Tranche Project, Secure Scientific Data Dissemination chipseq To install this package, start R and enter: source(" biocLite("chipseq") In most cases, you don't need to download the package archive at all. Bioconductor version: Release (3.0) Tools for helping process short read data for chipseq experiments Author: Deepayan Sarkar, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer <maintainer at bioconductor.org> Citation (from within R, enter citation("chipseq")): Sarkar D, Gentleman R, Lawrence M and Yao Z. chipseq: chipseq: A package for analyzing chipseq data. Installation Documentation To view documentation for the version of this package installed in your system, start R and enter: browseVignettes("chipseq") Details Package Archives Follow Installation instructions to use this package in your R session.
A multipurpose high performance production molecular simulator 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
untitled about [CP2K Open Source Molecular Dynamics ] FluidDB Homer Software and Data Download The UCSC Genome Browser is quite possibly one of the best computational tools ever developed. Not only does it contain an incredible amount of data in a single application, it allows users to upload custom information such as data from their ChIP-Seq experiments so that they can be easily visualized and compared to other information. There are also other genome browsers that are available, and each has a different strength: UCSC Genome Browser Truly a unique resource, logs of data preloaded and annotations. WashU Epigenome Browser Capable of visualizing long-range interactions (great for data sets like Hi-C), also has a lot of preloaded data. The Integrated Genomics Viewer (IGV), great for looking at reads locally instead of needing to load them to a server/cloud based solution. Most of the tools that are part of HOMER cater to the strengths of the UCSC Genome Browser - however, the bedGraph and other files generated by HOMER can be normally be used in the other genome browsers as well. 1.