Genetic sequences are vitally important for life, but why? Our theory of life may have been built on an unproven assumption. We have supposed that the genetic material in cells, the genome, is a list of instructions. This assumption has held since the discovery of DNA in the mid-twentieth century, but now there may be another, largely unexplored way to interpret the role of the genome. There are several reasons we should suppose that the coding regions of DNA sequence (genes) don't carry instructions at all. Instead, they may be just lists of templates for proteins that can be produced by the cell, if the cell chooses to do so.

"In order to identify the rare few drug candidates that induce desired epigenetic effects, scientists need methods to screen hundreds of thousands of potential compounds," says Alexey Terskikh, Ph.D., associate professor in Sanford Burnham Prebys' Development, Aging and Regeneration Program and senior author of the study. "Our study describes a powerful image-based approach that enables high-throughput epigenetic drug discovery." Epigenetics refers to chemical tags on DNA that allow cellular machinery greater or less access to genes -- thus altering gene expression. Nearly all changes in a cell, including reaction to a drug and environmental stress, are reflected by its epigenetic state. Several medicines that target epigenetic alterations are approved by the U.S. Food and Drug Administration (FDA) for the treatment of cancer, and researchers are working to find additional epigenetic-based therapies.

Researchers at the Sanford Burnham Prebys Medical Discovery Institute say that machine learning's powerful ability to detect patterns in complex data is revolutionizing how scientists diagnose disease and, now, how they discover new drugs. The Sanford Burnham team has developed a machine-learning algorithm that gleans information from microscope images that allow for high-throughput epigenetic drug screens. They believe that this approach ("Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape"), described in eLife, could unlock new treatments for cancer, heart disease, mental illness, and other diseases. "High-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data. In the field of epigenetics, such screening methods have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks and employs machine learning to accurately distinguish between such patterns," the investigators wrote.

Accurate diagnosis is essential for appropriate disease treatment. A core technique used to diagnose brain cancer today is the microscope-based analysis of tumour samples on glass slides, termed histology. However, this requires the appraisal of subtle cellular alterations, which in some cases may lead to different classifications for a given sample by different individuals. Nowadays, technological developments enable vast amounts of molecular data to be obtained and assessed for a tumour without the need for such subjective diagnostics. Machine-based-learning approaches are being developed to aid the diagnosis of clinical samples, and in a paper in Nature, Capper et al.1 report such a method for classifying brain tumours on the basis of molecular patterns.