In a groundbreaking development, researchers at the Broad Institute of MIT and Harvard, the McGovern Institute for Brain Research at MIT, and the National Center for Biotechnology Information (NCBI) have unveiled a sophisticated search algorithm capable of identifying 188 previously unknown rare CRISPR systems in bacterial genomes. Published in Science, the study showcases the algorithm's efficiency in navigating vast genomic datasets.
The algorithm, named Fast Locality-Sensitive Hashing-based clustering (FLSHclust), was crafted by the team led by CRISPR pioneer Feng Zhang. Employing big-data clustering techniques, FLSHclust swiftly sifted through three major public databases containing data from diverse bacterial sources, such as coal mines, breweries, Antarctic lakes, and dog saliva.
Surprisingly, the researchers uncovered a rich diversity of CRISPR systems, some of which exhibited the potential to edit DNA in human cells, target RNA, and perform various other functions. These novel systems hold promise for applications such as more precise gene editing with fewer off-target effects in mammalian cells compared to current Cas9 systems. Additionally, they could serve as diagnostics or molecular records of cellular activity.
The researchers emphasize the unprecedented level of diversity and flexibility revealed by their search, underscoring the likelihood of more undiscovered rare systems as genomic databases continue to expand. Feng Zhang,
