MOTIVATION: Although peak finding in next-generation sequencing (NGS) datasets 
has been addressed extensively, there is no consensus on how to analyze and 
process biological replicates. Furthermore, most peak finders do not focus on 
accurate determination of enrichment site widths and are not widely applicable 
to different types of datasets.
RESULTS: We developed JAMM (Joint Analysis of NGS replicates via Mixture Model 
clustering): a peak finder that can integrate information from biological 
replicates, determine enrichment site widths accurately and resolve neighboring 
narrow peaks. JAMM is a universal peak finder that is applicable to different 
types of datasets. We show that JAMM is among the best performing peak finders 
in terms of site detection accuracy and in terms of accurate determination of 
enrichment sites widths. In addition, JAMM's replicate integration improves peak 
spatial resolution, sorting and peak finding accuracy.
AVAILABILITY AND IMPLEMENTATION: JAMM is available for free and can run on Linux 
machines through the command line: http://code.google.com/p/jamm-peak-finder.