High content RNAi screens require a lot of software development for image analysis, data handling, integration and presentation. Below is an overview of some of the software developed by us and by others that have helped us to perform these screens. Most of them are opensource, and free for academic use.
If you have any questions regarding the software made by us, you can contact us directly or leave a message on the software support forum.
CellProfiler v1 modules
CellProfiler (http://www.cellprofiler.org/) is free open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. The CellProfiler project is based at the Broad Institute Imaging Platform. To enable the automated analysis and correction of population context effects by the image based screening community we have created two additional modules compatible with CellProfiler v1 called MeasurePopulationContext and PopulationContextCorrect.
Cell Classifier supports supervised machine learning on cellular phenotypes identified in high-throughput high-content screens (HCS) using CellProfiler. CellClassifier was originally developed by Paui Ramo and others at the Pelkmans lab. It has an intuitive and easy-to-use graphical user interface developed in MatLab to upload and visualize microscopy images. CellClassifier displays each original image interactively, so that each cell is recognized and made 'clickable'. Each cell can therefore be easily annotated, rare phenotypes within a population can be found, and it can be observed if single-cell phenotypes depend on the population context of that cell.
iBRAIN is an acronym for image-Based RNAi and is developed by Berend Snijder as a middle ware between image analysis software, content management of siRNA libraries, the storage of large sets of microscopy images, the distribution of computation jobs on large computer clusters, and the harvesting and visualization of obtained results. It contains numerous meta-information on genes and proteins, obtained from ontology databases and STRING. It also incorporates probabilistic algorithms to determine the true loss-of-function phenotype of a gene from measurements of multiple siRNAs targeting the same gene. Some of these algorithms have been developed by us, and will be published in the future. iBRAIN also provides a webbrowser-compatible user interface with which the progress in computational analysis can be easily monitored for each running project.
openBIS is an open, distributed system for managing biological information. The main goal is to support biological research data workflows from the source (i.e. the measurement instruments) to facilitate the process of answering biological questions by means of cross-domain queries against raw data, processed data, knowledge resources and its corresponding metadata. The openBIS software framework can be easily extended and has so far been customized for high-content screening, proteomics, deep-sequencing and metabolomics.