Blacklisting computing proves valuable for genetic analysis

A computational technique used to block unwanted files and messages as a form of access or spam control has proven to be a novel method for streamlining the identification of genetic drivers of disease.

The approach, called blacklisting, has been leveraged by researchers at the Icahn School of Medicine at Mount Sinai and The Rockefeller University as a filter to single out genetic variations in patient genomes and exomes that do not cause illness.

“Until now, there has been no viable published method for filtering out non-pathogenic variants that are common in human genomes and absent from current genomic databases,” said Yuval Itan, assistant professor of genetics and genomic sciences at the Icahn School of Medicine. “Using the blacklist, researchers will now be able to remove genetic ‘noise’ and focus on true disease-causing mutations.”

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Itan and his colleagues co-authored a paper describing the technique in the December issue of Proceedings of the National Academy of Science of the United States of America.

“Computational analyses of human patient exomes aim to filter out as many nonpathogenic genetic variants as possible, without removing the true disease-causing mutations,” according to the authors. “This involves comparing the patient’s exome with public databases to remove reported variants inconsistent with disease prevalence, mode of inheritance, or clinical penetrance.”

However, they contend that their work using the blacklisting computational technique “demonstrates the power of extracting variant blacklists from private databases as a specific in-house but broadly applicable tool for optimizing exome analysis.”

Researchers have developed a program, called ReFiNE, as well as an associated web server that others can use to automate the creation of their own blacklists.

Ultimately, Itan hopes that the tool will help clinicians to conduct more accurate and rapid genetic analyses than the traditional cumbersome process of sifting through large amounts of data, thereby accelerating the pace of genomic medicine.

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