Genome

Scientists from the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and Harvard, and the National Center for Biotechnology Information at the National Institutes of Health, have developed a new search algorithm to find enzymes of interest in vast microbial sequence databases. This algorithm, called Fast Locality-Sensitive Hashing-based clustering (FLSHclust), discovered…

Researchers at MIT, Harvard, and the National Institutes of Health have utilized a new search algorithm to identify 188 different types of rare CRISPR systems in bacterial genomes. This data holds potential to advance genome-editing technology, enabling more precise treatments and diagnostics. The algorithm, developed in the lab of prominent CRISPR researcher, Professor Feng Zhang uses…

Scientists from the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and Harvard, and the National Center for Biotechnology Information at the National Institutes of Health have developed a new algorithm that can sift through massive amounts of genomic data to identify unique CRISPR systems. Known as Fast Locality-Sensitive Hashing-based clustering…

Microbial sequence databases hold a vast array of information about enzymes and other molecules that could be utilized in biotechnology applications. However, the sheer size of these databases has made it challenging to efficiently search for specific enzymes of interest. Researchers from the McGovern Institute for Brain Research at MIT, the Broad Institute of MIT and…