CRISPR-FOCUS: A web server for designing focused CRISPR screening experiments
CRISPR-FOCUS: A web server for designing focused CRISPR screening experiments
Qingyi Cao 0 1 2 3
Jian Ma 0 1 3
Chen-Hao Chen 0 1 3
Han Xu 0 1 3
Zhi Chen 0 1 2 3
Wei Li 0 1 3
X. Shirley Liu 0 1 3
0 Current address: Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center , Houston, TX , United States of America
1 Funding: This study was supported by National Natural Science Foundation of China , No. 31401104 (QC) , National Natural Science Foundation of China Grant 31329003 and NIH R01 HG008927 of US (to XSL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
2 State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University , Hangzhou, Zhejiang , P. R. China , 2 Department of Bioinformatics, School of Life Science and Technology, Tongji University , Shanghai , P. R. China , 3 Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute , Boston, MA , United States of America, 4 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health , Boston, MA , United States of America
3 Editor: Jianwei Zhang, University of Arizona , UNITED STATES
The recently developed CRISPR screen technology, based on the CRISPR/Cas9 genome editing system, enables genome-wide interrogation of gene functions in an efficient and costeffective manner. Although many computational algorithms and web servers have been developed to design single-guide RNAs (sgRNAs) with high specificity and efficiency, algorithms specifically designed for conducting CRISPR screens are still lacking. Here we present CRISPR-FOCUS, a web-based platform to search and prioritize sgRNAs for CRISPR screen experiments. With official gene symbols or RefSeq IDs as the only mandatory input, CRISPRFOCUS filters and prioritizes sgRNAs based on multiple criteria, including efficiency, specificity, sequence conservation, isoform structure, as well as genomic variations including Single Nucleotide Polymorphisms and cancer somatic mutations. CRISPR-FOCUS also provides pre-defined positive and negative control sgRNAs, as well as other necessary sequences in the construct (e.g., U6 promoters to drive sgRNA transcription and RNA scaffolds of the CRISPR/Cas9). These features allow users to synthesize oligonucleotides directly based on
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
the output of CRISPR-FOCUS. Overall, CRISPR-FOCUS provides a rational and
highthroughput approach for sgRNA library design that enables users to efficiently conduct a
focused screen experiment targeting up to thousands of genes.
(CRISPR-FOCUS is freely available at http://cistrome.org/crispr-focus/)
The Clustered Regularly Interspaced Short Palindromic Repeats
(CRISPR)±CRISPR-associated system genes 9 (Cas9) system has been proving itself to be a prominent genome-editing
technique [1±2]. Based on the CRISPR/Cas9 system, CRISPR screening is a high-throughput
technology that enables researchers to examine the effect of perturbing tens of thousands of
genes in parallel [3±5]. In a CRISPR-based screening experiment, single-guide RNA (sgRNA)
pools designated to target different genomic loci are delivered into the cells by the lentivirus
system, while the function of a gene can be inferred by comparing the abundance of cell
populations bearing sgRNAs that target this particular gene across different conditions. CRISPR
screening has been applied to interrogate gene functions in different contexts, including
immune response [6±7], cancer progression [8±10] and metastasis [
], while recently this
technique was being used to identify the functions of non-coding elements as well [12±18].
Many CRISPR screening experiments are conducted as unbiased, genome-scale
approaches, where several genome-wide screening libraries are available [3,8±9,19]. On the other
hand, focused screen is also conducted in many studies, where researchers use a small-scale
library to target gene sets of specific interests (e.g., oncogenes/tumor suppressors for
oncologists or cytokines for immunologists) [
], to validate hits of genome-wide screens [
], or to
reduce the cost of screens (e.g., in in vivo settings [
To design libraries for CRISPR screens (especially focused screens), several computational
tools can be applied [19,21±30]. However, most of these algorithms provide optimized sgRNAs
for only one or several genes/sequences [22±23,29]. A few web-based tools with nominal batch
design capacity require users to provide target sequence for each individual gene, have strict
size limits on the sequence file uploaded, could only accept limited numbers (10±20 mostly) of
gene IDs as input, or base their work on mining of public domain libraries [19,25±26,30].
Some other tools with substantial batch-design capacity are not web-based, and require users
to download the whole database, compile the source code and fine tune up to dozens of
parameters [21,24,27±28]. Therefore, a user-friendly automatic tool is needed to facilitate the design
process of CRISPR screen experiments.
Another issue of library design comes from the rational sgRNA evaluation and selection
based on multiple criteria. Preferably, sgRNA should have fewer off-target effects (based on the
alignment of spacer sequence across the whole genome [23,26±28]), and higher on-target
knockout efficiencies (determined mainly by the sgRNA sequence context [
]), while it is proved
necessary to consider both of them [
]. Other factors, like sequence conservation [
isoform structures of target genes [
], also have a marked impact on the results of the screen
experiments. Once multiple scores are calculated for all candidate sgRNAs, a method will become
necessary for sgRNAs prioritizing and filtering. Common practices include weight-averaging all
scores by assigning a fixed (or empirical) weight for each criterion [
]; or applying the filters
one by one, followed by ranking the candidates lexicographically [
]. These approaches might
be too loose or too rigid in sgRNA selection, because the distribution of these scores might vary
among different genes. To reach optimal sgRNA ranking results, an ideal method should
consider all criteria, and summarize them appropriately in a context dependent way.
In light of requirements from CRISPR screen experiments, we developed CRISPR-FOCUS,
a web-based method for library design of CRISPR screens. With minimum user input,
CRISPR-FOCUS selects different numbers of sgRNAs targeting up to one thousand genes in
human or mouse genome. SgRNAs in the output are ranked by their summary score, which is
a comprehensive evaluation of efficiency, specificity, as well as target sequence conservation
and the target of multiple isoforms. To our knowledge, CRISPR-FOCUS is the only web-based
tool that is specially optimized for CRISPR screening experiments.
Methods and implementation
The scheme of CRISPR-FOCUS is presented in Fig 1. All possible sgRNA candidates that have
canonical Protospacer Adjacent Motif (PAM) in human and mouse genome are discovered
and stored in the backend database. For each of the candidate sequence, all their attributes
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(described in details below) are pre-computed and stored. When user performs a query
through the web interface, CRISPR-FOCUS will retrieve all possible candidates, prioritize
them and return the top ones with highest scores.
Criteria for sgRNA performance evaluation
To reach the best CRISPR-based knockout effect, the selection of sgRNAs should be optimized
to (1) maximize their on-target cleavage effects (i.e., maximize efficiency), (2) minimize
potential off-target effects (i.e., maximize specificity), (3) ensure the fidelity of their sequence with
corresponding target loci (and to avoid regions with possible genomic variations), and (4)
consider the importance of target region (evaluated by sequence conservation and isoform
structure). CRISPR-FOCUS evaluates every sgRNA with the following indices.
Efficiency. The cleavage efficiency of a sgRNA is a major factor that determines the
sensitivity of a screen experiment . We used SSC [
], a computational algorithm that we
previously developed to predict the cleavage efficiency of candidate sgRNAs. SSC takes spacer
sequences as well as its flanking sequences as input, and uses Least Absolute Shrinkage and
Selection Operator (LASSO) model to calculate an efficiency score for each sgRNA.
CRISPRFOCUS will filter sgRNAs with efficiency score below zero.
Specificity. For each candidate sgRNA, CRISPR-FOCUS first calculated its specificity
] to evaluate the overall similarity with putative off-target genomic loci. For sgRNAs
that have perfect-match off-targets, we further divided them into three categories according to
their off-target positions: (1) non-exon hits that do not overlap with exons of any coding or
non-coding genes, (2) exon (but non-coding) hits that overlap with exons of non-coding
genes, and (3) coding region hits that overlap with exons of coding genes. These sgRNAs may
be considered in a rescue step (described later).
The effect of possible variations. SgRNAs are usually designed based on the reference
genome sequence. The knockout efficiencies of these sgRNAs may be affected by the genomic
sequences in cells that are different from the reference, especially mutation. CRISPR-FOCUS
prefers sgRNAs that cover no or fewer mutations, including Single Nucleotide Polymorphisms
(SNPs) and somatic mutations (especially in cancer). CRISPR-FOCUS retrieved SNP
information from dbSNP [
], and annotated each sgRNA with all possible SNPs whose minor allele
frequency (MAF) is higher than 0.05. sgRNAs that cover no or fewer variations will be
preferentially chosen in the selection procedure. If screen experiments are conducted in cancer cells,
users could also choose whether to avoid recurrent somatic mutations from different cancer
types (using the COSMIC database [
Sequence conservation. Regions in a gene with higher conservation rates across species
are more likely to be important, as they usually encode conserved functional domains (like
catalytic center for enzyme or DNA binding domain for transcriptional factor) whose knockout
are more likely to disrupt gene function [
]. CRISPR-FOCUS annotated each sgRNA with an
average phastCon conservation score [
] of the corresponding target position.
Isoform structure. Some genes have multiple isoforms (or transcripts) with different
structures. To completely knockout a gene, a sgRNA should ideally target as many isoforms as
possible. For each exon region, CRISPR-FOCUS calculates an ªisoform commonality scoreº,
which is defined as the percentage of isoforms that uses this exon. SgRNAs targeting exon
regions with higher scores are preferred.
SgRNA selection and ranking
For each gene in the query, CRISPR-FOCUS first retrieves all genomic coordinates of all
exons, and collects all sgRNA candidates that overlap with these regions. It will next perform a
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Fig 1. The main scheme of CRISPR-FOCUS.
ªfilter and rescueº procedure (described in S1 File in detail) to rank all candidates and pick up
the top ones. For the filtering step, CRISPR-FOCUS will filter sgRNAs that are empirically
regarded as ªbadº candidates, including sgRNAs that: (1) overlap with a SNP or mutation loci,
(2) contains >40% guanine counts (`G's), which is observed to have higher off-target effects
], or (3) are perfectly matched to putative off-target loci within the genome. The remaining
ones will be ranked by a summary score, which is a weighted summary of efficiency,
specificity, phastCon conservation and exon commonality score, while all the weights are dynamically
defined by the Criteria Importance Through Intercriteria Correlation (CRITIC) method [
The purpose of this method is to determine the objective weight for each criterion in multiple
criteria decision problems. Briefly in CRITIC, a value Cj is calculated to quantify the amount
of information transmitted by criterion j, which is determined by both contrast intensity and
conflict of the decision criteria. The contrast intensity is represented by the standard deviation
of j, while the conflict is measured as the multiplicative aggregation of one minus correlation
coefficients between j and the rest of criteria. Finally, object weight wj is generated by
normalizing Cj to the unity of all C values.
If the number of remaining sgRNAs does not reach the desired number, CRISPR-FOCUS
will execute a ªrescueº step to retrieve more possible sgRNAs. At this stage, sgRNAs with
potential off-target hits will be rescued in the following order: (1) sgRNAs with non-exon
offtarget hits only, (2) sgRNAs with off-target hits located on non-coding elements but not coding
regions, (3) sgRNAs with off-target hits located on coding regions. sgRNAs within the same
category will be prioritized based on their number of off-target hits, or by the summary score
if two candidates have the same number of hits within the same category. A detailed flowchart
of the whole procedure is depicted in Fig 2.
The web portal
The web portal of CRISPR-FOCUS (Fig 3) accepts a gene ID (either official gene symbol or
RefSeq ID) list as input, and returns the designated number of sgRNA candidates per each
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Fig 2. Workflow of the sgRNA selection/ranking process in CRISPR-FOCUS. The sgRNA selection/ranking process in CRISPR-FOCUS
is composed of (A) a filter step and (B) a rescue step.
gene. Users can input up to 1000 genes, and retrieve up to 30 sgRNAs per gene. Users can also
select sgRNAs from either Homo sapiens or Mus musculus. The web portal applies Common
Gateway Interface (CGI) to fetch input, while all backend scripts were written in Python
CRISPR-FOCUS also provides other options to accommodate different requirements,
including the selection of different sgRNA lengths (19 or 20nt) [
]. As commonly used
constituents of CRISPR/Cas9 delivery system, human U6 promoter and spCas9 scaffold could be
appended to the output, allowing users to synthesize the library directly from the output.
Furthermore, CRISPR-FOCUS includes a set of negative control sgRNAs (targeting several
known ªsafe-harborº loci within human or mouse genome) [40±41] and positive control
sgRNAs (targeting 58 essential ribosome genes identified in ). The input and output
formats are described in Table A in S2 File. The execution of CRISPR-FOCUS is based on
Fig 3. The main user interface of CRISPR-FOCUS. A screenshot of the CRISPR-FOCUS website (http://
cistrome.org/crispr-focus/)) is shown.
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genome assembly hg38 (for human) and mm10 (for mouse), while full versions of public
domain databases applied to annotate sgRNAs could be found in Table B in S2 File.
Results and discussion
CRISPR-FOCUS provides a high throughput platform for rational sgRNA library design of
CRISPR screen experiment. It could accomplish a full scale design (up to 1000 target genes
with 30 sgRNAs for each) within about twenty seconds. To our knowledge, CRISPR-FOCUS is
now the only web-based sgRNA design tool that provides batch processing mode for custom
CRISPR library design, as well as the most comprehensive tool in sgRNA performance
evaluation. By shortening the distance from ªsilico to benchº, CRISPR-FOCUS facilitates the design
of screening experiments and promotes high-throughput functional studies in various scopes.
S1 File. The schema for sgRNA ranking and selection.
S2 File. Additional supporting information.
rating some of the figures.
The authors thank Hanfei Sun, Chenfei Wang, Binbin Wang and Jinzeng Wang for their help
on web server deployment and maintenance, and Wenyan Cui for help on plotting and
decoConceptualization: Qingyi Cao, Zhi Chen, Wei Li, X. Shirley Liu.
Formal analysis: Qingyi Cao.
Funding acquisition: X. Shirley Liu.
Methodology: Qingyi Cao, Jian Ma, Chen-Hao Chen, Han Xu, Wei Li.
Software: Qingyi Cao, Jian Ma, Chen-Hao Chen, Han Xu.
Supervision: Zhi Chen, Wei Li, X. Shirley Liu.
Writing ± original draft: Qingyi Cao, Wei Li, X. Shirley Liu.
Writing ± review & editing: Qingyi Cao, Wei Li, X. Shirley Liu.
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