Automates visualization, analysis and exploration of complex and highly resolved microbial 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. It filters the raw optical densities in a three-step procedure to remove or reduce noise and bias. PREGOG’s data filtering has been tested by comparing its performance to a multi-layered neural networks noise reduction filter.

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PRECOG forum

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PRECOG classification

PRECOG specifications

Software type:
Standalone
Restrictions to use:
None
Input format:
XL~, CSV, TXT
Programming languages:
C#
Computer skills:
Medium
Requirements:
Microsoft Dot Net framework , Windows Installer
Source code URL:
https://github.com/Endimion75/PRECOG/archive/master.zip
Interface:
Graphical user interface
Input data:
growth-data to process
Operating system:
Windows
License:
Other
Stability:
Stable

User report

0 user reviews

0 user reviews

No review has been posted.

PRECOG forum

No open topic.

PRECOG classification

PRECOG specifications

Software type:
Package
Restrictions to use:
None
Input format:
XL~, CSV, TXT
License:
Other
Stability:
Stable
Source code URL:
https://github.com/Endimion75/PRECOG/archive/master.zip
Interface:
Web user interface
Input data:
growth-data to process
Programming languages:
C#
Computer skills:
Basic
Requirements:
Microsoft Dot Net framework

Publications

  • (Fernandez-Ricaud et al., 2016) PRECOG: a tool for automated extraction and visualization of fitness components in microbial growth phenomics. BMC Bioinformatics.
    PMID: 27334112

PRECOG support

Additional informations

http://precog.lundberg.gu.se/Pages/Content/Tutorial

Credits

Institution(s)

Department of Marine Sciences, University of Gothenburg, Göteborg, Sweden; Department of Cell and Molecular Biology, Lundberg Laboratory, University of Gothenburg, Göteborg, Sweden; Centre for Integrative Genetics, Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway

Funding source(s)

This tool was funded by the Swedish Research Council FORMAS (216-2013-549) and from the national Bioinformatics in Life Sciences.

Link to literature

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