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Paper text:
a variety of experimental genome annotation technologies provide counts , probabilities or intensity values for chromosomal positions spread over a complete or partial genome .
such technologies include cdna and tag - sequencing protocols to map the 5 and 3 ends of mrna ( 1,2 ) , chip  chip analysis to reveal transcription factor binding sites and epigenetic markers , and high - density snp profiling platforms for various kinds of genotype  phenotype association studies .
a recurrent problem in the processing of such data is the identification of individual clusters ( alternatively called peaks or islands ) by some kind of signal detection and noise - filtering algorithm .
genome coordinates with available quantitative measurements are often not evenly distributed over the analyzed chromosomal range .
furthermore , the horizontal axis is discrete in nature and the size and shape of the target objects are in most cases largely unknown in advance .
moreover , the position - specific readout from the experimental protocol may be a function of several overlaid biological and technical processes for instance , in the case of promoter mapping , the number of cdna 5 ends at a given position is thought to reflect primarily transcription initiation events , but technical artifacts or premature termination of cdna synthesis may also contribute to the signal .
peak - recognition algorithms exist in many variants ( 3,4 ) and their application is described for instance for the analysis of ms data and chromatographic profiles or time series ( 5 ) .
existing methods frequently lack flexibility and at the example of the package mclust in r ( 6 ) , implementation of additional constraints is difficult .
these methods are typically based on more or less explicit physical assumptions about the signal - generating process and peak shapes , and therefore can not be directly ported to new applications .
often , there is no experimental gold standard available for evaluation of the results . in such cases ,
we initially developed the program madap for the inference of promoters from mrna 5 end profiles obtained from the mapping of full - length cdnas to the genome sequence ( 7 ) .
more recently , we discovered the usefulness of madap for the interpretation of chip  chip data .
given the potential of an even more general use , the web server presented here is primarily intended to enable a rapid evaluation of the suitability of madap for new kinds of data .
finding appropriate parameter settings likely represents the largest obstacle in applying madap to a new genome annotation problem .
please note that previously established parameters might not be suited for a novel data set with distinct characteristics such as background noise .
the server may also be used for small - scale productive applications . however , for large - scale genome annotations task , we recommend to use a local installation of the program which can be freely downloaded from our ftp site .
the madap web server takes as input an uploaded file containing a set of tabulator - separated numbers representing for instance positions on a chromosome .
the numbers describing the data points occur at a frequency corresponding to the strength ( or intensity ) of a measure at this given position , for instance the number of times a 5 end of a full - length transcript ( 8) is observed at this position .
alternatively , data can be supplied in a file in gff format ( www.sequenceontology.org/gff3.shtml ) . in this case
, a frequency score of each feature has to be indicated in column 6 (  score ) of the gff file .
the function of madap is to determine the most likely model describing the input data set .
a mixture of normal ( gaussian ) distributions with centers , standard deviations and relative frequencies are used to model the data points , an approach also known as a mixture modeling .
although the shape of the distributions of the clusters hidden in the input data is frequently not known and not necessarily resembling a normal distribution , we observe that the algorithm of madap as further described later copes remarkably well with most kinds of distributions .
using normal distributions has the advantage that the probability of observing a unit event ( e.g. one cdna 5 end ) at a specific position can easily be computed given the center position and standard deviation of a set of normal distributions .
the number , the location of the centers and the relative frequency of the normal distributions are initially deduced from the data . using a standard expectation maximization ( em ) approach ( 4 ) madap optimizes the center positions of zero to many clusters and their standard deviation .
a known drawback in this approach is that isolated points distant from the cluster centers ( leverage points ) can have an undesirable high impact on the selection of the model that best describes the clusters of points . in order to control for disturbing isolated points ,
we add to the mixture model an additional non - gaussian , uniform  background component , thereby reducing the negative influence on the model selection .
madap thus differs from the standard algorithm ( 9 ) in the addition of a background distribution and in the possibility to specify a set of additional constraints explained in the following summary of the optimization steps of the model . in a first model initialization step ,
minimal and maximal numbers of clusters can be defined by user - specified parameters ; the initial numbers of clusters are additionally limited by the number of distinct data points in the input data .
the center positions of the clusters are initially attributed to data points with the highest frequency .
a second parameter for background control subtracts a user - defined constant from all positions for model initialization .
the initial centers of clusters are optimized until stabilization of the data likelihood or discarded if a maximal number of iterations is reached . in a third step ,
models resulting from the em step are required to comply with user - defined model constraints .
such constraints include a minimal number of data points attributed to a cluster and a minimal distance between peaks of neighboring clusters .
noncompliant clusters in models are removed and the em step described earlier is repeated with a model with a reduced number of clusters . if there is no cluster left , the model is rejected .
two variants for likelihood computation are offered : the usual likelihood under a mixture model ( 4 ) , and a likelihood that is calculated after attributing each data point to the cluster with the highest density at that position ( see explanatory document on web server ) . due to limits in computational resources ,
the web server version of madap features a few restrictions , notably in the number of initial clusters .
users are advised to either split up their data sets to ranges putatively containing less than 50 clusters , or to install madap on their local computers . for
further descriptions of the algorithm and its parameters can be found on the site of the madap web server .
in the following we are going to describe with two examples how madap can be used for definition of promoters from full - length cdna data or from chip  chip data . note
given a spectrum of transcription start sites over a genomic range , there is no objective way to answer the question of how many promoters they represent .
the consensus answer is likely to come from an interacting learning process with new methods and data .
we therefore equipped the madap algorithm with a variety of parameters that enable the user to guide the partitioning process in a desired direction .
default parameters on the web server correspond to optimized values for the transcription start site ( tss ) task .
in particular , we try to relate the parameters chosen in this application to assumptions about the biological signal , in the following presented in the estimated order of importance . aiming for high robustness and precision of tss mapping , we required at least 10 counts ( here cdna 5 ends ) per clusters ( parameter n = 10 ) .
we assumed that in average 70% of all full - length transcripts initiate within 20  bp of its 
main tss , thus being best described by a fixed standard deviation of the initial gaussian components equal to 20 ( d = 20 ) .
alternative promoters were defined as neighboring tss having a minimal distance of 50  bp ( p = 50 ) .
parameters ( m = 1 ) and ( m = 16 ) specify the range for the number of clusters in the initial models .
computation time increases significantly with increasing ranges , because the more models have to be tested .
parameter ( s = 0 ) defines the background subtraction for model initialization and parameter ( e = 0.02 ) represents an estimate of the proportion of data points that belong to a random point background distribution .
optimal values for these parameters strongly depend not only on the kind of application , but also on the actual data set with , for instance specific noise levels .
parameter ( c = 5 ) defines an integration range within which data points are initially attributed to a cluster center .
parameters ( u = 6 ) and ( w = 11 ) specify extended reporting ranges , for which the number and the fraction of points is reported in the text output , without influence on the clustering .
this led to the following parameter settings ( default on the web server , resulting output shown in supplementary figure 1 ) : 

 due to the fact that exact characteristics of the putative clusters in the input data are not known , some parameter settings may appear arbitrary .
a second demo file provided on the web server is derived from a chip  chip experiment using the nimblegen platform with an antibody against a component of the preinitiation complex ( 10 ) .
data describing a 250  kb segment of chromosome 12 was extracted from the geo database , and the locations of the probes were remapped onto the current human genome assembly ( ncbi 36 ) .
the intensity of the hybridization signal on the chip was transformed into integers by a simple exponentiation to the power of 10 , setting an arbitrary maximum of 200 ( the numerical values provided by geo represent log - intensities ) .
the input file provided in gff format contains 336 genome coordinates associated with a total of 3357 digitized intensity units .
this demo gff file was submitted to madap with the following parameters : 

 changes in parameters c , p , d , u and w relate to the larger cluster width expected for chip signals .
the background probability ( e = 0.002 ) was adjusted to a lower noise level .
figure 1 shows the output of the madap web server on this second demo file aligned with the corresponding genome annotations visualized by the ensembl genome viewer ( 11 ) . 

figure 1.output of madap server aligned with ensembl genome annotations : madap executed on data derived from a chip  chip experiment using chromatin from imr90 cells and antibodies against taf1 . on top , a histogram of the input data is shown , with the x - axis indicating genomic positions on a portion of human chro .
12 , and the y - axis representing transformed hybridization intensities of corresponding probes of a nimblegen whole genome array .
madap determines the location of 8 clusters of putative promoters , which largely correspond to 5 ends of annotated transcribed sequences , as visualized in the ensembl contigview below .
output of madap server aligned with ensembl genome annotations : madap executed on data derived from a chip  chip experiment using chromatin from imr90 cells and antibodies against taf1 . on top , a histogram of the input data is shown , with the x - axis indicating genomic positions on a portion of human chro .
12 , and the y - axis representing transformed hybridization intensities of corresponding probes of a nimblegen whole genome array .
madap determines the location of 8 clusters of putative promoters , which largely correspond to 5 ends of annotated transcribed sequences , as visualized in the ensembl contigview below . on the madap web server ,
the results are presented graphically on top of histograms of the input data . in an overview plot ,
the distribution of determined clusters is displayed as numbers plotted at the approximate location of the corresponding cluster .
a detailed view of each cluster allows the determination of the center and the estimated standard deviation .
in addition to the visual representation of the location of the inferred clusters , the output of the madap server includes parseable output files in text format .
output containing a recapitulation of the parameters used and a description of the clusters found under the models described above .
supplementary files are intended to help to track the iteration behavior of madap , including the files 
we present here a web server for the clustering program madap , which was initially developed for the determination of tss ( 7 ) .
although madap uses internally normal distributions , it was designed to model non - contiguous distributions of any shape and proved to be remarkable robust in this aspect .
others have exploited cdna full - length sequencing data or 5 tags ( 5 sage , cage ) for promoter mapping and have presumably developed alternative solutions to the tss clustering problem . however , to our knowledge none of these methods has been explicitly described or made public via web servers .
madap is in principle versatile enough to interpret data from any source in terms of a finite number of clusters characterized by center positions , volume and extension . in the scope of primary genome annotation data ,
we envisage an extended usage of madap in the analysis of 3 ends of transcripts and the inference of polya signals , and on data derived from chip  chip , or from tiling arrays .
a recurring task in the analysis of mass genome annotation data from high - throughput technologies is the identification of peaks or clusters in a noisy signal profile . 
 examples of such applications are the definition of promoters on the basis of transcription start site profiles , the mapping of transcription factor binding sites based on chip - chip data and the identification of quantitative trait loci ( qtl ) from whole genome snp profiles . 
 input to such an analysis is a set of genome coordinates associated with counts or intensities . 
 the output consists of a discrete number of peaks with respective volumes , extensions and center positions . 
 we have developed for this purpose a flexible one - dimensional clustering tool , called madap , which we make available as a web server and as standalone program . 
 a set of parameters enables the user to customize the procedure to a specific problem . 
 the web server , which returns results in textual and graphical form , is useful for small to medium - scale applications , as well as for evaluation and parameter tuning in view of large - scale applications , requiring a local installation . 
 the program written in c++ can be freely downloaded from ftp://ftp.epd.unil.ch/pub/software/unix/madap . 
 the madap web server can be accessed at http://www.isrec.isb - sib.ch / madap/.