Functional annotation and classification of Metagenomic and Genomic Datasets
Functional annotation of gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, and is currently a bottleneck for the efficient analysis. Prediction-based methods could provide valuable alternatives to homology-based approaches for the functional annotation of genomic and metagenomic data due to favorable trade-offs among automation, speed and considerable accuracy. In this scenario, a combined approach using prediction-based (Random Forest) and similarity-based (RAPSearch2) approaches could provide efficient and reliable alternatives for functional annotation.
Motivation to develop a better, comprehensive and more efficient software
Most of the programs which are currently available for the large-scale analysis of genomic or metagenomic datasets are based on homology-based approaches. Therefore, we have developed Woods tool using an integrated approach for fast and accurate functional annotation of proteins in both genomic and metagenomic datasets. It is available as stand-alone tool as well as a publicly available web-server.
How to cite:
Sharma, Ashok K., Ankit Gupta, Sanjiv Kumar, Darshan B. Dhakan, and Vineet K. Sharma. "Woods: a fast and accurate functional annotator and classifier of genomic and metagenomic sequences." Genomics 106, no. 1 (2015): 1-6.