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).

Advances in artificial intelligence (AI) deep learning, genomics, and computing hardware is accelerating life sciences research and discovery. In a new study published today in Nature Communications, researchers from NVIDIA Corporation (NASDAQ: NVDA) and Harvard University's Department of Stem Cell and Regenerative Biology create an AI deep learning tool called AtacWorks that denoises genomic sequencing data and find areas with accessible DNA that may help speed up new diagnostics, de novo drugs, and treatments for diseases in the future. Early intervention and treatment of cancer and genetic diseases may make the difference in outcomes and requires early intervention. The challenge is that sample size of cell data may be small and the data itself may contain extraneous "noise." Having a way to filter and reduce the non-relevant data, or noise, and to boost the relevant data, or signal, in those cases can help speed up research.

ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks, a deep learning toolkit to denoise sequencing coverage and identify regulatory peaks at base-pair resolution from low cell count, low-coverage, or low-quality ATAC-seq data. Models trained by AtacWorks can detect peaks from cell types not seen in the training data, and are generalizable across diverse sample preparations and experimental platforms. We demonstrate that AtacWorks enhances the sensitivity of single-cell experiments by producing results on par with those of conventional methods using ~10 times as many cells, and further show that this framework can be adapted to enable cross-modality inference of protein-DNA interactions. Finally, we establish that AtacWorks can enable new biological discoveries by identifying active regulatory regions associated with lineage priming in rare subpopulations of hematopoietic stem cells. ATAC-seq measures chromatin accessibility as a proxy for the activity of DNA regulatory regions across the genome. Here the authors present AtacWorks, a deep learning tool to denoise and identify accessible chromatin regions from low cell count, low-coverage, or low-quality ATAC-seq data.

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.