Computational Biology & Omics Lab


The Computational Biology and Omics lab is primarily based at the Bioinformatic Institute, ASTAR, Biopolis, Singapore. The lab specialises in a number of cutting-edge methodologies and analyses to interpret the complex, dynamic and large-scale datasets obtained from time-series transcriptomics, proteomics and metabolomics of living cells. The members of our group come with diverse mathematical, computational and statistical expertise with wide international exposure. Depending on the type of data and cell type, the team develops 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. To understand disease conditions, the dynamic models can predict target for suppressing immune or cancer response. Such systems biology approaches reduce laborious experiments, thereby, saving valuable time and cost for projects.


Biostatistical Tools


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.

ScatLay  identifies differential genes from gene expression data by using the overlap of 2 scatter plots. Plots are generated in log10 scale. The source code can be interacted via command-line interface.

ScatLay identifies differentially expressed genes by overlaying gene expression scatter plot of 2 different conditions on top of that of 2 replicates between the same condition. The non-overlapping genes are differentially expressed genes.


Senior Principal Investigator SELVARAJOO Kumar   |    [View Bio]  
Visiting Scientist MOHAMED Helmy 
Research Officer SIRBU Olga 
Senior Research Fellow YEO Hock Chuan 
Senior Post-Doctoral Research Fellow RASHID Md Mamunur 
Research Officer HEE Yan Ting 

Selected Publications