, BsaI, BbsI, PacI, ISceI and MluI. The function also has two additional arguments,
flanking5andflanking3, to specify nucleotide sequences flanking the spacer sequence (5’ and 3’, respectively) in the lentiviral cassette that will be used for gRNA delivery. The function will effectively search for restriction sites in the full sequence[flanking5][spacer][flanking3].The
enzymeAnnotationfunction can be used to retrieve the added annotation:Gene annotation
The function
addGeneAnnotationadds transcript- and gene-level contextual information to gRNAs from aTxDb-like object:The gene annotation can be retrieved using the function
geneAnnotation:It contains a lot of information that contextualizes the genomic location of the protospacer sequences.
The ID columns (
tx_id,gene_id,protein_id,exon_id) give Ensembl IDs. Theexon_rankgives the order of the exon for the transcript, for example “2” indicates it is the second exon (from the 5’ end) in the mature transcript.The columns
cut_cds,cut_fiveUTRs,cut_threeUTRsandcut_intronsindicate whether the guide sequence overlaps with CDS, 5’ UTR, 3’ UTR, or an intron, respectively.percentCDSgives the location of thecut_sitewithin the transcript as a percent from the 5’ end to the 3’ end.aminoAcidIndexgives the number of the specific amino acid in the protein where the cut is predicted to occur.downstreamATGshows how many in-frame ATGs are downstream of thecut_site(and upstream from the defined percent transcript cutoff,met_cutoff), indicating a potential alternative translation initiation site that may preserve protein function.For more information about the other columns, type
?addGeneAnnotation.TSS annotation
Similarly, one might want to know which protospacer sequences are located within promoter regions of known genes:
For more information, type
?addTssAnnotation.SNP information
Common single-nucleotide polymorphisms (SNPs) can change the on-target and off-target properties of gRNAs by altering the binding. The function
addSNPAnnotationannotates gRNAs with respect to a reference database of SNPs (stored in a VCF file), specified by thevcfargument.VCF files for common SNPs (dbSNPs) can be downloaded from NCBI on the dbSNP website. We include in this package an example VCF file for common SNPs located in the proximity of human gene IQSEC3. This was obtained using the dbSNP151 RefSNP database obtained by subsetting around IQSEC.
The
rs_site_relgives the relative position of the SNP with respect to thepam_site.allele_refandallele_minorreport the nucleotide of the reference and minor alleles, respectively.MAF_1000GandMAF_TOPMEDreport the minor allele frequency (MAF) in the 1000Genomes and TOPMED populations.Filtering and ranking gRNAs
Once gRNAs are fully annotated, it is easy to filter out any unwanted gRNAs since
GuideSetobjects can be subsetted like regular vectors in R.As an example, suppose that we only want to keep gRNAs that have percent GC between 20% and 80% and that do not contain a polyT stretch. This can be achieved using the following lines:
Similarly, it is easy to rank gRNAs based on a set of criteria using the regular
orderfunction.For instance, let’s sort gRNAs by the CRISPRater on-target score:
One can also sort gRNAs using several annotation columns. For instance, let’s sort gRNAs using the CRISPRrater score, but also by prioritizing first gRNAs that have no 1-mismatch off-targets:
The
rankSpacersfunction is a convenience function that implements our recommended rankings for the SpCas9, enAsCas12a and CasRx nucleases. For a detailed description of our recommended rankings, see the documentation ofrankSpacersby typing?rankSpacers.If an Ensembl transcript ID is provided, the ranking function will also take into account the position of the gRNA within the target CDS of the transcript ID in the ranking procedure. Our recommendation is to specify the Ensembl canonical transcript as the representative transcript for the gene. In our example, ENST00000538872 is the canonical transcript for IQSEC3:
CRISPRa/CRISPRi design
For CRISPRa and CRISPRi applications, the CRISPR nuclease is engineered to lose its endonuclease activity, therefore should not introduce double-stranded breaks (DSBs). We will use the dead SpCas9 (dSpCas9) nuclease as an example here. Note that users don’t have to distinguish between dSpCas9 and SpCas9 when specifying the nuclease in
crisprDesignandcrisprBaseas they do not differ in terms of the characteristics stored in theCrisprNucleaseobject.CRISPRi: Fusing dSpCas9 with a Krüppel-associated box (KRAB) domain has been shown to be effective at repressing transcription in mammalian cells (Gilbert et al. 2013). The dSpCas9-KRAB fused protein is a commonly-used construct to conduct CRISPR inhibition (CRISPRi) experiments. To achieve optimal inhibition, gRNAs are usually designed targeting the region directly downstream of the gene transcription starting site (TSS).
CRISPRa: dSpCas9 can also be used to activate gene expression by coupling the dead nuclease with activation factors. The technology is termed CRISPR activation (CRISPRa), and several CRISPRa systems have been developed (see Kampmann (2018) for a review). For optimal activation, gRNAs are usually designed to target the region directly upstream of the gene TSS.
crisprDesignprovides functionalities to be able to take into account design rules that are specific to CRISPRa and CRISPRi applications. ThequeryTssfunction allows to specify genomic coordinates of promoter regions. TheaddTssAnnotationannotates gRNAs for known TSSs, and includes a column nameddist_to_tssthat indicates the distance between the TSS position and the PAM site of the gRNA. For CRISPRi, we recommend targeting the 25-75bp region downstream of the TSS for optimal inhibition. For CRISPRa, we recommend targeting the region 75-150bp upstream of the TSS for optimal activation; see (Sanson et al. 2018) for more information.For more information, please see the following two tutorials:
CRISPR base editing with BE4max
We illustrate the CRISPR base editing (CRISPRbe) functionalities of
crisprDesignby designing and characterizing gRNAs targeting IQSEC3 using the cytidine base editor BE4max (Koblan et al. 2018).We first load the BE4max
BaseEditorobject available incrisprBase:The editing probabilities of the base editor BE4max are stored in a matrix where rows correspond to the different nucleotide substitutions, and columns correspond to the genomic coordinate relative to the PAM site. The
editingWeightsfunction fromcrisprBaseallows to retrieve those probabilities. One can see that C to T editing is optimal around 15 nucleotides upstream of the PAM site for the BE4max base editor:We obtain a
GuideSetobject using the first exon of the IQSEC3 gene and retain only the first 2 gRNAs for the sake of time:The function
addEditedAllelesfinds, characterizes, and scores predicted edited alleles for each gRNA, for a chosen transcript. It requires a transcript-specific annotation that can be obtained using the functiongetTxInfoDataFrame. Here, we will perform the analysis using the main isoform of IQSEC3 (transcript id ENST00000538872).We first get the transcript table for ENST00000538872,
and then add the edited alleles annotation to the
GuideSet:The
editingWindowargument specifies the window of editing that we are interested in. When not provided, it uses the default window provided in theBaseEditorobject. Note that providing large windows can exponentially increase computing time as the number of possible alleles grows exponentially.Let’s retrieve the edited alleles for the first gRNA:It is a
DataFrameobject that contains useful metadata information:The
wildtypeAllelereports the unedited nucleotide sequence of the region specified by the editing window (with respect to the gRNA PAM site). It is always reported from the 5’ to 3’ direction on the strand corresponding to the gRNA strand. Thestartandendspecify the corresponding coordinates on the transcript.Let’s look at the edited alleles:
The
DataFrameis ordered so that the top predicted alleles (based on thescorecolumn) are shown first. Thescorerepresents the likelihood of the edited allele to occur relative to all possible edited alleles, and is calculated using the editing weights stored in theBE4maxobject. Theseqcolumn represents the edited nucleotide sequences. Similar to thewildtypeAlleleabove, they are always reported from the 5’ to 3’ direction on the strand corresponding to the gRNA strand. Thevariantcolumn indicates the functional consequence of the editing event (silent, nonsense or missense mutation). In case an edited allele leads to multiple editing events, the most detrimental mutation (nonsense over missense, missense over silent) is reported. Theaacolumn reports the result edited amino acid sequence.Note that several gRNA-level aggregate scores have also been added to the
GuideSetobject when callingaddEditedAlleles:The
score_missense,score_nonsenseandscore_silentcolumns represent aggregated scores for each of the mutation type. They were obtained by summing adding up all scores for a given mutation type across the set of edited alleles for a given gRNA. ThemaxVariantcolumn indicates the most likely to occur mutation type for a given gRNA, and is based on the maximum aggregated score, which is stored inmaxVariantScore. For instance, for spacer_1, the higher score is thescore_missense, and thereforemaxVariantis set to missense.For more information, please see the following tutorial:
CRISPR knockdown with Cas13d
It is also possible to design gRNAs for RNA-targeting nucleases using
crisprDesign. In contrast to DNA-targeting nucleases, the target spacer is composed of mRNA sequences instead of DNA genomic sequences.We illustrate the functionalities of
crisprDesignfor RNA-targeting nucleases by designing gRNAs targeting IQSEC3 using the CasRx (RfxCas13d) nuclease (Konermann et al. 2018).We first load the CasRx
CrisprNucleaseobject fromcrisprBase:The PFS sequence (the equivalent of a PAM sequence for RNA-targeting nucleases) for CasRx is
N, meaning that there is no specific PFS sequences preferred by CasRx.We will now design CasRx gRNAs for the transcript ENST00000538872 of IQSEC3.
Let’s first extract all mRNA sequences for IQSEC3:
We can use the usual function
findSpacersto design gRNAs, and we only consider a random subset of 100 gRNAs for the sake of time:Note that all protospacer sequences are located on the original strand of the mRNA sequence. For RNA-targeting nucleases, the spacer and protospacer sequences are the reverse complement of each other:
The
addSpacerAlignmentscan be used to perform an off-target search across all mRNA sequences using the argumentcustom_seq. Here, for the sake of time, we only perform an off-target search to the 2 isoforms of IQSEC3 specified by themRNAsobject:The columns
n0_geneandn0_txreport the number of on-targets at the gene- and transcript-level, respectively. For instance,spacer_1095maps to the two isoforms of IQSEC3 hasn0_txis equal to 2:Note that one can also use the
bowtiealigner to perform an off-target search to a set of mRNA sequences. This requires building a transcriptome bowtie index first instead of building a genome index. See thecrisprBowtievignette for more detail.For more information, please see the following tutorial:
Design for optical pooled screening (OPS)
Optical pooled screening (OPS) combines image-based sequencing (in situ sequencing) of gRNAs and optical phenotyping on the same physical wells (Feldman et al. 2019). In such experiments, gRNA spacer sequences are partially sequenced from the 5 prime end. From a gRNA design perspective, additional gRNA design constraints are needed to ensure sufficient dissimilarity of the truncated spacer sequences. The length of the truncated sequences, which corresponds to the number of sequencing cycles, is fixed and chosen by the experimentalist.
To illustrate the functionalities of
crisprDesignfor designing OPS libraries, we use theguideSetExample. We will design an OPS library with 8 cycles.We add the 8nt OPS barcodes to the GuideSet using the
addOpsBarcodesfunction:The function
getBarcodeDistanceMatrixcalculates the nucleotide distance between a set of query barcodes and a set of target barcodes. The type of distance (hamming or levenshtein) can be specified using thedist_methodargument. The Hamming distance (default) only considers substitutions when calculating distances, while the Levenshtein distance allows insertions and deletions.When the argument
binnarizeis set toFALSE, the return object is a matrix of pairwise distances between query and target barcodes:When
binnarizeis set toTRUE(default), the matrix of distances is binnarized so that 1 indicates similar barcodes, and 0 indicates dissimilar barcodes. Themin_dist_editargument specifies the minimal distance between two barcodes to be considered dissimilar:The
designOpsLibraryallows users to perform a complete end-to-end library design; see?designOpsLibraryfor documentation.For more information, please see the following tutorial:
Design of gRNA pairs with the object
The
findSpacerPairsfunction incrisprDesignenables the design of pairs of gRNAs and works similar tofindSpacers. As an example, we will design candidate pairs of gRNAs that target a small locus located on chr12 in the human genome:We first specify the genomic locus:
and find all pairs using the function
findSpacerPairs:The first and second arguments of the function specify the which genomic region the first and second gRNA should target, respectively. In our case, we are targeting the same region with both gRNAs. The other arguments of the function are similar to the
findSpacersfunction described below.The output object is a
PairedGuideSet, which can be thought of a list of twoGuideSet:The first and second
GuideSetstore information about gRNAs at position 1 and position 2, respectively. They can be accessed using thefirstandsecondfunctions:The
pamOrientationfunction returns the PAM orientation of the pairs:and takes 4 different values:
in(for PAM-in configuration)out(for PAM-out configuration),fwd(both gRNAs target the forward strand) andrev(both gRNAs target the reverse strand).The function
pamDistancereturns the distance between the PAM sites of the two gRNAs. The functioncutLengthreturns the distance between the cut sites of the two gRNAs. The functionspacerDistancereturns the distance between the two spacer sequences of the gRNAs.For more information, please see the following tutorial:
Miscellaneous design use cases
Design with custom sequences
crisprDesignalso allows gRNA design for DNA sequences without genomic context (such as a synthesized DNA construct). See?findSpacersfor more information, and here’s an example:Off-target search in custom sequences
One can also search for off-targets in a custom sequence as follows:
For more information, please see the following tutorial:
Session Info
References
Feldman, David, Avtar Singh, Jonathan L Schmid-Burgk, Rebecca J Carlson, Anja Mezger, Anthony J Garrity, Feng Zhang, and Paul C Blainey. 2019. “Optical Pooled Screens in Human Cells.” Cell 179 (3): 787–99.
Gilbert, Luke A, Matthew H Larson, Leonardo Morsut, Zairan Liu, Gloria A Brar, Sandra E Torres, Noam Stern-Ginossar, et al. 2013. “CRISPR-Mediated Modular RNA-Guided Regulation of Transcription in Eukaryotes.” Cell 154 (2): 442–51.
Kampmann, Martin. 2018. “CRISPRi and CRISPRa Screens in Mammalian Cells for Precision Biology and Medicine.” ACS Chemical Biology 13 (2): 406–16.
Koblan, Luke W, Jordan L Doman, Christopher Wilson, Jonathan M Levy, Tristan Tay, Gregory A Newby, Juan Pablo Maianti, Aditya Raguram, and David R Liu. 2018. “Improving Cytidine and Adenine Base Editors by Expression Optimization and Ancestral Reconstruction.” Nature Biotechnology 36 (9): 843–46.
Konermann, Silvana, Peter Lotfy, Nicholas J Brideau, Jennifer Oki, Maxim N Shokhirev, and Patrick D Hsu. 2018. “Transcriptome Engineering with RNA-Targeting Type VI-d CRISPR Effectors.” Cell 173 (3): 665–76.
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.
Sanson, Kendall R, Ruth E Hanna, Mudra Hegde, Katherine F Donovan, Christine Strand, Meagan E Sullender, Emma W Vaimberg, et al. 2018. “Optimized Libraries for CRISPR-Cas9 Genetic Screens with Multiple Modalities.” Nature Communications 9 (1): 1–15.