In the Big Data in Biology section, we'll explore methods for analyzing large scale NGS data sets using computational algorithms, statistical tools, and supercomputers. The skills needed for large scale sequence data analysis can be applied to answer many different biological questions. The computational power available to researchers has also improved over time, but the volume and heterogeneity of the data often exceed the strategies and tools available for their collection and analysis. Because analyzing big data in biology is incredibly difficult, Hunter says, open science is increasingly important.
The search for large scale data integration requires all biological disciplines to identify these theories and discuss their implications for the modeling and analysis of big data (Leonelli, 201. It also promotes efforts to document that history in databases, so that future data users can evaluate the quality of the data on their own and according to their own standards). The choice and definition of the keywords used to classify and retrieve data are very important for further interpretation. BGI programmers relied on this framework to teach software tools to perform large scale data analysis on many computers at the same time. Mountains of data and analysis are altering the way science progresses and are keeping biologists from getting their feet and hands wet.
Munson says that Aspera has established a pay-per-use system in the Amazon cloud to address the problem of data sharing. Transferring data with FASP is hundreds of times faster than methods that use the normal Internet protocol, says software engineer Michelle Munson, executive director and co-founder of Aspera. It then provides a full summary of the important strategies adopted for the management of biological big data, including a discussion of all the tools or software recently used for the processing and analysis of high-performance biological big data. Far from being “the end of theory”, computational big data mining involves important theoretical commitments.
When a person gives consent for their data to be used in a way, researchers can't suddenly change that use, he says. To increase the speed and capacity of analysis as data sets grow, BGI combined a series of cloud-based analysis steps into a workflow called Gaea, which uses the Hadoop open-source software framework. BGI employs more than 600 software engineers and developers to manage its information technology infrastructure, manage data, and develop software tools and workflows. The company stores its data and performs analysis in its own IT infrastructure, rather than in the cloud, to keep the data private and protected.
Genomic data represents 2 petabytes of that amount, a figure that more than doubles every year3 (see “Data Explosion”).