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Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
Experimental techniques that survey an entire genome demand flexible, highly interactive visualization tools that can display new data alongside foundation datasets, such as reference gene annotations. The Integrated Genome Browser (IGB) aims to meet this need. IGB is an open source, desktop graphical display tool implemented in Java that supports real-time zooming and panning through a genome; layout of genomic features and datasets in moveable, adjustable tiers; incremental or genome-scale data loading from remote web servers or local files; and dynamic manipulation of quantitative data via genome graphs.
Availability: The application and source code are available from http://igb.bioviz.org and http://genoviz.sourceforge.net.
Contact: aloraine@uncc.edu
The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms
are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively
on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene
set on the GO hierarchy, and takes advantage of Cytoscape's versatile visualization environment to produce an intuitive and
customizable visual representation of the results.
Availability: http://www.psb.ugent.be/cbd/papers/BiNGO/
Contact: martin.kuiper{at}psb.ugent.be
The presence of Set2-mediated methylation of H3K36 (K36me) correlates with transcription frequency throughout the yeast genome. K36me targets the Rpd3S complex to deacetylate transcribed regions and suppress cryptic transcription initiation at certain genes. Here, using a genome-wide approach, we report that the Set2-Rpd3S pathway is generally required for controlling acetylation at coding regions. When using acetylation as a functional readout for this pathway, we discovered that longer genes and, surprisingly, genes transcribed at lower frequency exhibit a stronger dependency. Moreover, a systematic screen using high-resolution tiling microarrays allowed us to identify a group of genes that rely on Set2-Rpd3S to suppress spurious transcripts. Interestingly, most of these genes are within the group that depend on the same pathway to maintain a hypoacetylated state at coding regions. These data highlight the importance of using the functional readout of histone codes to define the roles of specific pathways.
Motivation:
When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations.
Results:
We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably.
Availability:
Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org.
Supplementary information:
Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11-20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.
Eukaryotic genomes are packaged into nucleosomes whose position and chemical modification state can profoundly influence regulation of gene expression. We profiled nucleosome modifications across the yeast genome using chromatin immunoprecipitation coupled with DNA microarrays to produce high-resolution genome-wide maps of histone acetylation and methylation. These maps take into account changes in nucleosome occupancy at actively transcribed genes and, in doing so, revise previous assessments of the modifications associated with gene expression. Both acetylation and methylation of histones are associated with transcriptional activity, but the former occurs predominantly at the beginning of genes, whereas the latter can occur throughout transcribed regions. Most notably, specific methylation events are associated with the beginning, middle, and end of actively transcribed genes. These maps provide the foundation for further understanding the roles of chromatin in gene expression and genome maintenance.