A deep learning tool could improve genomic sequencing processes, identifying disease-causing mechanisms that might otherwise be missed by traditional screening methods, according to a study published in Nature Machine Intelligence.

Researchers from Children’s Hospital of Philadelphia (CHOP) and New Jersey Institute of Technology (NJIT) developed the tool, which can help predict sites of DNA methylation – a process that can change the activity of DNA without changing its overall structure.

DNA methylation is involved in many key cellular processes and is an important component in gene expression. Errors in methylation can be linked to a wide range of human diseases. Genomic sequencing tools can effectively pinpoint polymorphisms that may cause a disease, but these same methods are unable to capture the effects of methylation because the individual genes still look the same.

Researchers have made a considerable effort to study DNA methylation of N⁶-adenine (6mA) in eukaryotic cells, which include human cells. Although there is genomic data available, the role of methylation in these cells remains elusive.

“Previously, methods that had been developed to identify these methylation sites in the genome were very conservative and could only look at certain nucleotide lengths at a given time, so a large number of methylation sites were missed,” said Hakon Hakonarson, PhD, Director of the Center for Applied Genomics (CAG) at CHOP and one of the senior co-authors of the study.

“We needed to develop a better way of identifying and predicting methylation sites with a tool that could identify these motifs throughout the genome that may have a robust functional impact and are potentially disease causing.”

To overcome this issue, the team developed a deep learning algorithm that could predict where these sites of methylation happened, which could then help researchers determine the effect they might have on nearby genes.

The software, called Deep6mA, applies neural networks to study DNA methylation sites on natural multicellular organisms. This new method holds several advantages, researchers noted. The approach allows for the automation of the sequence feature representation of different levels of detail. Additionally, the method facilitates the integration of a broad spectrum of methylation sequences on nearby genes of interest.

The innovative process could also lead to model development and prediction in large-scale genomic data.

The researchers applied the algorithm to three different types of representative organisms, including A. thaliana, D. melanogaster, and E.coli, the first two being eukaryotic. The deep learning tool was able to identify 6mA methylation sites down to the resolution of a single nucleotide, or basic unit of DNA. Even in this initial confirmation study, researchers were able to visualize regulatory patterns they were unable to see using traditional methods.

“One limitation is that our proposed prediction is purely based on sequence information,” said Zhi Wei, PhD, a professor of computer science at NJIT and a senior co-author of the study.

“Whether a candidate is a 6mA site or not will also depend on many other factors. Methylation, including 6mA, is a dynamic process, which will change with cellular context. In the future, we would like to take other factors into consideration such as gene expression. We hope to predict 6mA across cellular context by integrating other data.”

Despite this limitation, the researchers believe that their study shows the ability for deep learning to accelerate personalized medicine and enhance clinical care.

“We already know that a number of genes have a disease-causing mechanism brought about by methylation, and while this study was not done in human cells, the eukaryotic cell models were very comparable,” Hakonarson said.

“Genomic scientists looking to translate their findings into clinical applications would find this tool very useful, and the level of precision could eventually lead to the discovery of specific cells or targets that are candidates for therapeutic intervention.”

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July 31, 2019 – Improving collaboration between specialists and integrating multiple datasets to leverage big data will be key for advancing research for dry age-related macular degeneration (AMD), according to a new report from a National Institute of Health (NIH) working group.

Over 11 million people in the United States are diagnosed with AMD, an eye disease that ultimately results in blindness. It is the leading cause of blindness among individuals 65 years of age and older.

The disease can manifest in one of two forms: neovascular (wet) or non-neovascular (dry). While the neovascular form progresses more rapidly, there are several known and proven treatments for the disease. There are no preventive measures for dry AMD nor treatment options.

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“The working group thoroughly assessed what is known about dry AMD pathobiology, and the recommendations will be informative for considering future NEI research priorities to align with promising pathways for discovering therapeutic targets,” said Director of National Eye Institute (NEI), Paul Sieving, MD, PhD, in an earlier news release.

The working group recommended a systems biology approach to disease treatment, an integration of genomic, preclinical, medical, pharmacological, and clinical data to inform modeling of the disease progression. Synthesizing big data from all these areas including tissue samples from clinical trials will help inform predictive modeling which can then be used to inform individual patient care.

A personalized approach to disease management may also be helpful, the working group recommended. Such an approach should consider the disease stage, progression, and individual risk factors to provide preventive and treatment strategies specific to the patient, the report said. Collaborating will all points of care will allow a multidisciplinary team to use a patient’s unique clinical, imaging, and genomic data to treat the disease.

“We propose that researchers utilize a systems biology approach, integrating the big data available from clinical registries and various fields of biology known as ‘omics’ to develop better models and ultimately treatments for patients with this blinding disease,” stated report co-author Joan W. Miller, MD.

Due to a lack of preventive strategies and treatment options for dry AMD, the working group noted the need for improved understanding of the disease pathology and promoted clinical trial investigations to do so. Previous research has shown a genetic link to the disease as well as several lifestyle factors including smoking, but there is no work examining the effects these factors have on dry AMD.

A better understanding of how these factors impact the disease will help providers be better informed to watch for risk factors and promote inventive preventive strategies. Such understanding only comes from examining data and promoting the use of big, integrated data sources to help investigators use multiple sources to answer their questions.

Effective disease management will need multiple targets that differ based on the disease stage progression, the report notes. A strategy overhaul needs to take place that focuses on large-scale, collaborative, systems biology in order to effectively treat the disease.

“This approach would integrate basic, genomic, pre-clinical, medical, pharmacological, and clinical data into mathematical models of pathological processes at different stages of dry AMD in order to ask how relevant individual components act together within the living system,” Miller said.

The working group was appointed by the National Advisory Eye Council, a 12-member panel that establishes guidelines for the NEI under the NIH. The group was charged with a multilayered goal: to raise public health awareness about the impact of dry AMD, review the current state of research about the disease for a better understanding of its pathology, propose future research directions, encourage scientists to focus on AMD, and promote collaboration among a network of specialized providers.

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