We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!
Researchers affiliated with Nvidia and Harvard today detailed AtacWorks, a machine learning toolkit designed to bring down the cost and time needed for rare and single-cell experiments. In a study published in the journal Nature Communications, the coauthors showed that AtacWorks can run analyses on a whole genome in just half an hour compared with the multiple hours traditional methods take.
Most cells in the body carry around a complete copy of a person’s DNA, with billions of base pairs crammed into the nucleus. But an individual cell pulls out only the subsection of genetic components that it needs to function, with cell types like liver, blood, or skin cells using different genes. The regions of DNA that determine a cell’s function are easily accessible, more or less, while the rest are shielded around proteins.
AtacWorks, which is available from Nvidia’s NGC hub of GPU-optimized software, works with ATAC-seq, a method for finding open areas in the genome in cells pioneered by Harvard professor Jason Buenrostro, one of the paper’s coauthors. ATAC-seq measures the intensity of a signal at every spot on the genome. Peaks in the signal correspond to regions with DNA such that the fewer cells available, the noisier the data appears, making it difficult to identify which areas of the DNA are accessible.
ATAC-seq typically requires tens of thousands of cells to get a clean signal. Applying AtacWorks produces the same quality of results with just tens of cells, according to the coauthors.
AtacWorks was trained on labeled pairs of matching ATAC-seq datasets, one high-quality and one noisy. Given a downsampled copy of the data, the model learned to predict an accurate high-quality version and identify peaks in the signal. Using AtacWorks, the researchers found that they could spot accessible chromatin, a complex of DNA and protein whose primary function is packaging long molecules into more compact structures, in a noisy sequence of 1 million reads nearly as well as traditional methods did with a clean dataset of 50 million reads.
AtacWorks could allow scientists to conduct research with a smaller number of cells, reducing the cost of sample collection and sequencing. Analysis, too, could become faster and cheaper. Running on Nvidia Tensor Core GPUs, AtacWorks took under 30 minutes for inference on a genome, a process that would take 15 hours on a system with 32 CPU cores.
In the Nature Communications paper, the Harvard researchers applied AtacWorks to a dataset of stem cells that produce red and white blood cells — rare subtypes that couldn’t be studied with traditional methods. With a sample set of only 50 cells, the team was able to use AtacWorks to identify distinct regions of DNA associated with cells that develop into white blood cells, and separate sequences that correlate with red blood cells.
“With very rare cell types, it’s not possible to study differences in their DNA using existing methods,” Nvidia researcher Avantika Lal, first author on the paper, said. “AtacWorks can help not only drive down the cost of gathering chromatin accessibility data, but also open up new possibilities in drug discovery and diagnostics.”
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Learn more about membership.