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Input

The user should submit single or multiple cDNA/mRNA sequences in FASTA format to run the HRGPred server. An example of single sequence is given below.

>DQ296477.1 1181 Abutilon theophrasti library (Dauch AL) Abutilon theophrasti cDNA, mRNA sequence ATATATTGGTAAGCTACGACAGGACTTTGCTGGTGGCCGATCCGAGGAGGTGCGAGCCCAA GAAGTTCGGTGGAAGAGGTGCTAGAGCAAGGTTCCAGAAGAGTTACCGTTGAAGAAATTTC ATGGTTTGTTTTGAAAACTGGATTTTCCTTGTTTTTTCAATAGATGGTATCGTTATGCACA TTTTCGATTAAAATTTTGCTATGTTATGACTGAGTTTCAGA

Output

The out put is provided in a tabular format as given below. The table shows the probability with which the sequence(s) is(are) predicted into non-resistant and/or different classes of resistant category. If the probabilty is >0.5, then the test sequence can be said to be predicted in that class.

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.