tiger.md

TIGER Cas13 Efficacy Prediction

Welcome to TIGER! This Hugging Face space is an online tool that accompanies our Nature Biotechnology article. TIGER's ability to make accurate on- and off-target predictions enables biologists to both design highly effective gRNAs and precisely modulate transcript expressing by engineering gRNA mismatches. If you utilize our model, please consider citing us:

Wessels, H.-H., Stirn, A., Méndez-Mancilla, A., Kim, E. J., Hart, S. K., Knowles, D. A., & Sanjana, N. E. (2023). Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learning. Nature Biotechnology. https://doi.org/10.1038/s41587-023-01830-8

This tool differs from our manuscript in two ways. First, this version of TIGER predicts using just target and guide sequence, which will marginally reduce performance (fig 3c). Second, we map TIGER's outputs to the unit interval to make estimates more interpretable: a one corresponds to high gRNA activity and a zero denotes no activity. This transformation, maps estimates with no detectable Cas13 activity to (0,0.025] and the most active 2.5% of estimates to [0.975,1) This transformation is monotonically decreasing and therefore preserves Spearman, AUROC, and AUPRC performance. We label these transformed LFC estimates as Guide Score in our prediction tables.

Using TIGER

We support two methods for transcript entry:

• Manual entry of a single transcript
• Uploading a FASTA file that can contain one or many transcripts provided each has a unique ID

We currently offer three run modes:

• We report all on-target gRNAs for each provided transcript. This mode does not support off-target identification due to current computational constraints.
• We report the top ten most active, on-target gRNAs for each provided transcript. This mode allows for the optional identification of off-target effects.
• We report the top ten most active on-target gRNAs for each provided transcript and their titration candidates (all possible single mismatches). This mode also does not support off-target identification due to current computational constraints.

We use version 19 of gencode (protein-coding and lncRNA) to identify off-target candidates.

Feature Roadmap

• Off-target scanning speed improvements
• Off-target scanning for titration mode
• Allow user to select more than the top ten guides per transcript
• Incorporate non-scalar features (target accessibility, hybridization energies, etc...)

To report bugs or to request additional features, please click the "Community" button in the top right corner of this screen and start a new discussion. Alternatively, please email Andrew Stirn.