NAMS webserver: coding potential assessment and functional annotation of plant transcripts.

Abstract

Recent advances in transcriptomics have uncovered lots of novel transcripts in plants. To annotate such transcripts, dissecting their coding potential is a critical step. Computational approaches have been proven fruitful in this task; however, most current tools are designed/optimized for mammals and only a few of them have been tested on a limited number of plant species. In this work, we present NAMS webserver, which contains a novel coding potential classifier, NAMS, specifically optimized for plants. We have evaluated the performance of NAMS using a comprehensive dataset containing more than 3 million transcripts from various plant species, where NAMS demonstrates high accuracy and remarkable performance improvements over state-of-the-art software. Moreover, our webserver also furnishes functional annotations, aiming to provide users informative clues to the functions of their transcripts. Considering that most plant species are poorly characterized, our NAMS webserver could serve as a valuable resource to facilitate the transcriptomic studies. The webserver with testing dataset is freely available at http://sunlab.cpy.cuhk.edu.hk/NAMS/.