PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics. Issue 1 (December 2016)
- Record Type:
- Journal Article
- Title:
- PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics. Issue 1 (December 2016)
- Main Title:
- PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics
- Authors:
- Fernandez-Ricaud, Luciano
Kourtchenko, Olga
Zackrisson, Martin
Warringer, Jonas
Blomberg, Anders - Abstract:
- Abstract Background Phenomics 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. Results To 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.Abstract Background Phenomics 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. Results To 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. Conclusions PRECOG is a tool that streamlines growth data pre-processing, phenotypic trait extraction, visualization, distribution and the creation of vast and informative phenomics databases. … (more)
- Is Part Of:
- BMC bioinformatics. Volume 17:Issue 1(2016)
- Journal:
- BMC bioinformatics
- Issue:
- Volume 17:Issue 1(2016)
- Issue Display:
- Volume 17, Issue 1 (2016)
- Year:
- 2016
- Volume:
- 17
- Issue:
- 1
- Issue Sort Value:
- 2016-0017-0001-0000
- Page Start:
- 1
- Page End:
- 15
- Publication Date:
- 2016-12
- Subjects:
- Phenomics -- Yeast -- growth -- Data pre-processing -- Fitness components -- Automation -- Data presentation
Bioinformatics -- Periodicals
Computational biology -- Periodicals
570.285 - Journal URLs:
- http://www.biomedcentral.com/bmcbioinformatics/ ↗
http://www.pubmedcentral.nih.gov/tocrender.fcgi?journal=13 ↗
http://link.springer.com/ ↗ - DOI:
- 10.1186/s12859-016-1134-2 ↗
- Languages:
- English
- ISSNs:
- 1471-2105
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
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British Library HMNTS - ELD Digital store - Ingest File:
- 9952.xml