PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics

BMC Bioinformatics

Software

Martin Zackrisson¹  Jonas Warringer²  Anders Blomberg³  Luciano Fernandez-Ricaud³  Olga Kourtchenko⁴ 

[1] Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390, Göteborg, Sweden;Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390, Göteborg, Sweden;Centre for Integrative Genetics (CIGENE), Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, 1432, Ås, Norway;Department of Marine Sciences, Lundberg Laboratory, University of Gothenburg, Medicinaregatan 9c, 41390, Göteborg, Sweden;Department of Marine Sciences, University of Gothenburg, P.O. Box 461, SE 405 30, Göteborg, Sweden;

关键词: Phenomics;    Yeast;    growth;    Data pre-processing;    Fitness components;    Automation;    Data presentation;   

DOI  :  10.1186/s12859-016-1134-2

 received in 2016-01-27, accepted in 2016-06-09,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundPhenomics is a field in functional genomics that records variation in organismal phenotypes in the genetic, epigenetic or environmental context at a massive scale. For microbes, the key phenotype is the growth in population size because it contains information that is directly linked to fitness. Due to technical innovations and extensive automation our capacity to record complex and dynamic microbial growth data is rapidly outpacing our capacity to dissect and visualize this data and extract the fitness components it contains, hampering progress in all fields of microbiology.ResultsTo automate visualization, analysis and exploration of complex and highly resolved microbial growth data as well as standardized extraction of the fitness components it contains, we developed the software PRECOG (PREsentation and Characterization Of Growth-data). PRECOG allows the user to quality control, interact with and evaluate microbial growth data with ease, speed and accuracy, also in cases of non-standard growth dynamics.Quality indices filter high- from low-quality growth experiments, reducing false positives. The pre-processing filters in PRECOG are computationally inexpensive and yet functionally comparable to more complex neural network procedures. We provide examples where data calibration, project design and feature extraction methodologies have a clear impact on the estimated growth traits, emphasising the need for proper standardization in data analysis.ConclusionsPRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases.

【 授权许可】

CC BY   
© The Author(s). 2016

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