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RNA-Seq is a new tool, which utilizes high-throughput sequencing to measure RNA transcript counts at an extraordinary accuracy. It provides quantitative means to explore the transcriptome of an organism of interest. However, interpreting this extremely large data coming out from RNA-Seq into biological knowledge is a problem, and biologist-friendly tools to analyze them are lacking. In our lab, we develop a Transcriptator web application based on a computational Python pipeline with a user-friendly Java interface. This pipeline uses the web services available for BLAST
(Basis Local Search Alignment Tool), QuickGO and DAVID (Database for Annotation, Visualization and Integrated Discovery) tools. It offers a report on statistical analysis of functional and gene ontology annotation enrichment. It enables a biologist to identify enriched biological themes, particularly Gene Ontology (GO) terms related to biological process, molecular functions and cellular locations. It clusters the transcripts based on functional annotation and generates a tabular report for functional and gene ontology annotation for every single transcript submitted to our web server. Implementation of QuickGo web-services in our pipeline enable users to carry out GO-Slim analysis. Implementation of PORTRAIT (Prediction of transcriptomic ncRNA by ab initio methods) in our pipeline enables us to identify non coding RNA on a transcriptome context. It helps the user to characterize the denovo assembled reads which does not mapped to genome. Later we investigate the regulatory role of these non coding RNA on gene transcription. Finally, it generates easy to read tables and interactive charts for better understanding of the data. The pipeline is modular in nature, and provides an opportunity to add new plugins in the future.

Web application URL is freely available at: