SUMMARY: NTAP is designed to analyze ChIP-chip data generated by the NimbleGen tiling array platform and to accomplish various pattern recognition tasks that are useful especially for epigenetic studies. The modular design of NTAP makes the data processing highly customizable. Users can either use NTAP to perform the full process of NimbleGen tiling array data analysis, or choose post-processing modules in NTAP to analyze pre-processed epigenetic data generated by other platforms. The output of NTAP can be saved in standard GFF format files and visualized in GBrowse. AVAILABILITY AND IMPLEMENTATION: The source code of NTAP is freely available at http://ntap.cbi.pku.edu.cn/. It is implemented in Perl and R and can be used on Linux, Mac and Windows platforms.

BACKGROUND: To date, many genomic and pathway-related tools and databases have been developed to analyze microarray data. In published web-based applications to date, however, complex pathways have been displayed with static image files that may not be up-to-date or are time-consuming to rebuild. In addition, gene expression analyses focus on individual probes and genes with little or no consideration of pathways. These approaches reveal little information about pathways that are key to a full understanding of the building blocks of biological systems. Therefore, there is a need to provide useful tools that can generate pathways without manually building images and allow gene expression data to be integrated and analyzed at pathway levels for such experimental organisms as Arabidopsis. RESULTS: We have developed PathMAPA, a web-based application written in Java that can be easily accessed over the Internet. An Oracle database is used to store, query, and manipulate the large amounts of data that are involved. PathMAPA allows its users to (i) upload and populate microarray data into a database; (ii) integrate gene expression with enzymes of the pathways; (iii) generate pathway diagrams without building image files manually; (iv) visualize gene expressions for each pathway at enzyme, locus, and probe levels; and (v) perform statistical tests at pathway, enzyme and gene levels. PathMAPA can be used to examine Arabidopsis thaliana gene expression patterns associated with metabolic pathways. CONCLUSION: PathMAPA provides two unique features for the gene expression analysis of Arabidopsis thaliana: (i) automatic generation of pathways associated with gene expression and (ii) statistical tests at pathway level. The first feature allows for the periodical updating of genomic data for pathways, while the second feature can provide insight into how treatments affect relevant pathways for the selected experiment(s).