MADAP is a flexible clustering tool for the interpretation of one-dimensional genome annotation data mapped onto complete or partial genome sequences. Such data might consist in counts, probabilities, or intensities and be obtained from cDNA and tag sequencing protocols to map the 5' and 3'ends of mRNA, from ChIP-chip analysis, or from genome-wide SNP-typing used in genotype-phenotype association studies. MADAP identifies groups of data corresponding to one or several genomic sites, and estimates the volume and extension of such groups (clusters).

Input: set of integer numbers, typically representing genomic positions. These numbers can occur multiple times, according to the strength of a measure at a given genomic site. The MADAP web-server accepts data in different formats, including gff files (see below).

Processing: MADAP models one-dimensional input data by a set of clusters defined by center and range, which have sufficient support from the data and which are compatible with constraints defined by the user.

Output: clustering results are provided in graphical and tabular form, accompanied with descriptions of iteration steps of the algorithm.

Source code: MADAP is also available as a stand-alone program at : ftp. A more detailed description is available here.

Publication: Schmid CD, Sengstag T, Bucher P and Delorenzi M. (2007) MADAP, a flexible clustering tool for the interpretation of one-dimensional genome annotation data. Nucleic Acids Res, 35, W201-205 (PMID: 17526516).

Data Input
Upload (Demo file) :
view Demo file
Upload (Demo GFF file) :
view Demo gff file
Model initialisation parameters
     Minimal number of clusters : (-m, kminnbcomponents)
     Maximal number of clusters : (-M, kmaxnbcomponents)
     Integration range : (-c, kdefaultfusionsdist)
     Background subtraction : (-s, kminnbpoints)
Model constraints parameters
     Min. distance btw peaks of clusters : (-p, kminimalpeakdist)
     Min. points in cluster : (-n, kminnbdatapoints)
Model fitting parameters
     Standard deviation : (-d, kfixvar)
     Adapting standard deviation :
     Probability of error data : (-e, kerrorprior)
Output Options
     Mode for highest likelihood computation : classical mixture model likelihood
full attribution of points to clusters
     1rst extended reporting range : (-u, krefdist1)
     2nd extended reporting range : (-w, krefdist2)
Options for graphical output
     X axis label:
     Y axis label:
     Box around cluster in global plot:

Last update 27 Sep. 2012