CiteULike: jyuh imaging

BioGraphE: high-performance bionetwork analysis using the Biological Graph Environment

George Chin — 2008-06-28T12:39:39-00:00

BMC Bioinformatics, Vol. 9, No. Suppl 6. (2008)

BACKGROUND:Graphs and networks are common analysis representations for biological systems. Many traditional graph algorithms such as k-clique, k-coloring, and subgraph matching have great potential as analysis techniques for newly available data in biology. Yet, as the amount of genomic and bionetwork information rapidly grows, scientists need advanced new computational strategies and tools for dealing with the complexities of the bionetwork analysis and the volume of the data.RESULTS:We introduce a computational framework for graph analysis called the Biological Graph Environment (BioGraphE), which provides a general, scalable integration platform for connecting graph problems in biology to optimized computational solvers and high-performance systems. This framework enables biology researchers and computational scientists to identify and deploy network analysis applications and to easily connect them to efficient and powerful computational software and hardware that are specifically designed and tuned to solve complex graph problems. In our particular application of BioGraphE to support network analysis in genome biology, we investigate the use of a Boolean satisfiability solver known as Survey Propagation as a core computational solver executing on standard high-performance parallel systems, as well as multi-threaded architectures.CONCLUSION:In our application of BioGraphE to conduct bionetwork analysis of homology networks, we found that BioGraphE and a custom, parallel implementation of the Survey Propagation SAT solver were capable of solving very large bionetwork problems at high rates of execution on different high-performance computing platforms.

A Framework for the Automated Analysis of Subcellular Patterns in Human Protein Atlas Images

Justin Newberg — 2008-04-26T04:07:50-00:00

J. Proteome Res. (25 April 2008)

Abstract: The systematic study of subcellular location patterns is required to fully characterize the human proteome, as subcellular location provides critical context necessary for understanding a proteins function. The analysis of tens of thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates a need for automated subcellular pattern analysis. We therefore describe the application of automated methods, previously developed and validated by our laboratory on fluorescence micrographs of cultured cell lines, to analyze subcellular patterns in tissue images from the Human Protein Atlas. The Atlas currently contains images of over 3000 protein patterns in various human tissues obtained using immunohistochemistry. We chose a 16 protein subset from the Atlas that reflects the major classes of subcellular location. We then separated DNA and protein staining in the images, extracted various features from each image, and trained a support vector machine classifier to recognize the protein patterns. Our results show that our system can distinguish the patterns with 83% accuracy in 45 different tissues, and when only the most confident classifications are considered, this rises to 97%. These results are encouraging given that the tissues contain many different cell types organized in different manners, and that the Atlas images are of moderate resolution. The approach described is an important starting point for automatically assigning subcellular locations on a proteome-wide basis for collections of tissue images such as the Atlas.

Toward a confocal subcellular atlas of the human proteome.

L Barbe — 2008-07-03T08:25:06-00:00

Molecular & cellular proteomics : MCP, Vol. 7, No. 3. (March 2008), pp. 499-508.

Information on protein localization on the subcellular level is important to map and characterize the proteome and to better understand cellular functions of proteins. Here we report on a pilot study of 466 proteins in three human cell lines aimed to allow large scale confocal microscopy analysis using protein-specific antibodies. Approximately 3000 high resolution images were generated, and more than 80% of the analyzed proteins could be classified in one or multiple subcellular compartment(s). The localizations of the proteins showed, in many cases, good agreement with the Gene Ontology localization prediction model. This is the first large scale antibody-based study to localize proteins into subcellular compartments using antibodies and confocal microscopy. The results suggest that this approach might be a valuable tool in conjunction with predictive models for protein localization.

GGT 2.0: versatile software for visualization and analysis of genetic data.

R van Berloo — 2008-06-25T06:38:13-00:00

The Journal of heredity, Vol. 99, No. 2. (r 2008), pp. 232-236.

Ever since its first release in 1999, the free software package for visualization of molecular marker data, graphical genotype (GGT), has been constantly adapted and improved. The GGT package was developed in a plant-breeding context and thus focuses on plant genetic data but was not intended to be limited to plants only. The current version has many options for genetic analysis of populations including diversity analyses and simple association studies. A second release of the GGT package, GGT 2.0 (available through http://www.plantbreeding.wur.nl), is therefore presented in this paper. An overview of existing and new features that are available within GGT 2.0, and a case study in which GGT 2.0 is applied to analyze an existing set of plant genetic data, are presented and discussed.

An image score inference system for RNAi genome-wide screening based on fuzzy mixture regression modeling.

Jun Wang — 2008-06-25T06:07:02-00:00

Journal of biomedical informatics (29 April 2008)

With recent advances in fluorescence microscopy imaging techniques and methods of gene knock down by RNA interference (RNAi), genome-scale high-content screening (HCS) has emerged as a powerful approach to systematically identify all parts of complex biological processes. However, a critical barrier preventing fulfillment of the success is the lack of efficient and robust methods for automating RNAi image analysis and quantitative evaluation of the gene knock down effects on huge volume of HCS data. Facing such opportunities and challenges, we have started investigation of automatic methods towards the development of a fully automatic RNAi-HCS system. Particularly important are reliable approaches to cellular phenotype classification and image-based gene function estimation. We have developed a HCS analysis platform that consists of two main components: fluorescence image analysis and image scoring. For image analysis, we used a two-step enhanced watershed method to extract cellular boundaries from HCS images. Segmented cells were classified into several predefined phenotypes based on morphological and appearance features. Using statistical characteristics of the identified phenotypes as a quantitative description of the image, a score is generated that reflects gene function. Our scoring model integrates fuzzy gene class estimation and single regression models. The final functional score of an image was derived using the weighted combination of the inference from several support vector-based regression models. We validated our phenotype classification method and scoring system on our cellular phenotype and gene database with expert ground truth labeling. We built a database of high-content, 3-channel, fluorescence microscopy images of Drosophila Kc(167) cultured cells that were treated with RNAi to perturb gene function. The proposed informatics system for microscopy image analysis is tested on this database. Both of the two main components, automated phenotype classification and image scoring system, were evaluated. The robustness and efficiency of our system were validated in quantitatively predicting the biological relevance of genes.

Using iterative cluster merging with improved gap statistics to perform online phenotype discovery in the context of high-throughput RNAi screens

Zheng Yin — 2008-06-06T07:33:06-00:00

BMC Bioinformatics, Vol. 9 (05 June 2008), 264.

Constraint factor graph cutbased active contour method for automated cellular image segmentation in RNAi screening

C Chen — 2008-04-29T09:12:54-00:00

Journal of Microscopy, Vol. 230, No. 2. (May 2008), pp. 177-191.

Biochemistry. A postgenomic visual icon.

JN Weinstein — 2008-06-25T05:39:48-00:00

Science (New York, N.Y.), Vol. 319, No. 5871. (28 March 2008), pp. 1772-1773.

JColorGrid: software for the visualization of biological measurements.

MP Joachimiak — 2008-06-13T12:06:32-00:00

BMC bioinformatics, Vol. 7 (2006)

BACKGROUND: Two-dimensional data colourings are an effective medium by which to represent three-dimensional data in two dimensions. Such "color-grid" representations have found increasing use in the biological sciences (e.g. microarray 'heat maps' and bioactivity data) as they are particularly suited to complex data sets and offer an alternative to the graphical representations included in traditional statistical software packages. The effectiveness of color-grids lies in their graphical design, which introduces a standard for customizable data representation. Currently, software applications capable of generating limited color-grid representations can be found only in advanced statistical packages or custom programs (e.g. micro-array analysis tools), often associated with steep learning curves and requiring expert knowledge. RESULTS: Here we describe JColorGrid, a Java library and platform independent application that renders color-grid graphics from data. The software can be used as a Java library, as a command-line application, and as a color-grid parameter interface and graphical viewer application. Data, titles, and data labels are input as tab-delimited text files or Microsoft Excel spreadsheets and the color-grid settings are specified through the graphical interface or a text configuration file. JColorGrid allows both user graphical data exploration as well as a means of automatically rendering color-grids from data as part of research pipelines. CONCLUSION: The program has been tested on Windows, Mac, and Linux operating systems, and the binary executables and source files are available for download at http://jcolorgrid.ucsf.edu.

Towards zoomable multidimensional maps of the cell

Zhenjun Hu — 2007-05-05T13:02:17-00:00

Nature Biotechnology, Vol. 25, No. 5. (04 May 2007), pp. 547-554.

From the Cover: A drug-controllable tag for visualizing newly synthesized proteins in cells and whole animals

Michael Lin — 2008-06-10T10:20:00-00:00

Proceedings of the National Academy of Sciences, Vol. 105, No. 22. (3 June 2008), pp. 7744-7749.

Research on basic cellular processes involving local production or delivery of proteins, such as activity-dependent synaptic modification in neurons, would benefit greatly from a robust, nontoxic method to visualize selectively newly synthesized copies of proteins of interest within cells, tissues, or animals. We report a technique for covalent labeling of newly synthesized proteins of interest based on drug-dependent preservation of epitope tags. Epitope tags are removed from proteins of interest immediately after translation by the activity of a sequence-specific protease until the time a protease inhibitor is added, after which newly synthesized protein copies retain their tags. This method, which we call TimeSTAMP for time-specific tagging for the age measurement of proteins, allows sensitive and nonperturbative visualization and quantification of newly synthesized proteins of interest with exceptionally tight temporal control. We demonstrate applications of TimeSTAMP in retrospectively identifying growing synapses in cultured neurons and in visualizing the distribution of recently synthesized proteins in intact fly brains. 10.1073/pnas.0803060105

Demonstration of epithelial-mesenchymal transition in kidney. The contribution of a coupled histochemical and immunohistochemical staining with Periodic Acid-thionin Schiff.

MC Santos — 2008-06-06T02:30:24-00:00

Applied immunohistochemistry & molecular morphology : AIMM / official publication of the Society for Applied Immunohistochemistry, Vol. 16, No. 2. (March 2008), pp. 191-195.

Traditional Periodic Acid Schiff has been extensively used, coupled with immunohistochemistry for epithelia or mesenchymal cells, to highlight renal tubular basement membrane (TBM). We recently tried to perform such technique in a 5/6 nephrectomy model of progressive renal fibrosis to demonstrate TBM disruption as an evidence for epithelial-mesenchymal transdifferentiation. Despite excellent basement membrane staining with traditional fuchsin-Periodic Acid Schiff, the interface between epithelial and mesenchymal cells was frequently blurred when revealed with 3'3 diaminobenzidine tetrachloride-peroxidase. Also, it was inadequate when revealed with alkaline phosphatase-fast red. We devised a triple staining method with Periodic Acid-Thionin Schiff to highlight basement membrane in blue, after double immunostaining for epithelium and mesenchymal cells. Blue basement membrane rendered a brisk contrast and highlighted boundaries between epithelial-mesenchymal interfaces. This method was easy to perform and useful to demonstrate the TBM, yield a clear demonstration of the very focal TBM disruption found in this model of progressive renal fibrosis.

Merging molecular imaging and RNA interference: Early experience in live animals.

Alexei A Bogdanov — 2008-06-01T16:36:07-00:00

Journal of cellular biochemistry (4 February 2008)

The rapid development of non-invasive imaging techniques and imaging reporters coincided with the enthusiastic response that the introduction of RNA interference (RNAi) techniques created in the research community. Imaging in experimental animals provides quantitative or semi-quantitative information regarding the biodistribution of small interfering RNAs and the levels of gene interference (i.e., knockdown of the target mRNA) in living animals. In this review we give a brief summary of the first imaging findings that have potential for accelerating the development and testing of new approaches that explore RNAi as a method for achieving loss-of-function effects in vivo and as a promising therapeutic tool. J. Cell. Biochem. (c) 2008 Wiley-Liss, Inc.

jSquid: a Java applet for graphical on-line network exploration.

Martin Klammer — 2008-05-04T12:03:32-00:00

Bioinformatics (Oxford, England) (29 April 2008)

SUMMARY: jSquid is a graph visualization tool for exploring graphs from protein-protein interaction or functional coupling networks. The tool was designed for the FunCoup web site, but can be used for any similar network exploring purpose. The program offers various visualization and graph manipulation techniques to increase the utility for the user. AVAILABILITY: jSquid is available for direct usage and download at http://jSquid.sbc.su.se including source code under the GPLv3 license, and input examples. It requires Java version 5 or higher to run properly. CONTACT: erik.sonnhammer@sbc.su.se SUPPLEMENTARY INFORMATION: available at Bioinformatics online.

GraphFind: enhancing graph searching by low support data mining techniques.

A Ferro — 2008-05-12T12:07:35-00:00

BMC bioinformatics, Vol. 9 Suppl 4 (2008)

BACKGROUND: Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these graph databases, a key role is played by systems that search for all exact or approximate occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed. RESULTS: This paper presents GraphFind. The system implements efficient graph searching algorithms together with advanced filtering techniques that allow approximate search. It allows users to select candidate subgraphs rather than entire graphs. It implements an effective data storage based also on low-support data mining. CONCLUSIONS: GraphFind is compared with Frowns, GraphGrep and gIndex. Experiments show that GraphFind outperforms the compared systems on a very large collection of small graphs. The proposed low-support mining technique which applies to any searching system also allows a significant index space reduction.

VariVis: a visualisation toolkit for variation databases

Timothy Smith — 2008-04-23T07:16:11-00:00

BMC Bioinformatics, Vol. 9 (23 April 2008), 206.

Cytoscape ESP: simple search of complex biological networks.

Maital Ashkenazi — 2008-05-04T12:03:46-00:00

Bioinformatics (Oxford, England) (28 April 2008)

SUMMARY: Cytoscape ESP enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. AVAILABILITY: http://conklinwolf.ucsf.edu/genmappwiki/Google_Summer_of_Code_2007/Maital CONTACT: ashkenaz@agri.huji.ac.il.

Visualizing data

2008-05-16T01:46:42-00:00

GenomeVx: simple web-based creation of editable circular chromosome maps.

GC Conant — 2008-05-13T04:27:39-00:00

Bioinformatics (Oxford, England), Vol. 24, No. 6. (15 March 2008), pp. 861-862.

We describe GenomeVx, a web-based tool for making editable, publication-quality, maps of mitochondrial and chloroplast genomes and of large plasmids. These maps show the location of genes and chromosomal features as well as a position scale. The program takes as input either raw feature positions or GenBank records. In the latter case, features are automatically extracted and colored, an example of which is given. Output is in the Adobe Portable Document Format (PDF) and can be edited by programs such as Adobe Illustrator. AVAILABILITY: GenomeVx is available at http://wolfe.gen.tcd.ie/GenomeVx

Computerized image analysis as a tool to quantify infiltrating leukocytes: a comparison between high- and low-magnification images.

AC Johansson — 2008-05-08T15:50:25-00:00

The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society, Vol. 49, No. 9. (September 2001), pp. 1073-1079.

The purpose of the present study was to establish a rapid and reproducible method for quantification of tissue-infiltrating leukocytes using computerized image analysis. To achieve this, the staining procedure, the image acquisition, and the image analysis method were optimized. Because of the adaptive features of the human eye, computerized image analysis is more sensitive to variations in staining compared with manual image analysis. To minimize variations in staining, an automated immunostainer was used. With a digital scanner camera, low-magnification images could be sampled at high resolution, thus making it possible to analyze larger tissue sections. Image analysis was performed by color thresholding of the digital images based on values of hue, saturation, and intensity color mode, which we consider superior to the red, green, and blue color mode for analysis of most histological stains. To evaluate the method, we compared computerized analysis of images with a x100 or a x12.5 magnification to assess leukocytes infiltrating rat brain tumors after peripheral immunizations with tumor cells genetically modified to express rat interferon-gamma (IFN-gamma) or medium controls. The results generated by both methods correlated well and did not show any significant differences. The method allows efficient and reproducible processing of large tissue sections that is less time-consuming than conventional methods and can be performed with standard equipment and software.(J Histochem Cytochem 49:1073-1079, 2001)

Automated selection of DAB-labeled tissue for immunohistochemical quantification.

EM Brey — 2008-05-08T15:44:13-00:00

The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society, Vol. 51, No. 5. (May 2003), pp. 575-584.

The increased use of immunohistochemistry (IHC) in both clinical and basic research settings has led to the development of techniques for acquiring quantitative information from immunostains. Staining correlates with absolute protein levels and has been investigated as a clinical tool for patient diagnosis and prognosis. For these reasons, automated imaging methods have been developed in an attempt to standardize IHC analysis. We propose a novel imaging technique in which brightfield images of diaminobenzidene (DAB)-labeled antigens are converted to normalized blue images, allowing automated identification of positively stained tissue. A statistical analysis compared our method with seven previously published imaging techniques by measuring each one's agreement with manual analysis by two observers. Eighteen DAB-stained images showing a range of protein levels were used. Accuracy was assessed by calculating the percentage of pixels misclassified using each technique compared with a manual standard. Bland-Altman analysis was then used to show the extent to which misclassification affected staining quantification. Many of the techniques were inconsistent in classifying DAB staining due to background interference, but our method was statistically the most accurate and consistent across all staining levels.

Quantitative immunohistochemistry by measuring cumulative signal strength using commercially available software photoshop and matlab.

KA Matkowskyj — 2006-11-28T08:46:22-00:00

J Histochem Cytochem, Vol. 48, No. 2. (February 2000), pp. 303-312.

Currently available techniques for performing quantitative immunohistochemistry (Q-IHC) rely upon pixel-counting algorithms and therefore cannot provide information as to the absolute amount of chromogen present. We describe a novel algorithm for true Q-IHC based on calculating the cumulative signal strength, or energy, of the digital file representing any portion of an image. This algorithm involves subtracting the energy of the digital file encoding the control image (i.e., not exposed to antibody) from that of the experimental image (i.e., antibody-treated). In this manner, the absolute amount of antibody-specific chromogen per pixel can be determined for any cellular region or structure. (J Histochem Cytochem 48:303-311, 2000)

Alignment of LC-MS images, with applications to biomarker discovery and protein identification.

M Vandenbogaert — 2008-04-01T03:53:02-00:00

Proteomics, Vol. 8, No. 4. (February 2008), pp. 650-672.

LC-MS-based approaches have gained considerable interest for the analysis of complex peptide or protein mixtures, due to their potential for full automation and high sampling rates. Advances in resolution and accuracy of modern mass spectrometers allow new analytical LC-MS-based applications, such as biomarker discovery and cross-sample protein identification. Many of these applications compare multiple LC-MS experiments, each of which can be represented as a 2-D image. In this article, we survey current approaches to LC-MS image alignment. LC-MS image alignment corrects for experimental variations in the chromatography and represents a computational key technology for the comparison of LC-MS experiments. It is a required processing step for its two major applications: biomarker discovery and protein identification. Along with descriptions of the computational analysis approaches, we discuss their relative merits and potential pitfalls.

Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline

Andrew Dowsey — 2008-03-28T04:50:20-00:00

Bioinformatics, Vol. 24, No. 7. (1 April 2008), pp. 950-957.

Motivation: The quest for high-throughput proteomics has revealed a number of challenges in recent years. Whilst substantial improvements in automated protein separation with liquid chromatography and mass spectrometry (LC/MS), aka shotgun' proteomics, have been achieved, large-scale open initiatives such as the Human Proteome Organization (HUPO) Brain Proteome Project have shown that maximal proteome coverage is only possible when LC/MS is complemented by 2D gel electrophoresis (2-DE) studies. Moreover, both separation methods require automated alignment and differential analysis to relieve the bioinformatics bottleneck and so make high-throughput protein biomarker discovery a reality. The purpose of this article is to describe a fully automatic image alignment framework for the integration of 2-DE into a high-throughput differential expression proteomics pipeline. Results: The proposed method is based on robust automated image normalization (RAIN) to circumvent the drawbacks of traditional approaches. These use symbolic representation at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in modelling and alignment. In RAIN, a third-order volume-invariant B-spline model is incorporated into a multi-resolution schema to correct for geometric and expression inhomogeneity at multiple scales. The normalized images can then be compared directly in the image domain for quantitative differential analysis. Through evaluation against an existing state-of-the-art method on real and synthetically warped 2D gels, the proposed analysis framework demonstrates substantial improvements in matching accuracy and differential sensitivity. High-throughput analysis is established through an accelerated GPGPU (general purpose computation on graphics cards) implementation. Availability: Supplementary material, software and images used in the validation are available at http://www.proteomegrid.org/rain/ Contact: g.z.yang@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btn059

High throughput microscopy: from raw images to discoveries.

R Wollman — 2008-01-18T02:07:39-00:00

J Cell Sci, Vol. 120, No. Pt 21. (1 November 2007), pp. 3715-3722.

Technological advances in automated microscopy now allow rapid acquisition of many images without human intervention, images that can be used for large-scale screens. The main challenge in such screens is the conversion of the raw images into interpretable information and hence discoveries. This post-acquisition component of image-based screens requires computational steps to identify cells, choose the cells of interest, assess their phenotype, and identify statistically significant 'hits'. Designing such an analysis pipeline requires careful consideration of the necessary hardware and software components, image analysis, statistical analysis and data presentation tools. Given the increasing availability of such hardware and software, these types of experiments have come within the reach of individual labs, heralding many interesting new ways of acquiring biological knowledge.

Quantitative image analysis of immunohistochemical stains using a CMYK color model

Nhu-An Pham — 2007-02-28T21:28:23-00:00

Diagnostic Pathology, Vol. 2 (27 February 2007), 8.

A web-based tool for in silico biomarker discovery based on tissue-specific protein profiles in normal and cancer tissues.

Erik Björling — 2007-10-11T02:22:15-00:00

Mol Cell Proteomics (3 October 2007)

Here we report the development of a publicly available web-based analysis tool for exploring proteins expressed in a tissue- or cancer-specific manner. The search queries are based on the human tissue profiles in normal and cancer cells in the Human Protein Atlas portal and rely on the individual annotation performed by pathologists of images representing immunohistochemically stained tissue sections. Approximately 1.8 million images representing more than 3000 antibodies directed towards human proteins were used in the study. The search tool allows for the systematic exploration of the protein atlas, to discover potential protein biomarkers. Such biomarkers include tissue specific markers, cell type specific markers, tumor type specific markers, markers of malignancy and prognostic or predictive markers of cancers. Here we show examples of database queries to generate sets of candidate biomarker proteins for several of these different categories. Expression profiles of candidate proteins can then subsequently be validated by examination of the underlying high-resolution images. The present study shows examples of search strategies revealing several potential protein biomarkers, including proteins specifically expressed in normal cells and in cancer cells from specified tumor types. The lists of candidate proteins can be used as a starting point for further validation in larger patient cohorts using both immunological approaches and technologies employing more classical proteomics tools.

Graphs in molecular biology.

W Huber — 2007-10-03T06:55:05-00:00

BMC Bioinformatics, Vol. 8 Suppl 6 (2007)

ABSTRACT : Graph theoretical concepts are useful for the description and analysis of interactions and relationships in biological systems. We give a brief introduction into some of the concepts and their areas of application in molecular biology. We discuss software that is available through the Bioconductor project and present a simple example application to the integration of a protein-protein interaction and a co-expression network.

Visualization of three-way comparisons of omics data

Richard Baran — 2007-03-05T13:09:13-00:00

BMC Bioinformatics, Vol. 8 (05 March 2007), 72.

GOlorize: a Cytoscape plug-in for network visualization with Gene Ontology-based layout and coloring.

O Garcia — 2007-09-13T12:51:56-00:00

Bioinformatics, Vol. 23, No. 3. (1 February 2007), pp. 394-396.

We have implemented a graph layout algorithm that exposes Gene Ontology (GO) class structure on the network nodes. It can be used in conjunction with BiNGO plug-in to Cytoscape, which finds the GO categories over-represented in a given network. Our plug-in, named GOlorize, first highlights the class members with category-specific color-coding and then constructs an enhanced visualization of the network using a class-directed layout algorithm. Availability: http://www.cytoscape.org/plugins2.php. Supplementary information: Installation instructions and tutorial at http://www.cytoscape.org/plugins/GOlorize/GOlorizeUserGuide.pdf.