The Role of Big Data Personalizing the Healthcare Experience: Genomics


Genomics is making headlines in both academia and the celebrity world.  When genetic tests revealed that Angelina Jolie was predisposed to breast cancer, intense media cover ensued. Since then, genetic testing and genomics have been propelled to the front of many more minds.

In this new data field, companies are approaching, collecting, analyzing and turning data into useable information from different perspectives.

What is Genomics?

Genomics is the study of the complete genetic material (genome) of organisms.  The field includes intensive efforts to sequence the entire human DNA in order to map and analyze individual genes as well as their interactions. The primary goal that drives these efforts is to understand the genetic basis of heritable traits. More specifically, it is to understand how genes work in order to prevent or cure diseases.

Who Cares and Why: Genomics & Big Data

The amount of data being produced by sequencing, mapping, and analyzing genomes makes genomics a part of Big Data. Genomics produces huge volumes of data — each human genome has 20,000-25,000 genes comprised of 3 million base pairs. This amounts to 100 gigabytes of data, which is equivalent to 102,400 photos.  Sequencing multiple human genomes quickly adds up to hundreds of petabytes of data. Furthermore, the data created by analysis of gene interactions multiply those further.

Genomics Fuels Personalized Medicine

Understanding each individual’s genome is a necessary foundation for predictive medicine. The patient’s genetic data that is obtained can be used to determine the most appropriate treatments.  Medicine should accommodate people of different shapes and sizes. Physicians and researchers can get a better picture of disease in an individual by combining sequenced genomic data with other medical data. This can save patients not only the hassle of ineffective treatments, but also money and time. Ultimately, the vision is to have treatments that will reflect an individual’s illness — not a one treatment fits all, as is too often true today.

Genomics Analysis Techniques

Three notable genomic start-ups have different approaches to using genomic data as useable information to improve individual and population health.

Firstly, Bina Technologies has created an information platform that allows users to take genomic sequence data, move it, and analyze it. They use a hybrid architecture that keeps some data on the premises and some in the cloud. By pushing computation back to where the data is, data can be reduced 1000-fold, speeding up sequencing time and facilitating movement of the data. Secondly, Portable Genomics uses a mobile visualization platform that is related to the iTunes platform. The visualization brings genomics to consumers and professionals in a very simple way. More specifically, it makes genomics immediately understandable and useable in personalized and preventive medicine. Lastly, NextBio offers a platform built on existing systems to aggregate and analyze genomics data in terms of other relevant medical data.

These different approaches illustrate the current rapidly evolving ecosystem in genomics and personalized medicine: Bina illustrates the power of genomics to improve population health; Portable Genomics exemplifies bringing the power of information to the individual; NextBio is the epitome of a one-stop shop, analyzing and aggregating large data from different streams to personalize individual treatments.

These companies play a role in turning genomic data into personalized information and treatments. Join us for more on Sept 18th at 10am PST for the Webinar Using Big Data to Personalize the Healthcare Experience.

Human Genomics: Then and Now 

Research in the field of genomics has come a long way in the past 60 years. The Human Genome Project (1990-2003) was the pioneering effort in studying the human genome and its effect on disease. It transformed sequencing from a manual process to an automated, computer-based one.

Driven by advances in technology that have dramatically reduced costs, Genome Wide Association Studies (GWAS) is expanding on the Human Genome Project in discovering connections between genes and diseases. GWAS tests single nucleotide polymorphisms (SNPs) for association between genes and diseases. A single nucleotide polymorphism is where there is one nucleotide difference between two genes. For example two sequence DNA fragments, AAGCCTA vs AAGCTTA have one differing nucleotide.

More than 1600 GWAS publications have connected 2000 gene associations with more than 300 common human disease traits.

GWAS hasn’t yet proven directly useful for guiding individual health, but we may be on the brink of changing this. There are three near future clinical applications for GWAS: 1) Predictive models to identify high risk patients, i.e. Type 1 Diabetes patients. 2) Classifying disease subtypes of potential use for more precisely guided clinical trials and targeted treatments. 3) Provide better information for screening drug candidates for toxicity and efficacy before clinical trials.

The Cost of Sequencing

By the time the Human Genome Project was completed, the cost to sequence the human genome was $40 million — down from $95 million just two years earlier. Academics and companies have been working hard to make sequencing affordable and therefore available to the public. Today, an individual human genome can be sequenced for around $5,000, consistently and accurately.

To learn more information and about the companies that are using genomics to improve human health, join us on Sept 18th at 10am PST for the Webinar, using Big Data to Personalize the Healthcare Experience.


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