(Archived) Tools for studying gene expression and regulation

Tools for data management

In my laboratory, we deal with data from small-scale and high-throughput experiments. I rely on open-source tools to manage, analyze, and visualize data. In addition, I develop tools in the form of R packages to handle the specific kinds of data my lab generates, perform analyses, and present my research output.

The tools I developed for these purposes include:

A tool for quality assessment, analysis, and testing of RT-qPCR data (Paper) (Package)
A tool to conduct co-localization analysis on fluorescence microscopy images (Paper) (Package) (App)

Regulation of gene expression

The binding of a transcription factor to the promoter region of a coding gene induces or represses its transcription. Often, this binding is accompanied or preceded by the binding of other cofactors and changes in the chromatin organization. We need to identify the targets of individual factors and their interaction. We observe the regulation mediated by transcription factors as changes in the target expression. The binding of transcription factors and the changes in gene expression can be modeled using ChIP-seq and RNA-seq data. Therefore, integrating the two types of data is necessary to get a complete picture of the role of transcription factors in regulating gene expression. I apply this framework to study the role of transcription factors and microRNAs in controlling key Autophagy genes adipogenesis and cancer.

The tools I developed during this work include:

A tool to integrate binding and expression data to predict transcription factors combined function (Paper) (Workflow) (Package) (App)
A tool to accessing microRNA and transcription factor-gene expression correlations in cancer (Paper) (Package)
A database for correlation analysis between microRNA and gene expression in cancer (Paper) (App)