Dataset

The dataset consisting of 120 cDNA sequences of resistant category and 120 sequences of non-resistant category that have been used to train the HRGPred as well as for the prediction purpose using HMM and BLAST algorithms, can be downloded here..

The independent dataset comprising 44 sequences of the resistant class (9 ACCase, 9 ALS, 7 EPSPS, 6 GS, 4 HPPD, 4 PDS and 5 PPO) and 1324 sequences of the non-resistant class can be downloaded here.

The dataset consisting of all the 3292 sequences of the non-resistant category can be downloaded here, and 2282 sequences obtained after removing the non-standard nucleotides can be downloaded here.

The dataset of all the 227 cDNA sequences (45 ACCase, 46 ALS, 36 EPSPS, 31 GS, 22 HPPD, 22 PDS and 25 PPO) collected from the database of international survey of herbicide resistant weeds (http://www.weedscience.org/Sequence/sequence.aspx) can be downloaded here.

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.