All India Institute of Medical Sciences, New Delhi, India, Ph.D., 1998
2016-present, Group Leader, Translational Bioinformatics Group
2005-2016, Staff Research Scientist, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India
2001-2005, Member, Scientific Working Group, Bioinformatics Initiative, TDR/WHO
2001-2005, Coordinator, WHO/TDR Regional training centre for Bioinformatics at ICGEB
2001-2005, World Health Organization Fellow, University of Pennsylvania, USA
2001, World Health Organization Fellow, Sanger Centre, UK
1998-2004, Staff Research Scientist, Malaria Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi
Since 2000, Founder and In-charge, Bioinformatics facility at the ICGEB, New Delhi, India
Since 1998, System Administrator, ICGEB, New Delhi
1998, DST fellow, All India Institute of Medical Sciences, New Delhi, India
The scientific interests of his Group include the use of computational biology tools to solve research problems in the post-genomic era.
- Specifically, the group is interested in:-
- Development and application of artificial intelligence-based methods to address complex biological data classification problems;
- Computer-aided drug designing; including molecular modeling, molecular dynamics, homology modeling, virtual screening and cheminformatics for lead identification;
- Application of comparative genomics methods to identify novel drug targets and gene expression regulatory elements like miRNAs and transcription factors;
- Applications for Systems Biology for translational bioinformatics;
- Computational and statistical analysis of NGS (Next Generation Sequence) data and;
- Development of biological databases and algorithms.
The group has organized several International Bioinformatics Workshops.
International Bioinformatics workshops, including those funded/organized by WHO, NAS (USA), and regional universities.
Collaborative research and Computational Biology support to different research Groups at ICGEB. Setting up of high-performance Linux based cluster for computational biology. In charge of maintenance of the IT infrastructure and computerization of ICGEB New Delhi operations.
Bioinformatics tools and servers
The bioinformatics servers developed by the group are freely available at here
Zeeshan M., Kaur I., Joy J., Saini E., Paul G., Kaushik A., Dabral S., Mohmmed A., Gupta D., Malhotra P. 2017. Proteomic Identification and Analysis of Arginine-Methylated Proteins of Plasmodium falciparum at Asexual Blood Stages. J Proteome Res. Jan 3. doi: 10.1021/acs.jproteome.5b01052
Kaushik, A., Shakir Ali, S., Gupta, D. 2017. Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways. Nat. Sci. Rep. , 40450; doi:10.1038/srep40450 (2017)
Kaur I., Zeeshan M., Saini E., et al. Widespread occurrence of lysine methylation in Plasmodium falciparum proteins at asexual blood stages. Sci Rep . 2016;6. doi:10.1038/srep35432.
Kumar R., Gupta D. Identification of CYP1B1-specific candidate inhibitors using combination of in silico screening, integrated knowledge-based filtering, and molecular dynamics simulations. Chem Biol Drug Des . 2016. doi:10.1111/cbdd.12803.
Kaushik, A., Bhatia, Y., Ali, S., Gupta, D. 2015. Gene network rewiring to study melanoma stage progression and elements essential for driving melanoma. PLOS ONE. 10(11):e0142443. doi: 10.1371/journal.pone.0142443.
Kaushik, A., Saraf, S., Mukherjee, S.K., Gupta, D. 2015. miRMOD: A tool for identification and analysis of 5′ and 3′ miRNA modifications in Next Generation Sequencing small RNA data. PeerJ. 3:e1332; DOI 10.7717/peerj.1332.
Saraf, A., Sanan-Mishra, N., Gursanscky, N.R., Carroll, B.J., Gupta, D., Mukherjee, S.K. 2015. 3′ and 5′ microRNA-end post-biogenesis modifications in plant transcriptomes: evidences from small RNA next generation sequencing data analysis. BBRC 467:892-9.
Jagga Z. and Gupta D. 2015. Machine learning for biomarker identification in cancer research- developments towards its clinical application. Future Medicine. 12:371-387.
Tajedin L., Anwar M., Gupta D, and Tuteja R. 2015. Comparative insight into nucleotide excision repair components of Plasmodium falciparum. DNA repair 28:60-72
Subramaniam. S., Mehrotra, M., Gupta, D. 2012. Development of target focused library against drug target of P. falciparum using SVM and molecular docking. J Cheminformatics. (Suppl 1), P48
Subramaniam, S., Mehrotra, M., Gupta, D. 2011. Support Vector Machine Based Prediction of P. falciparum Proteasome Inhibitors and Development of Focused Library by Molecular Docking. Combinatorial Chemistry & High Throughput Screening 14, 898-907
Subramaniam, S., Mehrotra, M., Gupta, D. 2011. Support Vector Machine Based Classification Model for screening Plasmodium falciparum proliferation Inhibitors and non-Inhibitors. Biomed Eng Comp Biol 3, 13–24 doi: 10.4137/BECB.S7503
Rathore S, Jain S, Sinha D, Gupta M, Asad M, Srivastava A, Narayanan, Ramasamy G, Chauhan VS, Gupta D, Mohmmed A. 2011. Disruption of a mitochondrial protease machinery in Plasmodium falciparum is an intrinsic signal for parasite cell death. Cell Death Dis. 2:e231. doi: 10.1038/cddis.2011.118
Gupta, D., Tuteja, N., 2011. Chaperones and foldases in endoplasmic reticulum stress signaling in plants. Plant Signal Behav 6, 2320
Ramana, J., Gupta D. 2010. Machine Learning Methods for Prediction of CDK-Inhibitors. PLoS ONE 5, e13357.doi:10.1371/journal.pone.0013357
Ramana, J., Gupta, D. 2010. FaaPred: A SVM-Based Prediction Method for Fungal Adhesins and Adhesin-Like Proteins. PLoS One 5 [doi: 10.1371/journal.pone.0009695] PubMed
Ramana, J., Gupta, D. 2009. LipocalinPred: a SVM-based method for prediction of lipocalins. BMC Bioinformatics 10, 445
Ramana, J., Gupta, D. 2009. ProtVirDB: A database of protozoan virulent proteins. Bioinformatics 25, 1568-1569
Subramaniam, S., Mohmmed, A., Gupta, D. 2009. Molecular modeling studies of the interaction between plasmodium falciparum HslU and HslV subunits. J. Biomol. Struct. Dyn. 26, 473-479
Garg, A., Gupta, D. 2008. VirulentPred: A SVM based prediction method for virulent proteins in bacterial pathogens. BMC Bioinformatics 9, 62
Kalita, M.K., Nandal, U.K., Pattnaik, A., Anandhan, S., Gowthaman R., Kumar, M., Raghava, G.P.S., Gupta, D. 2008. CyclinPred: a SVM-based method for predicting cyclin protein sequences. PLOS One 3 [doi:10.1371/journal.pone.0002605] PubMed
Bahl, A., Brunk, B., Crabtree, J., Fraunholz, M., Gajria, B., Grant, G.R., Ginsburg, H., Gupta, D., Kissinger, J.C., Labo, P., Li, L., Mailman, M.D., Milgram, A.J., Pearson, D.S., Roos, D.S., Schug, J., Stoeckert, Cj. Jr., Whetzel, P. 2003. PlasmoDB : The Plasmodium genome resource. A database integrating experimental and computational data. Nucl. Acids Res. 31, 212-215
Kissinger, J.C., Brunk, B., Crabtree, J., Fraunholz, M., Gajria, B., Milgram, A.J., Pearson, D.S., Schug, J., Bahl, A., Diskin, S.J., Ginsburg, H., Grant, G.R., Gupta, D., Labo, P., Li, L., Mailman, M.D., McWeeney, S.K., Whetzel, P., Stoeckert, Cj. Jr., Roos, D.S. 2002. The Plasmodium genome database. Designing and mining a eukaryotic genomic resource. Nature 3, 490-492