My research interests involve the development of methods and software for interactive and reproducible research, bringing together the fields of bioinformatics, statistics, genomics, and biology. I can do this thanks to the joint supervision of Prof. Wolfram Ruf and Prof. Miguel Andrade.
Being a Virchow Fellow at the Center for Thrombosis and Hemostasis, I can sit at the interface with many other disciplines and focus on translational bioinformatics. With platelets transcriptomics as a use case, I am particularly interested to develop methodologies and implement methods (software packages, web applications, …) for interactive and reproducible research in RNA-seq analysis and other -omics data.
I love working in the programming language, and with software from the Bioconductor project, where I also contributed some packages. In the time when I am not sitting behind a computer screen, I like to get physically overwhelmed by my three wonderful kids and spend time with my family.
PhD in Biostatistics/Bioinformatics, 2017
University Medical Center Mainz - IMBEI
MSc in Biomedical Engineering, 2008
Politecnico di Milano
BSc in Biomedical Engineering, 2005
Politecnico di Milano
idealsoftware package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation.
idealis implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis.
idealalso offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.
idealis distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.
pcaExplorersoftware package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports.
pcaExploreris implemented in R using the Shiny framework and exploits data structures from the open-source Bioconductor project. Users can easily generate a wide variety of publication-ready graphs, while assessing the expression data in the different modules available, including a general overview, dimension reduction on samples and genes, as well as functional interpretation of the principal components.
pcaExploreris distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/pcaExplorer/), and is designed to assist a broad range of researchers in the critical step of interactive data exploration.
iSEE(Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object.
iSEEis directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of
iSEEby applying it to explore a range of real transcriptomics and proteomics data sets.
Here are some detailed information on a couple of my latest contributions:
I am the maintainer or co-developer of the following R packages:
A package for exploring interactively any
SummarizedExperiment object, with an amazing support for reproducible research by meta-generating the required code.
You can find more on iSEE at the GitHub Organization page for it - https://github.com/iSEE.
I am the curator of the awesome-expression-browser list, filled with software and resources for exploring and visualizing (browsing) expression data
I designed and developed together with Denise Scherzinger TREND-DB, a Shiny application for exploring interactively Transcriptome 3’end diversification (TREND) data, as a companion to the original publication of Ogorodnikov et al.
🚧 WORK IN PROGRESS 🚧