Krisp: A Python package to aid in the design of CRISPR and amplification-based diagnostic assays from whole genome sequencing data

Authors: Foster, Zachary; Tupper, Andrew; Press, Caroline; Grunwald, Niklaus - Nik

Submitted to: PLoS Computational Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/6/2024
Publication Date: 6/20/2024
Citation: Foster, Z.S.L., Tupper, A.S., Press, C.M., Grunwald, N.J. 2024. Krisp: A Python package to aid in the design of CRISPR and amplification-based diagnostic assays from whole genome sequencing data. PLoS Computational Biology. 20(5):e1012139. https://doi.org/10.1371/journal.pcbi.1012139.
DOI: https://doi.org/10.1371/journal.pcbi.1012139

Interpretive Summary: Pathogens continue to emerge at accelerated rates affecting animals, plants, and ecosystems. Rapid development of novel diagnostic tools is needed to monitor novel pathogen variants or groups. We developed the computational tool krisp to identify genetic regions suitable for development of clustered regularly interspaced short palindromic repeats (CRISPR) diagnostics. Krisp scans whole genome sequence data for target and non-target groups to identify diagnostic regions based on DNA or RNA sequences. This computational tool has been validated using genome data for the sudden oak death pathogen. Krisp is released open source under a permissive license with all the documentation needed to quickly design CRISPR-Cas diagnostic assays.

Technical Abstract: Pandemics such as COVID-19 have provided an urgent need for development of rapid diagnostics to diagnose and monitor novel variants. CRISPR-Cas technology combined with isothermal amplification, has recently been applied to develop diagnostic assays for sequence-specific recognition of DNA or RNA. These assays have similar sensitivity to the gold standard qPCR but can be deployed as easy to use and inexpensive test strips. However, discovery of target RNA or DNA sequences that have conserved primers flanking a diagnostic sequence require extensive bioinformatic analyses of genome sequences. We developed the python package krisp to find primers and diagnostic sequences that differentiate taxonomic groups from each other at the variant, species or any taxonomic scale, using either unaligned genome sequences or VCF files as input. Krisp has been optimized to handle large datasets by using efficient algorithms that run in near linear time, use little RAM, and leverage multiple threads when available. The validity of krisp results has been demonstrated in the laboratory with the successful design of SHERLOCK assays to distinguish the sudden oak death pathogen Phytophthora ramorum from closely related Phytophthora spp. including detection of all known variants of P. ramorum. Krisp is released open source under a permissive license with all the documentation needed to quickly design CRISPR-Cas diagnostic assays.