The Computational Biology and Omics group performs a number of cutting-edge analyses to interpret the complex, dynamic and large-scale datasets obtained from time-series transcriptomics, proteomics and metabolomics. The members of our group comes with diverse mathematical, computational and statistical expertise with wide international exposure.
Depending on the type of data, the team develops a number of custom-made computational, mathematical and data analytic tools to investigate and predict an optimal outcome of a desired experiment. For example, for the optimal production of an industrially relevant compound, the models developed can test numerous targets in silico and identify/rank the best targets before actual experiments are performed. This reduces laborious experiments, thereby, saving valuable time and cost for projects.
ABioTrans is a software tool that identifies gene expression variability through entropy and noise analyses. It is focused on commonly-used statistical techniques, namely, Pearson and Spearman rank correlations, Principal Component Analysis (PCA), k-means and hierarchical clustering, Shannon entropy, noise (square of coefficient of variation), differential expression (DE) analysis, and gene ontology classifications.
GeneCloudOmics is a web-based bio-statistical/informatics tool developed in R for gene expression analysis.
GeneCloudOmics allows the user to directly read RNA-Seq or Microarray data files, pre-process them and perform several statistical and data mining analyses. It provides easy options for multiple statistical distribution fitting, Pearson and Spearman rank correlations, PCA, k-means and hierarchical clustering, differential expression (DE) analysis, Shannon entropy and noise (square of the coefficient of variation) analyses, Entropy analysis, support vector machine (SVM) and Random Forest clustering, tSNE and SOM analyses.
GeneCloudOmics also provides several gene and protein datasets analyses such as gene ontology (GO) classifications, pathways enrichment, protein-protein interaction (PPI), subcellular localization, protein complex enrichment, protein domains annotation and Protein Sequence Download.
Kumar is heading the Computational Biology & Omics laboratory at BII and SIFBI, A*STAR. He is also an adjunct Principal Investigator at the Synthetic Biology center (SynCTI), National University of Singapore. Prior, he was an Associate Professor in Systems Biology at the Institute for Advanced Biosciences, Keio University, Japan. He serves the editorial board of Genomics (Elsevier) and Scientific Reports (Nature Publishing Group). He has lead teams in Computational Biology, Systems Biology, Bioinformatics and Statistical Genetics. In particular, he has used original ideas, utilizing fundamental physical and statistical laws, to investigate multi-dimensional datasets, deterministic and stochastic modelling of complex protein signaling and metabolic networks. He has authored over 65 scientific articles, largely as corresponding author, which includes a single-authored book on Immuno Systems Biology (Springer). He has obtained several research grants, and has been an international grant reviewer. He has also presented invited/keynote talks at numerous international conferences. In 2013, 2015 and 2018, he founded and chaired the Symposium on Complex Biodynamics and Networks (cBio).
Computational Biology, Systems Biology, Bioinformatics, Data Analytics, Genomics, Cancer & Immunology, Synthetic Biology