Wikiomics:ChIP-chip

Information about the method is here: ChIP-on-chip

Programs used to analyze ChIP-CHIP data

 * SignalMap (Nimblegene) uses gff format; useful mostly for visualization


 * TiMAT2 is a collection of command line java tools used for for both low and high level tiling microarray data analysis using the Affymetrix, Nimblegen, and Agilent platforms. TiMAT2 is designed for processing chIP-chip, RNA difference, and comparative genomic hybridization experiments from both single and multi chip data sets. Options exist for distributed computing on a cluster via PBS. (dead link Nov 2007)


 * TAMALPAIS: Web-based online analysis of ChIP-chip data (gff format); emails results to user. Stand alone version available from author.


 * Promoter Array Analysis Server: simple list comparison for NimbleGen summary files for Minimum Promoter Designs; displays in browser


 * ChIPOTle Microsoft Excel: Microsoft Excel macros. Restriction on maximum number of rows accepted by Excel (ca 64k).


 * Mpeak mpeak Win only ver.2 ?not working?


 * CEAS BED/GFF file format (BED format) so far only human and mouse genomes, web based


 * HMMTiling


 * TileMap tilemap


 * TileHGMM: R package, requires replicates from chips, plus probe location file. For computational reasons data sould be separated for each chromosome.


 * Chipper online and stand-alone R-based software (2006) Chipper


 * DRIM online and stand alone program (2007) DRIM


 * Ringo(R package, 2007)

ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory.
 * ACME (R package),


 * TiMAT

Credits

 * Darek Kedra wrote this tutorial