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August 22, 2018
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Streamlining the Next-Generation of Genomic Sequencing – Saving Time and Saving Lives

We’ve just published our most recent case study! This story gives an in-depth look at how Quanam is leveraging a modern data architecture along with machine learning, to better equip clinicians.

Founded in Uruguay in 1978, Quanam is a multi-Latin federation of firms specializing in consulting and management, professional services, communication and change management. Priding itself on innovation and knowledge, Quanam’s broad range of clients vary from banking and government agencies to financial institutions and even genetic laboratories. Comprising a 400-strong team of engineers, analysts, economists, managers, accountants and statisticians, Quanam uses state-of-the-art solutions and tools to solve modern day global problems.

Quanam, partnering with GenLives, had the goal of allowing clinicians to make more accurate diagnoses in shorter timeframes. The genome of a single person can deliver up to four million mutations and generate up to 300 gigabytes of information. Quanam needed a way to isolate the information responsible for causing pathogenic conditions. Geneticists also had to sift through millions of existing academic papers manually, trying to identify these potentially dangerous conditions.

For this goal to become a reality, Quanam needed a big data platform that could withstand demanding storage and computing requirements to process multiple complex algorithms. With HDP, the company could analyze and store large volumes of data, and offered a foundation on which machine learning algorithms could be run.

With this new modern data architecture put into place, clinical geneticists can make more informed decisions on the dangers of genome variants. DNA sequencing now takes a matter of hours, rather than an entire day. Additionally, the now pre-enriched literature database uses algorithms to establish connections across millions of resources, helping to build a clearer picture of which genomic variant is dangerous. This results in a faster diagnosis, from an average of 5 years, down to 8-10 weeks.

“We’re convinced Hortonworks is a great option, especially in providing a proven infrastructure for both data at rest and data in motion.”-Fernando Lopez, Business Analytics – Big Data, Quanam.

Each new diagnosis can be re-used in later cases that clinicians encounter. So far, more than 50 patients have been analyzed with the new platform. With over 400 million patients globally suffering from rare conditions, the increasing the speed of molecular-based diagnoses promises to save lives.

To read the full story of Quanam’s use case, visit:

Plus view Quanam’s webinar here:



Infoprovincia says:

Matt Spillar, thank you for your blog post.Really thank you! Awesome.

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