GenoCAD: CAD Software for Synthetic Biology Tranche Project, Secure Scientific Data Dissemination IMP: Integrative Multi-species Prediction Cluster 3.0 The open source clustering software available here implement the most commonly used clustering methods for gene expression data analysis. The clustering methods can be used in several ways. Cluster 3.0 provides a Graphical User Interface to access to the clustering routines. It is available for Windows, Mac OS X, and Linux/Unix. Python users can access the clustering routines by using Pycluster, which is an extension module to Python. People that want to make use of the clustering algorithms in their own C, C++, or Fortran programs can download the source code of the C Clustering Library. Cluster 3.0 is an enhanced version of Cluster, which was originally developed by Michael Eisen while at Stanford University. Java TreeView To view the clustering results generated by Cluster 3.0, we recommend using Alok Saldanha's Java TreeView, which can display hierarchical as well as k-means clustering results. Python is a scripting language similar to Perl. License Acknowledgment
GeneMANIA » search for MRE11A, RAD51, MLH1, MSH2, DMC1, RAD51AP1, RAD50, MSH6, XRCC3, PCNA, XRCC2 in H. sapiens FluidDB StratomeX — Institute for Computer Graphics and Vision Demonstration Video of Caleydo StratomeX Caleydo StratomeX is a visualization technique for the analysis of multiple stratified datasets. A good example for such an analysis scenario is the identification and characterization of cancer subtypes. StratomeX can be used to explore the results of data analysis systems developed to perform analyses of TCGA data. StratomeX makes such analysis results easier accessible and requires no scripting. The core concept of our approach is to visualize stratifications (groupings) of samples (patients) and the relationships between these groupings in a given cancer type. Groups can also be derived from copy number levels of a particular gene or gene mutation status, e.g., one group for “wild-type”, one for “mutated”. In the visualization, stratifications are represented as columns and the individual groups are represented as bricks in these columns. To learn more about Caleydo StratomeX refer to the Caleydo help pages. Examples Clustering comparisons Help
Molecular Workbench : Simulations