Human genome contains the raw information in form of DNA that determines everything like potential risk for disease, state of health and many other things. Columbia University researchers have developed a computational tool that translate segments into a way to understand the code. Decoding genome can aids scientists understanding of how DNA guides everything from growth and development to aging and disease.

The genomes of simple organism like fruit fly contain 120 million letters worth of DNA, much of which has yet to be decoded because the cues it provides have been too subtle for existing tools to pick, said Richard Mann, PhD, a principal investigator at Columbia’s Mortimer B. Zuckerman Mind Brain Behavior Institute and a senior author of the paper. The new algorithm makes it east to access the genetic code and pick up even the faintest signals, resulting in a much more complete picture what DNA encodes.

Geneticists have looked for ways to decipher the mysteries hidden in DNA. One such mystery has involved a particularly pervasive class of genes known as the Hox genes. Hox genes are the body’s master architects; they determined some of the earliest and critical parts of growth and differentiation like how and where embryo develop and grow. Hox genes do this by producing proteins known as transcription factors, which bind to DNA sequences to turn large cohorts of genes on or off.

In the past, researchers discovered that the Hox transcription factors were also binding at many other locations—just more discretely at so-called ‘low-affinity sites.’ The scientists believed these low-affinity binding sites to be key to the Hox transcription factors being able to drive one aspect of development versus another. The problem remained how to decipher these sites from the genome.

Some researcher developed a genetic sequencing method called SELEX-seq to systematically characterize all Hox binding sites. The development had limitations: It required the same DNA fragment to be sequenced over and over again. With each new round, more pieces of the puzzle were revealed, but information about those critical low-affinity binding sites remained hidden.

To overcome this challenge, Dr. Bussemaker and his team developed a sophisticated new computer algorithm called No Read Left Behind, NRLB. It was able to explain the behavior of all DNA sequences in the SELEX-seq experiment. It gives access to spectrum of binding sites from the highest to the lowest affinity with a greater degree of sensitivity and accuracy than any existing method, including state-of-the-art deep learning algorithms.
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