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9 - Let the Data Do the Talking: Hypothesis Discovery from Large-Scale Data Sets in Real Time

Published online by Cambridge University Press:  05 December 2012

By
Christopher Oehmen ,
Scott Dowson ,
Wes Hatley ,
Justin Almquist ,
Bobbie-Jo Webb-Robertson ,
Jason McDermott ,
Ian Gorton  and
Lee Ann McCue
Edited by
Ian Gorton  and
Deborah K. Gracio
Christopher Oehmen
Affiliation:
Pacific Northwest National Laboratory
Scott Dowson
Affiliation:
Pacific Northwest National Laboratory
Wes Hatley
Affiliation:
Future Point Systems
Justin Almquist
Affiliation:
Pacific Northwest National Laboratory
Bobbie-Jo Webb-Robertson
Affiliation:
Pacific Northwest National Laboratory
Jason McDermott
Affiliation:
Pacific Northwest National Laboratory
Ian Gorton
Affiliation:
Pacific Northwest National Laboratory
Lee Ann McCue
Affiliation:
Pacific Northwest National Laboratory
Ian Gorton
Affiliation:
Pacific Northwest National Laboratory, Washington
Deborah K. Gracio
Affiliation:
Pacific Northwest National Laboratory, Washington
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Summary

Discovering Biological Mechanisms through Exploration

The availability of massive amounts of data in biological sciences is forcing us to rethink the role of hypothesis-driven investigation in modern research. Soon thousands, if not millions, of whole-genome DNA and protein sequence data setswill be available thanks to continued improvements in high-throughput sequencing and analysis technologies. At the same time, high-throughput experimental platforms for gene expression, protein and protein fragment measurements, and others are driving experimental data sets to extreme scales. As a result, biological sciences are undergoing a paradigm shift from hypothesisdriven to data-driven scientific exploration. In hypothesis-driven research, one begins with observations, formulates a hypothesis, then tests that hypothesis in controlled experiments. In a data-rich environment, however, one often begins with only a cursory hypothesis (such as some class of molecular components is related to a cellular process) that may require evaluating hundreds or thousands of specific hypotheses rapidly. This large number of experiments is generally intractable to perform in physical experiments. However, often data can be brought to bear to rapidly evaluate and refine these candidate hypotheses into a small number of testable ones. Also, often the amount of data required to discover and refine a hypothesis in this way overwhelms conventional analysis software and hardware. Ideally advanced hardware can help the situation, but conventional batch-mode access models for high-performance computing are not amenable to real-time analysis in larger workflows. We present a model for real-time data-intensive hypothesis discovery process that unites parallel software applications, high-performance hardware, and visual representation of the output.

Type
Chapter
Information
Data-Intensive Computing
Architectures, Algorithms, and Applications
 , pp. 235 - 257
Publisher: Cambridge University Press
Print publication year: 2012

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