CEREBRAL (CEll REgion-Based Rendering And Layout)
is a Java plugin for the open-source and widely used Cytoscape
molecular interaction viewer.
This plugin enhances Cytoscape's functionality by using extra annotation provided by the user to both automatically generate a more pathway-like representation
of a network and to provide an environment for the visualization, comparison, and clustering of expression data from multiple conditions.
Cerebral uses subcellular localization annotation to create a layered view of a cell, placing nodes in the region of the screen corresponding to the
appropriate localization. Cerebral can also place a subset of nodes, regulated genes for example, at the bottom of the screen and can further group
these bottommost nodes according to any user-specified attribute. Small multiple views enable a user to see expression data from 2 or more conditions
overlaid on a network, while a difference view allows two conditions to be selected and a new view is coloured based on the changes between the
selected conditions. An interactive k-means clustering tool also allows users to cluster their data, using a slider to adjust the number of desired
clusters, and quickly select nodes based on cluster membership.
INVEX (a web-based tool for INtegrative Visualization of EXpression data)
Gene expression or metabolomics data generated from clinical studies are often associated with multiple clinical parameters or metadata (i.e. diagnosis, genotype, survival, gender, etc.). It is of great interest to analyze as well as to visualize the data with regard to these metadata. To address this issue, we have developed INVEX - an easy-to-use, highly interactive web-based tool that allows researchers to perform flexible differential expression analysis with regard to different metadata and to visually explore the results within the context of metadata and biological annotations. In addition, INVEX allows users to easily create custom heatmaps from interesting expression patterns and enriched functional categories. INVEX main features:
- Data annotation for common gene and metabolite IDs;
- Built-in support for a wide variety of common as well as complex analyses of differential expression;
- Enrichment analysis to highlight meaningful biological pathways or functional groups;
- Visual data exploration and pattern discovery by interactive heatmaps;
- An intuitive heatmap builder for creating custom heatmaps.
is a web-based tool for annotation of microarray data with up-to-date annotations.
-- integrative approaches for biological network analysis & visual exploration. NetworkAnalyst is designed to perform protein-protein interacton (PPI) network analysis
from genes or proteins of interest. It integrates differential gene expression analysis, network construction and network analysis.
Its main features include:
- Comprehensive data processing support
- Flexible network construction support
- Powerful network analysis & visualization
is a computational method that use a phylogenetic distance approach to evaluate ortholog predictions for two species compared to a third outgroup species.\
Ortholuge is a computational method that can generate precise ortholog predictions between two species on a genome-wide scale (using additional outgroup data for reference). It can either evaluate a previously constructed set of orthologs or it can generate an initial tentative set of orthologs that are subsequently evaluated. Precise ortholog prediction is important for a variety of analyses that utilize comparative genomics, including regulatory element identification.
SIGORA (Signature Over-representation Analysis)
is a Pathway Analysis package. Pathway Analysis is the process of statistically linking observations on the molecular level to biological processes or pathways on the systems (cell, tissue, organ, organism) level. Traditional pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative. This can lead to identification of spurious/misleading pathways as statistically significant, since many components are shared amongst pathways. SIGORA seeks to avoid this pitfall by focusing on genes or gene-pairs that are (as a combination) specific to a single pathway. In relying on such pathway gene-pair signatures(Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways. The current version allows for pathway analysis of human and mouse data sets and contains pre-computed Pathway-GPS data for pathways in the KEGG, PID_NCI, INOH and REACTOME pathway repositories.
is a multi-functional microarray analysis pipeline.