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

• Published in: PLoS computational biology 2024-05, Vol.20 (5), p.e1012139
• Main Authors: Foster, Zachary S L, Tupper, Andrew S, Press, Caroline M, Grünwald, Niklaus J
• Format: Article
• Language: English
• Subjects: Algorithms; Assaying; Computational Biology - methods; COVID-19; COVID-19 - diagnosis; COVID-19 - virology; CRISPR; CRISPR-Cas Systems - genetics; Design; Diagnostic reagents; Diagnostic systems; DNA; DNA sequencing; Enzymes; Gene sequencing; Genetic research; Genome-wide association studies; Genomes; Genomics; Humans; Immune system; Multiprocessing; Nucleotide sequence; Nucleotide sequencing; Pandemics; Parallel processing; Pathogens; Proteins; Python (Programming language); RNA; SARS-CoV-2 - genetics; Semiconductor industry; Software; Technology application; Whole genome sequencing; Whole Genome Sequencing - methods
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1012139

Recent pandemics like COVID-19 highlighted the importance of rapidly developing diagnostics to detect evolving pathogens. CRISPR-Cas technology 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 aid in the discovery of primers and diagnostic sequences that differentiate groups of samples from each other, 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 a CRISPR diagnostic assay 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.