Did you know that a single human genome sequence constitutes an approximate 140-gigabyte data file? Not surprisingly, it took 10 years and $3 billion to completely map the first human genome. Fortunately, as Datanami’s Alex Woodie confirms, the medical establishment has come a long way since its big genomics breakthrough in 2003.
“Today whole-genome tests from Illumina can now be had in a matter of weeks and $1,000,” he explained. “While storing these test results (a whole genome is about 250 GB) is a Big Data exercise in itself, it’s what you do with that data that counts.”
As Woodie reports, genomic data is currently being leveraged across multiple spaces within the medical establishment, including various initiatives to help save children from genetic diseases. Indeed, Shane Corder, a senior HPC system engineer at Children’s Mercy Hospital discussed his experiences with pediatric genomics at the Leverage Big Data conference earlier this year. According to Corder, the hospital is preparing to take advantage of fast Illumina sequencers to help newborns suffering from genetic diseases. Unfortunately, approximately one in 30 children are afflicted by one of 8,000 genetic diseases.
“There are some fairly exciting new technological advances we’re hoping to implement here in the center that will eventually let us go sub-24 hours on a full genome,” Corder said in a statement quoted by Datanami.
As opposed to next-gen sequencers, the hospital’s fastest sequence technology takes about 50 hours to get a diagnosis.
“When a child is waiting for a diagnosis, a lot of times the disease lays waste to their body or their mind,” Corder said. “With quicker diagnoses and treatment, that can ultimately change the child’s life forever.”
According to Datanami’s George Leopold, the steady progress being made in “precision,” or genomic medicine has prompted an increasing need to “get a better handle” on soaring data volumes. In an effort to maximize and accelerate the integration of Big Data and data science into biomedical research, the National Institutes of Health (NIH) recently awarded a $1.3 million contract to the University of Illinois and Stanford University to develop new data compression approaches.
“[Interestingly], genomic data lends itself to data compression since sequences often contain much repetition as a result of a relatively small alphabet,” Leopold added. “Similar techniques were developed in the 1990s for video compression that led directly to applications like high-definition television.”
As we’ve previously discussed on Rambus Press, healthcare – in general – is expected to be a primary driver of the cross-industry Big Data market worldwide, with Transparency Market Research projecting a massive increase from $6.3 billion to $48.3 billion by 2018.
“[With] healthcare embracing the need for accurate data, real-time insights into financial performance and patient care and a better understanding of population health management and consumer behaviors, Big Data analytics will continue to be a sound investment,” Jennifer Bresnick of HealthITAnalytics confirmed.
Indeed, as Gregory Berg of AxisPoint Health recently noted in an Healthcare IT News article, Big Data is helping to improve healthcare analytics in a number of ways, including finding and targeting the appropriate individuals, delivering the right intervention at the right time and adjusting programs while closing the loop.
“Improved healthcare analytics leads to improved programs and the ability to create new ones. The potential to improve outcomes and contain costs from the analyzing Big Data in healthcare are, well, big,” he added. “It has been reported that preventive actions – such as early cholesterol screening for patients with associated histories, hypertension screening for adults or smoking cessation – could reduce the total cost of care by over $38 billion, through the prevention of downstream medical episodes, earlier identification of the most appropriate treatment and avoidance of interim chronic care.”