Adaptive Laboratory Evolution (ALE) methodologies are used for studying microbial adaptive mutations that optimize host metabolism. The Systems Biology Research Group (SBRG) at the University of California, San Diego, has implemented high-throughput ALE experiment automation that enables the group to expand their experimental evolutions to scales previously infeasible with manual workflows. The data generated by the high-throughput automation now requires a post-processing, content management and analysis framework that can operate on the same scale. We developed a software system which solves the SBRG's specific ALE big data to knowledge challenges. The software system is comprised of a post-processing protocol for quality control, a software framework and database for data consolidation and a web platform named ALE Analytics for report generation and automated key mutation analysis. The automated key mutation analysis is evaluated against published ALE experiment key mutation results from the SBRG and maintains an average recall of 89.6% and an average precision of 71.2%. The consolidation of all ALE experiments into a unified resource has enabled the development of web applications that compare key mutations across multiple experiments. These features find the genomic regions rph, hns/tdk, rpoB, rpoC and pykF mutated in more than one ALE experiment published by the SBRG. We reason that leveraging this software system relieves the bottleneck in ALE experiment analysis and generates new data mining opportunities for research in understanding system-level mechanisms that govern adaptive evolution.
|Commitee:||Bafna, Vineet, Pevzner, Pavel|
|School:||University of California, San Diego|
|School Location:||United States -- California|
|Source:||MAI 56/02M(E), Masters Abstracts International|
|Subjects:||Evolution and Development, Systems science, Computer science|
|Keywords:||Adaptive laboratory evolution, Big data to knowledge, Experimental evolution|
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