Scientists from NVIDIA and Harvard have made a huge breakthrough in genetic research. They developed a deep-learning toolkit that is able to significantly cut down the time and cost needed to run rare and single-cell experiments. According to a study published in Nature Communications, the AtacWorks toolkit can run inference on a whole genome, a process that normally takes a little over two days, in just half an hour. It's able to do so thanks to NVIDIA's Tensor Core GPUs. AtacWorks works with ATAC-seq, a well-established method designed to find open areas in the genome of healthy and diseased cells. These "open areas" are subsections of a person's DNA that are used to determine and activate specific functions (think liver, blood or skin cells).

Researchers from Nvidia and Harvard are publishing research this week on a new way they've applied deep learning to epigenomics -- the study of modifications on the genetic material of a cell. Using a neural network originally developed for computer vision, the researchers have developed a deep learning toolkit that can help scientists study rare cell types -- and possibly identify mutations that make people more vulnerable to diseases. The new deep learning toolkit, called AtacWorks, "allows us to study how diseases and genomic variation influence very specific types of cells of the human body," Nvidia researcher Avantika Lal, lead author on the paper, told reporters last week. "And this will enable previously impossible biological discovery, and we hope would also contribute to the discovery of new drug targets." AtacWorks, featured in Nature Communications, works with ATAC-seq -- a popular method for finding the parts of the human genome that are accessible in cells.