Translational Bioinformatics & Computational Genomics Lab | Department of Life Sciences

Translational Bioinformatics & Computational Genomics Lab

  • Variation browser wheel showing snips in genes in a tropical disease.

Lab is primarily working on disease genomics using bioinformatics and genomics approach to identify and develop gene signatures for human diseases. Single nucleotide variants plays important role in altering the proper functioning of proteins. Functional  variations or defects in gene regulation cause phenotypic variation or diseases. We conduct our research using an integrative approach where along with bioinformatics we also incorporate genomics, transcriptomics. Currently working on Molecular modelling of molecules of interest and Next generation sequencing of tropical region diseases and Cancer.

Cancer Genomics and Bioinformatics

Existing projects are emphasized on meta-analysis of cancer gene expression studies. This will provide information of significant genes and aberrant pathways with more confidence level observed to be involved in a specific cancer type or are present at exclusive stage of cancer. We along with my collaborators working towards development of colorectal cancer based drug resistance cell lines and identification of drug resistance mutations. We have made repositories exclusively for colorectal cancer like Colorectal Cancer Gene Database (CoReCG) has been developed ( which contain all validated colon-rectal cancer genes information.

Disease Genomics

Complete transcriptome sequencing of patient samples to analyze the expression of genes which are differentially regulated neglected tropical diseases. Further, the co-expression of microRNAs, lncRNAs and coding genes, will be deciphered to disentangle the molecular pathways that are triggered during the progression of these diseases from early stage to advance stages or malignancy. We also study comprehensive screening of variants frequency in the sample population and expression of alternative transcripts which will elucidate the reason for the heterogeneity observed in these patients. Moreover, we also work on plant transcriptome based big data to identify gene signatures for plant abiotic stress using meta-analysis and systems biology approaches.

Genome Sequencing Project

Group is also working on whole genome sequencing and Transcriptomic Profiling and data analysis of multi-centric National Agriculture Science Fund (NASF) funded ICAR research project on Pashmina goat. Consortium leader: Sher-e-Kashmir University of Agricultural Sciences and Technology, Division of Biotechnology, Shuhama, Srinagar.

Bioinformatics Tools, databases and Softwares

We have made few very significant tools and databases, some of them are: Colorectal Cancer Gene Database (CoReCG) has been developed ( which contain all validated colon-rectal cancer genes information.

TM-Aligner: Multiple sequence alignment tool for transmembrane proteins with reduced time and improved accuracy. TM-Aligner ( is an unrestricted (in terms of length and number of sequences) tool which can align transmembrane proteins even at low level of sequence identity, accurate alignment is possible at low sequence identity because biological membrane proteins have a transmembrane between cytoplasmic and extracellular regions. TM-Aligner has been designed as a unique global, progressive alignment method, with the aim of high-quality alignment which has almost no limitation over input data-set size. TM-Aligner uses UPGMA method to create an initial guide tree that describes sequence relatedness. To identify transmembrane regions TMHMM  has been employed and alignment were made using dynamic programming and Wu-Manber string matching algorithm to calculate sequence distances and incorporate local matches in global alignment.

My research rely on the massive data production and analysis of large-scale data from high-throughput Next Generation Sequencing (NGS) or of microarray origin. Additionally, we are also interested in genetic linkage and association studies to identify disease associated genetic variants.



Dr. Ashutosh Singh