Bioinformatics and Computational Biology, Dr. Sahu has developed web-based tools for the prediction of donor splice sites (ss), antimicrobial peptides (AMPs), unknown fungal species Heat shock proteins (HSPs), insecticide resistant proteins nitrogen fixation genes, herbicide resistance (HR) genes, miRNAs associated with abiotic stresses, shRNAs from a genomic sequence, B-cell epitopes for Foot and Mouth Disease Virus. He has also developed a tool for identifying and removing germplasm duplicates based on genotyping data. Dr. Sahu has developed many information systems of agricultural importance such as Cattle Genomic Resource Information System (CGRIS), Foot and Mouth Disease Information System (FMDISC), Mango Resource Information System (MRIS). He has also worked on in silico aspects of various animal diseases like FMD, calf scour, bovine viral diarrhea and infectious bovine rhinotracheitis diseases. He has published more than 40 research and review papers in the journals of national and international repute. Besides, He has more than 15 software copyrights, two ICAR technologies, and eight book chapters to his name.

Research/Review Papers

1. Sahu T.K., Verma S.K., Gayacharan et al. Transcriptome-wide association mapping provides insights into the genetic basis and candidate genes governing flowering, maturity and seed weight in rice bean (Vigna umbellata). BMC Plant Biol 24, 379 (2024). https://doi.org/10.1186/s12870-024-04976-y
2. Meher PK, Hati S, Sahu TK, Pradhan U, Gupta A, Rath SN, Svm-root: Identification of root associated proteins in plants by employing the support vector machine with sequence-derived features. Current Bioinformatics, 2023, 18.
3. Sahu S, Rao AR, Sahu TK, Pandey J, Varshney S, Kumar A, Gaikwad K. Predictive Role of Cluster Bean (Cyamopsis tetragonoloba) Derived miRNAs in Human and Cattle Health. Genes. 2024; 15(4):448. https://doi.org/10.3390/genes15040448
4. Choudhury N, Sahu TK, Rao AR, Rout AK, Behera BK. An Improved Machine Learning-Based Approach to Assess the Microbial Diversity in Major North Indian River Ecosystems. Genes. 2023; 14(5):1082. https://doi.org/10.3390/genes14051082.
5. Mir ZA, Chauhan D, Pradhan AK, Srivastava V, Sharma D, Budhlakoti N, Mishra DC, Jadon V, Sahu TK, Grover M, Gangwar OP, Kumar S, Bhardwaj SC, Padaria JC, Singh AK, Rai A, Singh GP, Kumar S. Comparative transcriptome profiling of near isogenic lines PBW343 and FLW29 to unravel defense related genes and pathways contributing to stripe rust resistance in wheat. Funct Integr Genomics. 2023 May 20;23(2):169. doi: 10.1007/s10142-023-01104-1. PMID: 37209309.
6. Choudhury N, Sahu TK, Rao AR, Rout AK, Behera BK (2023) A Metagenomic Insight into Assessment of Microbial Diversity in the River Ganga at Two Locations for Sustainable Development, Journal of Community Mobilization and Sustainable Development, Vol. 18(2), April-June 2023, 392-398.
7. Sahu TK, Singh AK, Mittal S, Jha SK, Kumar S, Jacob SR and Singh K (2022) G-DIRT: a web server for identification and removal of duplicate germplasms based on identity-by-state analysis using SNP genotyping data. Briefings in Bioinformatics, Volume 23(5): bbac348, https://doi.org/10.1093/bib/bbac348
8. Sahu TK, Meher PK, Choudhury NK, Rao AR. A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes. Brief Bioinform. 2022 Sep 20;23(5):bbac356. doi: 10.1093/bib/bbac356.
9. Meher, PK, Sahu, TK, Gupta, A, Kumar, A and Rustgi, S (2022). ASRpro: A machine‐learning computational model for identifying proteins associated with multiple abiotic stress in plants. The Plant Genome, p.e20259.
10. Raghunandan K, Tanwar, J.; Patil, S.N.; Chandra, A.K.; Tyagi, S.; Agarwal, P.; Mallick, N.; Murukan, N.; Kumari, J.; Sahu, T.K.; Jacob, S.R.; Kumar, A.; Yadav, S.; Nyamgoud, S.; Vinod; Singh, A.K.; Jha, S.K. Identification of Novel Broad-Spectrum Leaf Rust Resistance Sources from Khapli Wheat Landraces. Plants 2022, 11, 1965. https://doi.org/10.3390/plants11151965.
11. Prasad G, Mittal S, Kumar A, Chauhan D, Sahu TK, Kumar S, Singh R, Yadav MC and Singh AK (2022) Transcriptome Analysis of Bread Wheat Genotype KRL3-4 Provides a New Insight Into Regulatory Mechanisms Associated With Sodicity (High pH) Tolerance. Front. Genet. 12:782366. doi: 10.3389/fgene.2021.782366
12. Meher PK, Dash S, Sahu TK, Satpathy S and Pradhan SK. GIpred: a computational tool for prediction of GIGANTEA proteins using machine learning algorithm. Physiology and Molecular Biology of Plants 28, 1–16 (2022). https://doi.org/10.1007/s12298-022-01130-6
13. Meher PK, Begam S, Sahu TK, Gupta A, Kumar A, Kumar U, Rao AR, Singh KP, Dhankher OP. ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features. International Journal of Molecular Sciences. 2022; 23(3):1612. https://doi.org/10.3390/ijms23031612.
14. Kochhar A, Khan NS, Deval R, Pradhan D, Jena L, Bhuyan R, Sahu TK, Jain AK. Protein-protein interaction and in silico mutagenesis studies on IL17A and its peptide inhibitor. 3 Biotech. 2021 Jun;11(6):305. doi: 10.1007/s13205-021-02856-y.
15. Sahu TK, Gurjar AKS, Meher PK, Varghese C, Marwaha S, Rao GP, Rai A, Guleria N, Basagoudanavar SH, Sanyal A, Rao AR. Computational insights into RNAi-based therapeutics for foot and mouth disease of Bos taurus. Scientific Reports. 2020 Dec 9;10(1):21593. doi: 10.1038/s41598-020-78541-6.
16. Kairi A, Sahu TK and AR Rao (2020) An information system on buffalo (Bubalus bubalis) genome. Indian Journal of Animal Sciences 90(10):(accepted for publication).
17. Choudhary RK, Sahu TK, Kumar H, Rao AR, Choudhary SK, and Behera TK (2020) Computational identification of putative genes and vital amino acids involved in biennial rhythm in mango (Mangifera indica L.) Journal of Pharmacognosy and Phytochemistry 2020; SP6: 267-272
18. S Sahu, TK Sahu, S Ghosal, K Gaikwad, AR Rao (2020), Computational analysis of SNPs and INDELs in cluster bean cultivars involved in multiple trait expression, Indian J. Genet 80 (2), 179-185
19. Meher PK, Sahu TK, Gahoi S, Satpathy S, Rao AR (2019) Evaluating the performance of sequence encoding schemes and machine learning methods for splice sites recognition. Gene 705:113-126.
20. Meher PK, Sahu TK, Raghunandan K Gahoi S, Choudhary NK, Rao AR (2019) HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine. Scientific Reports 9: Article number: 778.
21. Meher PK, Sahu TK, Gahoi S, Tomar R, Rao AR (2019) funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model. BMC Genetics. 20(1):2. doi: 10.1186/s12863-018-0710-z.
22. Sahu TK, Pradhan D, Rao AR and Jena L (2018) In silico site-directed mutagenesis of neutralizing mAb 4C4 and analysis of its interaction with G-H loop of VP1 to explore its therapeutic applications against FMD. Journal of Biomolecular Structure and Dynamics. doi: 10.1080/07391102.2018.1494631.
23. Khan NS, Verma R, Pradhan D, Nayek A, Bhuyan R, Sahu TK, Jain AK (2018) Analysis of interlukin23 and 7G10 interactions for computational design of lead antibodies against immune-mediated inflammatory diseases. Journal of Receptor and Signal Transduction. 38(4):327–334.
24. Meher PK, Sahu TK, Gahoi S, Rao AR (2018) ir-HSP: Improved recognition of heat shock proteins, their families and sub-types based on g-spaced di-peptide features and support vector machine. Frontiers in Genetics. 8: 235. doi: 10.3389/fgene
25. Meher PK, Sahu TK, Mohanty J, Gahoi S, Purru S, Grover M, Rao AR (2018) nifPred: Proteome-wide identification and categorization of nitrogen-fixation proteins of diaztrophs based on composition-transition-distribution features using support vector machine. Frontiers in Microbiology 9, 1100. doi:10.3389/fmicb.2018.01100.
26. 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(1):190. doi: 10.1186/s12859-017-1587-y.
27. 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. doi: 10.1038/srep42362.
28. 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.
29. Meher PK, Sahu TK and Rao AR (2016) Identification of species based on DNA barcode using k-mer feature vector and Random forest classifier, Gene 592(2):316-24.
30. 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.
31. 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.
32. Meher PK, Sahu TK and Rao AR (2016). Prediction of donor splice sites using random forest with a new sequence encoding approach. BioData Mining, 9:4, DOI: 10.1186/s13040-016-0086-4.(Joint first author)
33. Meher PK, Sahu TK, Rao AR (2016) Performance evaluation of neural network, support vector machine and random forest for prediction of donor splice sites in rice. Indian Journal of Genetics and Plant Breeding, 76(2): 173-180.
34. Bhati J, Sahu TK and Pandey PK (2016): Carragheen molecular marker database (CAMM-DB): a comprehensive database for carragheen (Chondrus crispus) molecular markers. International Journal of Applied Biology and Pharmaceutical Technology 7(1):290-298.
35. Behera BK, Das P, Maharana J, Meena DK, Sahu TK, Rao AR, Chatterjee S,Mohanty BP, Sharma AP(2015), Functional screening and molecular characterization of halophilic and halotolerant bacteria by 16S rRNA gene sequence analysis. Proc Natl Acad Sci India B Biol Sci. 85(4): 957–964.
36. Meher PK, Sahu TK, Rao AR and 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.
37. Sahu, TK, Rao, AR, Meher PK, Sahoo BC, Gupta S and Rai A (2015). Computational prediction of MHC class I epitopes for most common viral diseases in cattle (Bos taurus). Indian Journal of Biochemistry and Biophysics, 52(1): 34-44.
38. Meher PK, Sahu TK, Rao AR and Wahi SD (2014). Application of Gibbs sampling methodology for identification of transcription factor binding sites in MADS box family genes in Arabidopsis thaliana. Indian Journal of Genetics and Plant Breeding, 74(1): 73-80.
39. Sahu TK, Rao AR, Dora S, Gupta S and Rai A (2014), In silico identification of late blight susceptibility genes in Solanum tuberosum, Indian Journal of Genetics and Plant Breeding,74(2): 229-237.
40. Meher, P.K., Sahu TK, Rao, A.R. and Wahi, S.D. (2014). A statistical approach for 5’ splice site prediction using short sequence motifs and without encoding sequence data. BMC Bioinformatics, 15: 362. DOI:10.1186/s12859-014-0362-6.
41. Rao AR, Dash M, Sahu TK, Wahi SD, Behera BK, Sharma AP and Bhatia VK (2014), Statistical and bio-computational applications in animal sciences, Indian Journal of Animal Sciences, 84 (5): 475–489.
42. Rao AR, Dash M, Sahu TK, Behera BK and Mohapatra T (2014), Detection of novel key residues of Mn SOD enzyme and its role in salinity management across species, Journal of Genetics, 93: e8–e16.
43. Rajan S, Sahu TK and Yadava LP (2013), Mango Resources Information System: an open access portrayal of phenotypical, genetic and chemical information on Mango, Acta Horticulturae (ISHS) 992:99-104
44. Sahu TK, Rao AR, Vasisht S, Singh N and Singh UP(2012), Computational Approaches, Databases and Tools for in silico Motif Discovery, Interdisciplinary Sciences: Computational Life Sciences, 4(4):239-255.
45. Singh N, Sahu T K, Rao AR, Mohapatra T (2012), shRNAPred (version 1.0): An open source and standalone software for short hairpin RNA (shRNA) prediction, BIOINFORMATION, 8(13):629-633
46. Sahu TK, Rao AR, Singh A, Behera BK, Mohapatra T. (2011), In silico identification of residues for anoxia tolerance across species, Online J Bioinformatics, 12(1):175-197, 2011.
47. Sahu TK, Patra MC (2011), Bio-computational interaction analysis of acetylcholinesterase (AChE) Toxin C from Dendroaspis polylepis with human AChE, Online J Bioinformatics, 12(1):139-148

Software Copyrights

1. Sahu TK, Meher PK, Rao AR (2021) Foot and Mouth Disease Information System for Cattle (FMDISC) SW-14088/2021: Indian Agricultural Statistics Research Institute, New Delhi.
2. Sahu TK, Rao AR, Rai A (2021) Cattle Genomic Resource Information System (CGRIS) SW-14070/2021: Indian Agricultural Statistics Research Institute, New Delhi.
3. Sahu TK, Meher PK Rao AR (2021) 1Flexible length B-Cell Epitope Prediction for FMDV(FlexiBef) SW-14069/2021: Indian Agricultural Statistics Research Institute, New Delhi.
4. Meher PK, Sahu TK, Rao AR (2019) SPIDBAR: Species Identification using DNA Barcode, SW-13040/2019. Indian Agricultural Statistics Research Institute, New Delhi.
5. Meher PK, Sahu TK, Rao AR (2019) DIRProt: Discriminating the insecticide resistance proteins from non-resistance proteins, SW-12353/2019. Indian Agricultural Statistics Research Institute, New Delhi.
6. Meher PK, Rao AR, Sahu TK (2019) Ir-HSP: Online software for improved recognition of Heat Shock Proteins (HSP) and their families, SW-12349/2019. Indian Agricultural Statistics Research Institute, New Delhi.
7. Meher PK, Sahu TK, Rao AR (2019) HRG Pred: Software For prediction of herbicide resistant genes, SW-12347/2019. Indian Agricultural Statistics Research Institute, New Delhi.
8. Meher PK, Sahu TK, Rao AR (2019) PreDoss : Prediction of donor splice sites in eukaryotic genes with improved accuracy, SW-12358/2019. Indian Agricultural Statistics Research Institute, New Delhi.
9. Meher PK, Sahu TK, Rao AR (2019) HSplice: A hybrid approach for predicting 5' splicing junctions, SW-12357/2019. Indian Agricultural Statistics Research Institute, New Delhi.
10. Meher PK, Sahu TK, Rao AR (2019) MalDoss: A web server for Donor Splice site prediction using machine learning approaches, SW-12345/2019. Indian Agricultural Statistics Research Institute, New Delhi.
11. Meher PK, Sahu TK, Rao AR (2019) dssPred: A web server for eukaryotic donor splice site prediction, SW-12552/2019: Indian Agricultural Statistics Research Institute, New Delhi.
12. Meher PK, Sahu TK, Rao AR (2019) iAMPpred: Online software for improved prediction of antimicrobial peptides, SW-12549/2019. Indian Agricultural Statistics Research Institute, New Delhi.
13. Sahu TK, Meher PK, Rao AR (2019) nifPred: A webserver for prediction of nitrogen fixation genes, SW-12548/2019: Indian Agricultural Statistics Research Institute, New Delhi.
14. Rao AR, Meher PK, Sahu TK(2019) funBarRF: DNA barcode based fungal species identification, SW-12551/2019: Indian Agricultural Statistics Research Institute, New Delhi.
15. Meher PK, Sahu TK, Rao AR (2019) DCDNC: Discrimination of coding sequence (CDS) from non-coding sequence (Intron). SW-12550/2019: Indian Agricultural Statistics Research Institute, New Delhi.
16. Singh N, Sahu TK, Rao AR, Mohapatra T (2013), shRNAPred (Version1.0) (software), SW-7548/2013: Indian Agricultural Statistics Research Institute, New Delhi.

Book chapters

1. Sahu TK, Singh M, Kalia S and Singh AK (2023) Genome-wide association study (GWAS) concept and methodology for gene mapping in plants. In Raina A, Wani MR, Laskar RA, Tomlekova N, Khan S(Eds.) Advanced Crop Improvement, Volume 2 - Case Studies of Economically Important Crops, Chapater 18, Springer, Charm. https://doi.org/10.1007/978-3-031-26669-0_17
2. Sahu TK, Singh N, Meher PK, Wahi SD and RAO AR(2014), Epigenetics in Sustaining the Livelihood, In Rao AR, Govil JN (Eds.), Biotechnology Vol. 6: Bioinformatics and Computational Biology, Chapter 20, Page 437-462, Studium Press LLC, USA.
3. Gupta S, Sahu TK And Meher PK (2014), Systems Biology-An Overview, In Rao AR, Govil JN (Eds.), Biotechnology Vol. 6: Bioinformatics and Computational Biology, Chapter 17, Page 379-406, Studium Press LLC, USA.
4. Sahu TK, Bajetha G, Jaiswal S, Rai A and Rao AR (2014), Livestock Genomics: Resources and Bio-Computational Applications, In Rao AR, Govil JN (Eds.), Biotechnology Vol. 6: Bioinformatics and Computational Biology, Chapter 16, page 343-377, Studium Press LLC, USA.
5. Rao AR, Sahu T K, Singh N (2013), Spliceomics: the OMICS of RNA splicing, In Barh D, Zambare V, and Azevedo V(Eds.), OMICS: Applications in Biomedical,Agricultural, and Environmental Sciences, Chapter-8, Page:199-222, CRC Press, Taylor & Francis Group, LLC, USA.
6. Shukla A, Chandra P, Somvanshi P, Sahu TK, Garg N, Manohar MK and Mishra BN(2012), Converging Nanobiotechnology and Bioinformatics: Approach towards Medical Diagnostics, In Roy AK (Ed.), Applied Computational Biology and Statistics in Biotechnology and Bioinformatics. Chapter 29, Page: 721-729, New Delhi, India: New India Publishing Agency.
7. Rajan S and Sahu TK (2009), Decision support tools for variety and disease identification in mango, In Govindakrishnan PM, Singh JP, Lal SS, Dua VK, Rawat S and Pandey SK(Eds.), Information Technology Applications in Horticultural Crops, Chapter 14, pp.121-124, Shimla, India: CPRI.
8. Rajan S, Sahu TK (2009), “Knowledgebase on mango and horticulture technologies”, In Govindakrishnan PM, Singh JP, Lal SS, Dua VK, Rawat S and Pandey SK(Eds.), Information Technology Applications in Horticultural Crops, Chapter 28, pp.201-210, Shimla, India: CPRI.