Colonyzer is an image analysis tool for quantifying the cell density of arrays of independent micro-organism cultures growing on solid agar. It specialises in being sensitive enough to detect the presence of cultures with extremely low cell density arising after dilute liquid inoculation onto agar from photographs of plates. Identifying cultures with few cells growing on an agar surface by photography or after scanning is difficult because culture opacity might not be high enough for colour differences between agar and cells to be apparent. In order to achieve its high levels of sensitivity, it relies on sophisticated algorithms for the identification and detection of lighting gradients in the photograph, and for the detection of thin, semi-translucent developing cultures in the corrected images.
Colonyzer is suitable for high-throughput screening of libraries of yeast mutants for example, and has proven extremely useful in the estimation of phenotypes such as exponential growth rate, appropriate for genome-wide investigations of genetic interaction. Colonyzer was used for cell density estimation in Addinall et al. 2011 and Chang et al. 2011 for example. A detailed description of Colonyzer, its algorithms, the motivation for its development, and some example analysis can be found in Lawless et al. 2010, and an example of how Colonyzer fits into the Quantitative Fitness Analysis workflow can be seen in Banks et al. 2012.
Colonyzer has been developed using Python 2.7 and several Python modules: numpy, scipy, pandas, pil, matplotlib & pygame. The Colonyzer GitHub pages contain the latest development code. The Colonyzer PyPI pages include a version which is easy to install using pip. The easiest way to get up and running with Colonyzer is to install Python 2.7, install the pip package manager for Python and then use pip to install the required modules and Colonyzer itself. To install Python 2.7 under Windows or OSX, download and run the installer appropriate for your machine here. Python is included with most versions of Linux by default. Instructions for installing the package manager pip can be found here though Windows users might find these instructions clearer.
Installation under Microsoft Windows
Once both Python 2.7 and pip are installed, the required packages (including Colonyzer itself) can be installed by typing the following at the command prompt. You can typically get a command terminal in Windows by searching for the
cmd program (e.g. Start -> Search -> Cmd). Note that Windows Vista users will have to right click on the Cmd icon and Run As Administrator. Onece you have a command terminal window open, type or paste the following command and press enter at the end of the line:
pip install numpy scipy pandas pil matplotlib pygame Colonyzer2
Installation under Linux
Linux users might prefer to allow their OS package management system install the required python packages instead of pip. To do this under Debian (e.g. Ubuntu), first install pip and all required packages using the OS package management system. Then, use pip to install Colonyzer, as follows:
sudo apt-get install python-pip python-numpy python-scipy python-pandas python-matplotlib python-pygame
sudo pip install Colonyzer2
Detailed documentation will appear here in due course, but for the moment, try using the timecourse demo script as follows:
- Collect a timecourse series of photographs of microbial cultures spotted in onto a solid agar plate (some demo image files here)
- Name the photographs using the first 15 characters as a plate identifier, with further characters for identifying particular images in a series (e.g. a timestamp would be appropriate). For example images following this patten would be ideal: W000155_030_001_2009-06-30_14-36-26.jpg.
- Open a terminal window in the directory containing the images (or navigate to that directory) and type this command (followed by enter) to analyse all of the images:
- Alternatively, copy the demo script to the directory containing the timecourse images. Then execute the script to analyze all images by double-clicking on the .py file under Windows, or by executing the following at the command prompt:
Colonyzer2 is under development on github. Feel free to fork the repo and to submit pull requests. Marcin Plech has contributed many useful bugfixes to the latest version of the software.
If you use Colonyzer in research leading to a publication, please cite our open access article:
Conor Lawless, Darren J Wilkinson, Alexander Young, Stephen G Addinall and David A Lydall Colonyzer: automated quantification of micro-organism growth characteristics on solid agar BMC Bioinformatics 2010, 11:287
The original ource code, installation instructions, a library of example images, and some auxiliary scripts for analyzing batches of images as presented in Lawless et al. 2010 and used in Addinall et al. 2011 and Chang et al. 2011 and demonstrated in Banks et al. 2012 can be found on sourceforge. However, the version presented above is more current and is recommended for new users. In particular the current version is faster and easier to install and use.
The qfa R package has been developed to further analyse the data, construct growth curves and infer culture fitnesses, and is compatible with either version of Colonyzer.