In this study, the known peptidoglycan hydrolases were divided into multiple classes based on their site of action and were used for the development of a computational tool 'HyPe' for identification and classification of novel peptidoglycan hydrolases from genomic and metagenomic data.  Random Forest multiclass model was selected for the development of HyPe tool as it showed up to 71.12 % sensitivity, 99.98 % specificity, 99.55 % accuracy and 0.80 MCC in four different classes of peptidoglycan hydrolases.  The ability of HyPe to identify novel peptidoglycan hydrolases was also demonstrated on 24 metagenomic datasets.


 http://metagenomics.iiserb.ac.in/hype/ and http://metabiosys.iiserb.ac.in/hype/ .
HyPe
Various classification models were developed using amino acid and dipeptide composition features by training and optimization of Random Forest and Support Vector Machines.  Random Forest multiclass model was selected for the development of HyPe tool as it showed up to 71.12 % sensitivity, 99.98 % specificity, 99.55 % accuracy and 0.80 MCC in four different classes of peptidoglycan hydrolases. 
Random Forest
Various classification models were developed using amino acid and dipeptide composition features by training and optimization of Random Forest and Support Vector Machines. 
Support Vector Machines
BMC genomics
D000900:Anti-Bacterial Agents; D002473:Cell Wall; D006868:Hydrolysis; D000069550:Machine Learning; D056186:Metagenomics; D015233:Models, Statistical; D009238:N-Acetylmuramoyl-L-alanine Amidase; D016366:Open Reading Frames; D010457:Peptidoglycan; D015203:Reproducibility of Results; D064878:Web Browser
Sharma Ashok K AK ;Kumar Sanjiv S ;K Harish H ;Dhakan Darshan B DB ;Sharma Vineet K VK 