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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Disease and Pest Management Research Unit » Research » Publications at this Location » Publication #410606
Research Project: Knowledge Based Tools for Exotic and Emerging Diseases of Small Fruit and Nursery Crops

Location: Horticultural Crops Disease and Pest Management Research Unit

Title: Krisp: A python package for designing CRISPR and amplification-based diagnostic assays from whole genome data

Author
item Foster, Zachary
item Tupper, Andrew
item Press, Caroline
item Grunwald, Niklaus

Submitted to: bioRxiv
Publication Type: Other
Publication Acceptance Date: 11/17/2023
Publication Date: 11/17/2023
Citation: Foster, Z.S., Tupper, A., Press, C.M., Grunwald, N.J. 2023. Krisp: A python package for designing CRISPR and amplification-based diagnostic assays from whole genome data. bioRxiv. 1/1-44. https://doi.org/10.1101/2023.11.16.567433.
DOI: https://doi.org/10.1101/2023.11.16.567433

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 CRISPR diagnostics and traditional amplification-based diagnostics such as PCR. 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 Phytophthora ramorum. Krisp is released open source under a permissive license with all the documentation needed to quickly design CRISPR-Cas diagnostic assays and other amplification-based assays.

Technical Abstract: Recent pandemics such as COVID-19 have highlighted the importance of rapidly developing diagnostics to detect and monitor evolving pathogens. CRISPR-Cas technology, combined with isothermal DNA amplification methods, has recently been used 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, the discovery of diagnostic regions of a genome flanked by conserved regions where primers can be designed requires extensive bioinformatic analyses of genome sequences. We developed the python package krisp to find primers and diagnostic sequences that differentiate groups of samples from each other at any taxonomic scale, using either unaligned genome sequences or a variant call format (VCF) file as input. Krisp has been optimized to handle large datasets by using efficient algorithms that run in near linear time, use minimal RAM, and leverage parallel processing 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 species. Krisp is released open source under a permissive license with all the documentation needed to quickly design CRISPR-Cas diagnostic assays.