Life Sciences and Big Data: Made for Each Other

You can’t find better examples of the promises and pitfalls of Big Data than in the realm of the life sciences. According to an excellent article, “Unraveling the Complexities of Life Sciences Data,” published in the journal Big Data, with the combination of the completion of the human genome project and the availability of advanced analysis technologies, “the 21st-century life sciences have entered the fourth paradigm of data-enabled sciences and the realm of big data.”

The authors, Roger Higdon, et. al., note that the scale of biological data is increasing exponentially. Sequencing technologies are producing data faster than the growth of computing power predicted by Moore’s Law – a 10,000-fold increase in sequencing as compared to a 16-fold uptick in computational power. This brings the life sciences squarely up against the challenges of the “5 Vs” of big data: volume, veracity, velocity, variety, and value.

The paper details the specific problems facing life sciences researchers and presents a series of solutions for handling the field’s treasure trove of complex life sciences data. Included is an integrated data resource developed by the Kolker Lab; an efficient way to functionally annotate newly sequence genomes and metagenomes; all-versus-all sequence alignments; a platform under development for visualizing complex data; and a plan for community outreach and education in the data-enabled sciences.

It is clear that the life sciences have become big data and data-enabled science,” the authors conclude. “Data-enabled science may have at its core the generation of data in the lab, but transforming the data to knowledge and action to breakthroughs and benefits goes far beyond the lab. The transformation will require massive resources and transdisciplinary collaborative efforts put forth by the scientific community to solve the challenges of big data. The need is urgent and growing, given the issues of data generation outstripping computing power and the lack of reproducibility of research. Organizations like DELSA Global can inform the life sciences community, lead the way for groups like the Kolker Lab to put forth new solutions to big data challenges, and create a new paradigm in the life sciences of cooperation, collaboration, and sharing at every level.

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