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According to the herbicide resistance action committee, herbicide resistance is the inherent ability of plant species to survive and reproduce after exposed to a herbicidal dosage which is lethal to its wild types. Globally, the evolvement of herbicide resistance has been a major cause of concern for sustainable agricultural production. Up to the end of 2016, around 477 herbicide resistant biotypes have been reported encompassing 252 weed species, where these biotypes have developed resistance to 23 of the 26 known herbicide sites of action and 161 different herbicides. The mechanism of herbicide resistance can be classified into two classes’ (i) target site resistance, and (ii) non-target site resistance. The target site resistance is mainly due to the mutations in the genes encoding the target sites of herbicides, whereas the non-target site resistance is due to the metabolic detoxification of herbicides. As far as target site resistance is concerned, mechanisms for seven classes of target proteins viz., acetolactate synthase (ALS), enolpyruvylshikimate-3-phosphate synthase (EPSPS), glutamine synthase (GS), protoporphyrinogen (PPO), acetyl-CoA carboxylase (ACCase), phytoene desaturase (PDS) and 4-hydroxyl phenyl pyruvate dioxygenase (HPPD) have been well reported in literature. Identification of the genes encoding for these target proteins in wet lab is certainly resource intesive. Thus, we developed the first computational tool HRGPred that can be used for predicting the genes encoding for the above mentioned seven categories of target proteins. We belive that the HRGPred will supplement the wet-lab experiments for annotation of herbicide resistance genes.

Our other Prediction servers

• dSSPred
• MaLDoSS
• PreDOSS
• HSplice
• SPIDBAR
• DCDNC
• iAMPpred
• DIRprot

Our recent publications

Meher PK , Sahu TK , Banchariya A, Rao AR (2017). DIRProt: A computational approach for discriminating insecticide resistant proteins from non-resistant proteins. BMC Bioinformatics, 18: 190.

Meher PK , Sahu TK , Saini V, and Rao AR (2017). Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC. Scientific Reports, 7: 42362.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2016). Discriminating coding from non-coding regions based on codon structure and methylation-mediated substitution: An application in rice and cattle. Computers and Electronics in Agriculture, 129: 66–73.

Meher PK , Sahu TK , Rao AR(2016). Prediction of donor splice sites using random forest with a new sequence encoding approach. BioData mining, 9:4.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2016). Identification of donor splice sites using support vector machine: a computational approach based on positional, compositional and dependency features. Algorithms for Molecular Biology, 11:16.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2016). A computational approach for prediction of donor splice sites with improved accuracy. Journal of Theoretical Biology, 404: 285–294.

Meher PK , Sahu TK , Rao AR(2016). Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier. Gene, 592: 316–324.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2015). Determination of window size and identification of suitable method for prediction of donor splice sites in rice (Oryza sativa) genome. Journal of Plant Biochemistry and Biotechnology, 24(4): 385-392.

Meher PK , Sahu TK , Rao AR, Wahi, SD(2014). A statistical approach for 5’ splice site prediction using short sequence motifs and without encoding sequence data. BMC Bioinformatics, 15: 362.