[show abstract][hide abstract] ABSTRACT: Genetic screens for phenotypic similarity have made key contributions to associating genes with biological processes. With RNA interference (RNAi), highly parallel phenotyping of loss-of-function effects in cells has become feasible. One of the current challenges however is the computational categorization of visual phenotypes and the prediction of biological function and processes. In this study, we describe a combined computational and experimental approach to discover novel gene functions and explore functional relationships. We performed a genome-wide RNAi screen in human cells and used quantitative descriptors derived from high-throughput imaging to generate multiparametric phenotypic profiles. We show that profiles predicted functions of genes by phenotypic similarity. Specifically, we examined several candidates including the largely uncharacterized gene DONSON, which shared phenotype similarity with known factors of DNA damage response (DDR) and genomic integrity. Experimental evidence supports that DONSON is a novel centrosomal protein required for DDR signalling and genomic integrity. Multiparametric phenotyping by automated imaging and computational annotation is a powerful method for functional discovery and mapping the landscape of phenotypic responses to cellular perturbations.
Molecular Systems Biology 06/2010; 6:370. · 11.34 Impact Factor
[show abstract][hide abstract] ABSTRACT: SUMMARY: EBImage provides general purpose functionality for reading, writing, processing and analysis of images. Furthermore, in the context of microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and use of existing tools in the R environment for signal processing, statistical modeling, machine learning and data visualization. AVAILABILITY: EBImage is free and open source, released under the LGPL license and available from the Bioconductor project (http://www.bioconductor.org/packages/release/bioc/html/EBImage.html).
[show abstract][hide abstract] ABSTRACT: The R-package EBImage provides functionality to perform image processing and image analysis on large sets of images in a programmatic fashion using the R language. We use the term image analysis to describe the extraction of numeric features (image descriptors) from images and image collections [Russ2002]. Image descriptors can then be used for statistical analysis, such as classification, clustering and hypothesis testing, using the resources of R and its contributed packages. Image analysis is not an easy task, and the definition of image descriptors depends on the problem. Analysis algorithms need to be adapted correspondingly. We find it desirable to develop and optimize such algorithms in conjunction with the subsequent statistical analysis, rather than as separate tasks. This is one of our motivations for writing EBImage as an R package. We use the term image processing for operations that turn images into images with the goals of enhancing, manipulating, sharpening, denoising or similar [Russ2002]. While some image processing is often needed as a preliminary step for image analysis, image processing is not the primary goal of the package. We focus on methods that do not require interactive user input, such as selecting image regions with a pointer etc. Whereas interactive methods can be very effective for small sets of images, they tend to have limited throughput and reproducibility. EBImage uses the [ImageMagick] library API's to implement much of its functionality in image processing and input/output operations. Cell-based assays Advances in automated microscopy have made it possible to conduct large scale cell-based assays with image-type phenotypic readouts. In such an assay, cells are grown in the wells of a microtitre 1 plate (often a 96-or 384-well format is used) under a condition or stimulus of interest. Each well is treated with one of the reagents from the screening library and the cells' response is monitored, for which in many cases certain proteins of interest are antibody-stained or labeled with a GFP-tag [Carpenter2004, Wiemann2004, Moffat2006, Neumann2006]. The resulting imaging data can be in the form of two-dimensional (2D) still images, three-dimensional (3D) image stacks or image-based time courses. Such assays can be used to screen compound libraries for the effect of potential drugs on the cellular system of interest. Similarly, RNA interference (RNAi) libraries can be used to screen a set of genes (in many cases the whole genome) for the effect of their loss of function in a certain biological process [Boutros2004].
[show abstract][hide abstract] ABSTRACT: MOTIVATION: CoCo, ChIP-on-Chip online, is an open-source web application that supports the annotation and curation of regulatory regions and associated target genes discovered in ChIP-on-chip experiments. CoCo integrates ChIP-on-chip results with diverse types of gene expression data (expression profiling, in situ hybridization) and displays them within a genomic context. Regulatory relationships between the transcription factor-bound regions and putative target genes can be stored and expanded throughout different sessions. AVAILABILITY: http://furlonglab.embl.de/methods/tools/coco.
[show abstract][hide abstract] ABSTRACT: Chromatin immunoprecipitation combined with DNA microarrays (ChIP-chip) is a high-throughput assay for DNA-protein-binding or post-translational chromatin/histone modifications. However, the raw microarray intensity readings themselves are not immediately useful to researchers, but require a number of bioinformatic analysis steps. Identified enriched regions need to be bioinformatically annotated and compared to related datasets by statistical methods.
We present a free, open-source R package Ringo that facilitates the analysis of ChIP-chip experiments by providing functionality for data import, quality assessment, normalization and visualization of the data, and the detection of ChIP-enriched genomic regions.
Ringo integrates with other packages of the Bioconductor project, uses common data structures and is accompanied by ample documentation. It facilitates the construction of programmed analysis workflows, offers benefits in scalability, reproducibility and methodical scope of the analyses and opens up a broad selection of follow-up statistical and bioinformatic methods.