Team develops a universal AI algorithm for in-depth cleaning of single cell genomic data

Just as asking a single person about their health will provide tailored, personalized information impossible to glean from a large poll, an individual cell's genome or transcriptome can provide much more information about their place in living systems than sequencing a whole batch of cells. But until recent years, the technology didn't exist to get that high resolution genomic data--and until today, there wasn't a reliable way to ensure the high quality and usefulness of that data. Researchers from the University of North Carolina at Charlotte, led by Dr. Weijun Luo and Dr. Cory Brouwer, have developed an artificial intelligence algorithm to "clean" noisy single-cell RNA sequencing (scRNA-Seq) data. The study, "A Universal Deep Neural Network for In-Depth Cleaning of Single-Cell RNA-Seq Data," was published in Nature Communications on April 7, 2022. From identifying the specific genes associated with sickle cell anemia and breast cancer to creating the mRNA vaccines in the ongoing COVID-19 pandemic, scientists have been searching genomes to unlock the secrets of life since the Human Genome Project of the 1990s.