CRISPR-Cas13 mediates robust transcript knockdown in human cells through direct RNA targeting [1] [2] [3] [4] . Compared to DNA-targeting CRISPR enzymes like Cas9, RNA targeting by Cas13 is transcript-and strand-specific: It can distinguish and specifically knock-down processed transcripts, alternatively spliced isoforms and overlapping genes, all of which frequently serve different functions. Previously, we have described a set of optimal design rules for RfxCas13d guide RNAs (gRNAs), and developed a computational model to predict gRNA efficacy for all human protein-coding genes 5 . However, there is a growing interest to target other types of transcripts, such as noncoding RNAs (ncRNAs) 6, 7 or viral RNAs 8, 9 , and to target transcripts in other commonly-used organisms [10] [11] [12] [13] . Here, we predicted relative Cas13-driven knock-down for gRNAs targeting messenger RNAs and ncRNAs in six model organisms (human, mouse, zebrafish, fly, nematode and flowering plants) and four abundant RNA virus families (SARS-CoV-2, HIV-1, H1N1 influenza and MERS). To allow for more flexible gRNA efficacy prediction, we also developed a web-based application to predict optimal Cas13d guide RNAs for any RNA target entered by the user.
To select optimal gRNAs for transcripts produced from the reference genomes of human, mouse, zebrafish, fly, nematode and flowering plants, we created a user-friendly Cas13 online platform (https://cas13design.nygenome.org/) (Fig. 1a) . We previously found that optimal Cas13 gRNAs depend on specific sequence and structural features, including position-based nucleotide preferences in the gRNA and the predicted folding energy (secondary structure) of the combined direct repeat plus gRNA 5 . Using this algorithm, we pre-computed gRNA efficacies, where possible, for all mRNAs and ncRNAs with varying transcript length for the 6 model organisms (Fig. 1b, Supplementary Fig. 1 ).
For the scored gRNAs for each organism, we found that approximately 20% are ranked in the top quartile (Q4 guides) for both mRNAs and ncRNAs (Fig. 1b) . Remarkably, even though the nucleotide composition can very between RNAs from different species 14-16 , we find a similar proportion of optimal RfxCas13d gRNAs across all six species.
Next, we examined how many predicted high efficacy gRNAs are present, on average, in different transcripts. To do this, we determined what fraction of the transcripts in each organism include n top-scoring (Q4) gRNAs for values of n between 1 and 25. We found that coding sequences contained a higher number of top-scoring gRNA per transcript across all organisms, whereas targeting the noncoding transcriptome is more challenging and varies across different organisms (Fig. 1c) . On average, we were able to find at least 25 Q4 gRNAs for >99% of coding exons in mRNAs but only 80% of ncRNAs. Beyond targeting transcripts from the reference genomes of these model organisms, there are also many other applications of Cas13, such as targeting transcripts from non-model organisms, cleavage of synthetic RNAs, and targeting of transcripts carrying genetic variants not found in the reference genome. Therefore, in addition to these prescored gRNAs, we have also developed a graphical interface that allows the user to input a custom RNA sequence for scoring and selection of optimal gRNAs.
Recently, several groups have proposed using CRISPR-Cas13 nucleases to directly target viral RNAs 8,17 , which has become an area of rapid technology development due to the recent COVID-19 pandemic 18 . However, these approaches do not use optimized Cas13 guide RNAs. Previously, we showed that optimal guide RNAs targeting an EGFP transgene can result in a ~10-fold increase in knock-down efficacy compared to other gRNAs 5 . Therefore, to speed development of effective CRISPR-based antiviral therapeutics, we applied our design algorithm to target SARS-CoV-2 and other serious viral threats using Cas13d.
To ensure coverage of diverse patient isolates, we collected 7,630 sequenced SARS-CoV-2 genomes submitted to the Global Initiative on Sharing All Influenza Data (GISAID) database from 58 countries/regions 19 (Fig. 2a) . Using the first sequenced SARS-CoV-2 isolate from New York City (USA/NY1-PV08001/2020) as a reference 20 , we evaluated how many individual SARS-CoV-2 genomes each reference gRNA can target (Fig. 2b) . Guide RNAs targeting protein-coding regions are mostly wellconserved across all genomes, with lower conservation in more variable regions such as Non-Structural-Protein 14 (NSP14) and Spike (S) protein. We found that gRNAs targeting in the 5' and 3' untranslated regions tended to be poorly conserved, as might be expected given the lack of coding function of these regions (Supplementary Fig. 2) . Upon examination of each of the 26 SARS-CoV-2 genes, we found that all gene transcripts could be targeted with Q4 gRNAs.
Similarly, we designed and scored all gRNAs for the coronavirus MERS and two other RNA viruses, HIV-1 which drives Acquired Immunodeficiency Syndrome (AIDS) and H1N1 pandemic influenza.
Unlike SARS-CoV-2, where a single high-efficacy (Q4) gRNA can target all genomes analyzed, we found that at least two gRNAs are needed to target nearly all available genomes. For the highly mutagenic virus HIV-1 21 , we found that nine gRNAs are needed to target all available genomes (Fig. 2c) . Given the tremendous interest in viral RNA targeting using Cas13 enzymes, this dataset of optimized gRNAs provides a platform for the development of CRISPR therapeutics for broad targeting of viral populations from diverse patient isolates. All designed gRNAs for model organism and viral transcripts can be interactively browsed or downloaded in bulk on the design tool website.
RNA-targeting CRISPR-Cas13 has great potential for transcriptome perturbation and antiviral therapeutics. In this study, we have designed and scored Cas13d gRNAs for both mRNAs and ncRNAs in six common model organisms and identified optimized gRNAs to target virtually all sequenced viral RNAs for SARS-CoV-2, HIV-1, H1N1 influenza and MERS. We further expanded our web-based platform to make the Cas13 gRNA design readily accessible for model organisms and created a new application to enable gRNA prediction for user-provided target RNA sequences. Given the current lack of Cas13 guide design tools, we anticipate this resource will greatly facilitate CRISPR-Cas13 RNA targeting in model organisms, emerging viral threats to human health and novel RNA targets.
For each RNA virus, we identified a minimal set of high-scoring Q4 gRNAs that could target all genomes collected. We used a greedy algorithm as described previously 8 : For each iteration, the gRNAs with the highest number of targeting genomes are added to the set. During each iteration, if multiple gRNAs target the same highest number of genomes, we will pick one for the minimal set and start the next iteration.
All designed Cas13 guide RNAs (for model organisms and RNA viruses) and the interactive design tool are available here: https://cas13design.nygenome.org/. For additional reproducibility, we provide shell scripts and R code to reproduce the figures here: https://gitlab.com/sanjanalab/cas13_webtool. The Cas13 guide design algorithm is available here: https://gitlab.com/sanjanalab/cas13. 