Learning to handle numbers and crunching them to infer biological meaning.

I am pursuing research in translational cancer research and using computational tricks to address problems posed by Biologists. I use pre-developed tools or algorithms to make sustainable workflows for future use to address biological queries or at times develop my own methods or hack into the established methods to provide solutions. I have already worked with Exome, RNA-Seq (bulk), ChiP-Seq & Microarray data for answering and solving complex biological problems. My next aim is to learn end-to-end of DNA methylation ( I have some knowledge of analyzing it for a single project) analysis of EPICarray and venture to the world of scRNA-Seq. My work is mostly integrative omics and gene regulatory network construction assessing the genomic, tranacriptomic and epigenomic layers. As of now my intentions for the research field is more moving towards clinical genomics and pushing towards Precision/Personalized Medicine, so I believe scRNA-Seq will be a huge asset for the future. I code in R(for statistical analysis and visualization), make processing workflows in shell/bash. I am a beginner in python. I have knowledge of Java and C/C++ but since my workplace does not use it so they are not much into use. I am now interested in learning some new scripting languages and used modular language tools or workflow management tools that can be employed for analysis of large scale NGS datasets. I am interested in Machine Learning and Deep Learning application in omics driven healthcare. Have dabbled a bit into this space but would like to pursue more such avenues and master it up.