Mr. Sanjeev Kumar
Sanjeev Kumar
Citations and H index

Research Interest

  • Biotic-abiotic stress of Agricultural Crop
  • Data Mining of Biological Data
  • Gene Regularly Network


  • Stochestic Processes (AS 607)
  • Tool & Technologies of Biological Data Mining (BI 525)
  • Biological Databases & Data analysis (BI 511)

Important Projects

  • Computational Analysis of Potato- Interaction Related to Late Blight Disease
  • Algorithms for Gene Classification based Ongene Expression Data
  • Establishment of National Agricultural Bioinformatics Grid in ICAR (April 2010 to Mar 2013) Funded by NAIP

  • Identification & Characterization of Genomic Sequences Responsible for ... in some level loops

  • Modeling Network of gene responses to abiotic stress in rice
  • A New Distributed Computing Framework for Data Mining (DIT funded project)
  • Development of an Improved Hybrid De-novo Whole Genome Assembler.

  • Machine Learning Approach for Binning of Metagenomics Data.

  • Platform or Integrated Genomics Warehouse

  • Algorithm for Gene Classification based on Gene Expression Data

  • Microbial domain research projects on computational aspects.

  • Gene Regulatory Networks Modelling For Heat Stress Responses of Source and Sink for Development of Climate Smart Wheat

  • A New Distributed Computing Framework for Data Mining

  • Genomics assisted crop improvement and management.

  • Phenomics of Moisture Deficit Stress Tolerance and Nitrogen Use Efficiency in Rice and Wheat – Phase II.

  • ICAR Consortium Research Platform on Genomics project entitled “Computational and Analytical Solutions for High-throughput Biological Data”- ICAR-IARI, New Delhi

  • Characterization, Evaluation, Genetic Enhancement and Generation of Genomic Resources for Accelerated Utilization and Improvement of Minor Pulses.

  • Computational biology approach for deciphering transcriptome and proteomic changes in rice-microbial interaction system.

  • Exploring the Epigenetic Control of Heat Stress Responses in Wheat for Characterizing the Regulatory Networks Associated with Thermotolerance.

  • Structural and functional genomics of potato and its pest / pathogen using bioinformatics approaches.

Awards and Honours

  • Qualified National Eligibility Test (NET) in Agricultural Statistics in the year of 1997-98 conducted by Agricultural Scientist Recruitment Board, ICAR, New Delhi.
  • Qualified National Eligibility Test (NET) in Computer Application in Agriculture in the year of 1998-99 conducted by Agricultural Scientist Recruitment Board, ICAR, New Delhi.

Important Publications

  • Varshney,N., Kashyap,D., Behra,S.K., Saini,V., Chaurasia,A., Kumar, S. & Jha, H.C. (2023) Predictive profiling of gram-negative antibiotics in CagA oncoprotein inactivation: a molecular dynamics simulation approach, SAR and QSAR in Environmental Research, 34:6, 501-521 iconpdf

  • Varshney, N., Kumar, S., Baral, B., Kashyap, D., Singh, S., Kandpal, M., Bhandari V, Chaurasia A, Kumar S, Jha, H. C. (2023). Unraveling the Aurora kinase A and Epstein Barr Virus nuclear antigen 1 axis in Epstein Barr Virus associated gastric cancer. Virology, 109901. iconpdf

  • Prabhakaran P, Hebbani AV, Menon SV, Paital B, Kumar, S., Kumar S, Singh MK, Sahoo DK and Desai PPD (2023) Insilico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning. Front. Microbials. 14:1194794. iconpdf

  • Sharanbasappa, Mishra, D.C., Sharma,A., Kumar, S., Maji, A.K., Budhlakoti, N., Sinha, D. A Deep Clustering based Novel Approach for Binning of Metagenomics Data. Current genomics. iconpdf

  • Madival, S. D., Mishra, D.C., Sharma, A., Kumar, S., Maji, A. K., Budhlakoti, N., Sinha, D., & Rai, A. (2022). A Deep Clustering-based Novel Approach for Binning of Metagenomics Data. Current Genomics, 23(5), 353-368.iconpdf

  • Ramtekey, V., Susmita C., Kumar, S., Sripathy K. V., Sheoran, S., Udaya B. K., Bhojaraja N. K, Kumar, S., Singh, A.N., Singh, H.V. (2022) Seed longevity in legumes: Deeper insights into mechanisms and molecular perspectives; Frontiers in Plant Science. iconpdf

  • Tiwari, D., Murmu, S., Indari, O., Jha, H. C., & Kumar, S. 2022. Targeting Epstein‐Barr virus dUTPase, an immunomodulatory protein using anti‐viral, anti‐inflammatory and neuroprotective phytochemicals. Chemistry & Biodiversity iconpdf

  • Priyadarshi, M. B., Sharma, A.,Chaturvedi, K.K., Bhardwaj, R., Lal, S.B., Farooqi, M.S., Kumar, S., Mishra, D.C., Singh, M. (2022). Machine Learning Algorithms for Protein Physicochemical Component Prediction Using Near Infrared Spectroscopy in Chickpea Germplasm. Indian J. Plant Genet. Resour. 35(1): 44–48. iconpdf

  • Sinha, D., Sharma, A., Mishra, D.C., Rai, A., Lal, S.B., Kumar, S., Farooqi, Md.S., Chaturvedi K. K. (2022). MetaConClust - Unsupervised Binning of Metagenomics Data Using Consensus Clustering. Current Genomicsiconpdf

  • Turabe, F. MHU, Chirumamilla, C.S., Novo, C.P., Wong, BHS., Kumar, S., KwanSze, S., Berghe, W.V., Verma, N.K, (2021) The steroidal lactone withaferin A impedes T-cell motility by inhibiting the kinase ZAP70 and subsequent kinome signalling, Journal of Biological Chemistry; 3;297(6):101377; iconpdf

  • Mishra P., Tondon G., Kumar M., Paital B., Swain S.S., Kumar S. and Samanta L. (2021) Promoter sequence interaction and structure based multi-targeted (redox regulatory genes) molecular docking analysis of vitamin E and curcumin in T4 induced oxidative stress model using H9C2 cardiac cell line. J Biomol Struct Dyn. 31:1-20. | iconpdf

  • Kumar, R.R., Goswami, S., Rai, G.K., Jain, N., Singh, P.K.,Dwijesh Chandra Mishra, Chaturvedi, K.K, Kumar, S, Singh, B., Singh, G.P.,Rai, A, Chinnusamy, V., Praveen, S. (2020). Protection from Terminal Heat Stress: a Trade-Off between Heat-Responsive Transcription Factors (HSFs) and Stress-Associated Genes (SAGs) under Changing Environment. Cereal Research Communications.iconpdf

  • Mishra,D.C,Arora, D., Kumar, R.R., Goswami, S., Varshney, S., Budhlakoti, N., Kumar, S., Chaturvedi, K.K., Sharma, A., Chinnusamy, V. and Rai, A., 2020. Weighted gene co-expression analysis for identification of key genes regulating heat stress in wheat. Cereal Research Communications, pp.1-9. iconpdf

  • Anu Sharma, Dwijesh Chandra Mishra, Neeraj Budhlakoti, Anil Rai, Shashi Bhushan Lal and Kumar Sanjeev (2020). Algorithmic and computational comparison of metagenome assemblers. Indian Journal of Agricultural Sciences. 90 (5): 847–54.

  • Farooqi Mohammad Samir, Mishra DC, Chaturvedi KK, Rai Anil, Lal SB, Kumar Sanjeev, Bhati Jyotika and Sharma Anu. (2019) A Review on Recent Statistical Models for RNA-Seq. Journal of Applied Bioinformatics & Computational Biology. 8:1 DOI: 10.4172/2329-9533.1000162.iconpdf

  • Kumar Ranjeet R, Singh Khushboo, Ahuja Sumedha,Tasleem Mohd, Singh Indra, Kumar Sanjeev, Grover Monendra, Mishra Dwijesh, Rai Gyanendra K, Goswami Suneha, Singh Gyanendra P, Chinnusamy Viswanathan, Rai Anil, Praveen Shelly (2018). Quantitative proteomic analysis reveals novel stress-associated active proteins (SAAPs) and pathways involved in modulating tolerance of wheat under terminal heat. Functional & Integrative Genomics, pp 1–20.iconpdf

  • Mishra Dwijesh Chandra *, Kumar Sanjeev, Lal SB, Saha Arijit, Chaturvedi KK,Budhlakoti Neeraj and Rai Anil (2018). TAGPT: A Web Server for Prediction of Trait Associated Genes using Gene Expression Data. Annals of Genetics and Genetic Disorders. 1(1): 1003.

  • Kumar Ranjeet Ranjan; Goswami Suneha; Singh Khushboo; Dubey Kavita; Rai Gyanendra K; Singh Bhupinder; Singh Shivdhar; Grover Monendra; Mishra Dwijesh; Kumar Sanjeev; Bakshi Suman; Rai Anil; Pathak Himanshu; Chinnusamy Viswanathan; Praveen Shelly. (2018) Characterization of Novel Heat-Responsive Transcription Factor (TaHSFA6e) Gene Involved in Regulation of Heat Shock Proteins (HSPs) - a Key Member of Heat Stress-Tolerance Network of Wheat. Journal of Biotechnology. Volume 279, Pages 1-12. iconpdf

  • Kumar A, Farooqi MS, Mishra DC, Kumar S, Rai A, Chaturvedi KK, Lal SB, Sharma, A. (2018). Prediction of miRNA and Identification of their Relationship Network Related to Late Blight Disease of Potato. Microrna. 7(1):11-19. doi: 10.2174/2211536607666171213123038. PubMed PMID: 29237394.

  • Singh Indra, Smita Shuchi, Mishra Dwijesh C., Kumar Sanjeev, Singh Binay K., Rai Anil.Abiotic Stress Responsive miRNA-Target Network and Related Markers (SSR and SNP) in Brassica juncea. Frontiers in Plant Science.iconpdf

  • Lal, S. B., Sharma, A., Chaturvedi, K. K., Farooqi, M. S.,Sanjeev Kumar, Mishra, D. C. (2017). State-of-the-Art Information Retrieval Tools for Biological Resources. Web Semantics for Textual and Visual Information Retrieval, A volume in the Advances in Data Mining and Database Management (ADMDM) Book Series, IGI-Global, Pp. 203-226

  • Mishra, DC, Smita, S, Singh I, Devi, MN,Sanjeev Kumar, Farooqi, MS, Chaturvedi, KK, Rai, A (2017). Prediction of novel putative miRNAs and their targets in buffalo. Indian Journal of Animal Sciences, 87(1), 59–63.

  • Gupta, OP, Nigam, D, Dahuja, A, Sanjeev Kumar,Vinutha ,T, Sachdev, A and Praveen, S (2017).Regulation of isoflavone biosynthesis by mirnas in two contrasting soybean genotypes at different seed developmental stages. Frontiers in Plant Sciences 8:567, DOI: 10.3389/fpls.2017.00567

  • Bhati, J, Chaduvula, PV, Sanjeev Kumar, Marla, SS and Rai, A (2016); In-silico prediction and functional analysis of salt stress responsive genes in Rice (Oryza sativa), Journal of Rice Research 4: 164. DOI:10.4172/2375- 4338.1000164.

  • Singh, BK, Mishra, DC, Yadav, S, Ambawat, S, Vaidya, E, Tribhuvan, KU, Kumar, A,Sanjeev Kumar, Chaturvedi, KK, Rani R, Yadav, P, Rai, A, Rai, PK, Singh, VV, Singh, D (2016).Identification, characterization, validation and cross-species amplification of genic-SSRs in Indian Mustard (Brassica juncea). Journal of Plant Biochemistry and Biotechnology. DOI: 10.1007/s13562-016-0353-y.

  • Sanjeev Kumar, Ambreen, H, Variath, MT, Rao, AR, Agarwal, M, Kumar, A, Goel, S and Jagannath, A (2016). Utilization of molecular, phenotypic, and geographical diversity to develop compact composite core collection in the oilseed crop, safflower (Carthamus tinctorius L.) through maximization strategy. Frontiersin Plant Science, 7, 1554, DOI:10.3389/fpls.2016.01554

  • Kumar, B, Hooda, KS, Yadav, OP, Gogoi, R, Kumar, V, Kumar, S, Abhishek, A, Bhati, P, Javaji, CS, Yatish, KR, Singh, V, Das, A, Mukri, G, Varghese, E, Kaur, H and Malik, V (2016). Inheritance study and stable sources of maydis leaf blight (Cochliobolus heterostrophus), Cereal and Research Communications, 44(3), 424–434.

  • Jasna, VK, Roy, BR, Padaria, RN, Sharma, JP, Varghese, E, Chakrabarty, B, Loganandhan, N and Kumar, S (2016). Institutional Role in Climate Resilience Building Process in Rainfed Agro-ecosystem. Journal of Community Mobilization and Sustainable Development, 11(2), 138-144.

  • Goswami, S, Kumar, RR, Dubey, K, Singh,JP, Tiwari, S, Kumar, A Smita, S, Mishra, DC, Kumar, S, Grover, M, Padaria, JC, Kala,YK, Singh, GP, Himanshu Pathak, H, Chinnusamy, V, Rai, A, Praveen, S and Rai, RD (2016). SSH analysis of endosperm transcripts and characterization of heat stress regulated expressed sequence tags in bread wheat. Frontiers in Plant Science, 7, 1230.

  • Rai, N, Mishra, DC, Kumar, S, Rai, A, Chaturvedi, KK, Lal, SB, Kumar, A, Farooqi, MS, Majumdar, PG, and Archak, S. (2016). Genome analysis of Rhizobium species using codon usage bias tools International Conference on Bioinformatics and Systems Biology (BSB, 2016). IEEE Xplore digital library, Page(s):1 – 4.

  • Suneha Goswami, Ranjeet R. Kumar, Kavita Dubey, Jyoti P. Singh, Sachidanand Tiwari, Ashok Kumar, Shuchi Smita, Dwijesh C. Mishra, Sanjeev Kumar, Monendra Grover, Jasdeep C. Padaria, Yugal K. Kala, Gyanendra P. Singh, Himanshu Pathak, Viswanathan Chinnusamy, Anil Rai, Shelly Praveen and Raj D. Rai. (2016). SSH analysis of endosperm transcripts and characterization of heat stress regulated expressed sequence tags in bread wheat. Frontiers in Plant Science, 7, 1230.

  • Jyotika Bhati, Pavan Chaduvula K, Anil Rai, Kishore Gaikwad, Soma Marla S and Sanjeev Kumar(2016). In-Silico Prediction and Functional Analysis of Salt Stress Responsive Genes in Rice (Oryza sativa). J Rice Res., 4:1

  • Nigam Deepti, Kadimi Puneet K, Sanjeev Kumar Mishra Dwijesh C and Rai Anil. (2015). Computational analysis of miRNA-targets Community Network reveals cross talk among different metabolism. Genomics Data. 5, 292-296. .

  • Pavan K Chaduvula, Jyotika Bhati, Anil Rai, Kishore Gaikwad, Soma S Marla, M Elangovan, Sanjeev Kumar (2015): Insilico expressed sequence tag analysis in identification and characterization of salinity stress responcible genes in Sorghum bicolour, Australian Journal of Crop Sciences, 9(9), 799-806. .

  • S Singh, P K Agrawal and Sanjeev Kumar (2006). Physiological Analysis of Growth and Productivity in Wheat Cultivars. Indian J. Plant Physiology, 11(1), 57-62.

  • N Jain, A Bhatia, R Kaushik, Sanjeev Kumar, H C Joshi and H Pathak (2005). Impact of post methanation distillery affluent irrigation on ground water quality. Environmental Monitoring and Assessment, Vol. 110, 243-255.

  • Sanjeev Kumar, A R Rao and V K Bhatia (2004). Bayesian Estimation of Heritability in Animal Breeding Experiments Under 2-way Nested Classification. Journal of Indian Society of Agricultural Statistics, 58(3), 1-11.

  • Kaur R, Sanjeev Kumar and H P Gurung (2002). A Pedo-transfer function (PTF) for estimating soil bulk density from basic soil data and its comparison with existing PTFs. Australian Journal of Soil Research, Vol. 40, 847-857.

  • Ravinder Kaur, Sanjeev Kumar, H P Gurung, J S Rawat, A K Singh, Shiv Prasad and Geeta Rawat (2002). Evaluation of Pedo-Transfer Functions for Predicting Field Capacity and Wilting Point Soil Moisture Contents from Routinely Surveyed Soil Texture and Organic Carbon Data. Indian Journal of Soil Science, 50(2), 205-208.

  • Nigam Deepti, Sanjeev Kumar, Mishra D C, Rai Anil, Smita Suchi and Saha Arijit (2014). Synergetic regulatory networks mediated by microRNAs and transcription factors under Salinity, Heat and Drought stress in Oryza Sativa spp. GENE (DOI: 10.1016/j.gene.2014.10.054)

  • Manju Mary Paul, Anil Rai and Sanjeev Kumar (2014). Classification of cereal proteins related to abiotic stress based on their physicochemical properties using support vector machine. Current Science, Vol. 107, No. 8, 1283-89.

  • Bhati Jyotika, Chaduvula Kumar Pavan, Rani Ruchi, Sanjeev Kumar and Rai Anil (2014). In-silico prediction and functional analysis of salt stress responsive genes in Maize (Zea mays). European Journal of Molecular Biology and Biochemistry, 1(4):151-157.

  • Rao A R and Sanjeev Kumar (2001). Bayesian estimation of heritability using GIBBS sampling for half-sib mating design. Indian Journal of Applied Statistics, Vol. 6, 12-26.

  • Book Chapter:

  • D.C. Mishra, Sanjeev Kumar, Anil Rai, Sudhir Srivastava (2013). Machine Learning Techniques and Its Application in Bioinformatics. Information and Knowledge Management: Tools, Techniques and Practices. Publisher: New India Publishing Agency.

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