Research
Comprehensive & Comprehensible Omics Data Analysis
The extensive generation of complex omics datasets, led by life sciences and clinical disciplines, has resulted in a vast number of approaches to process, model, and interpret large-scale data, providing information on the expression of genes, the accessibility of chromatin, the methylation status, and the mutational landscape in a broad spectrum of scenarios. Recent advances in single-cell and spatial technologies allow now researchers to study many of these phenomena at unprecedented resolution.
The bioinformatic analyses of such massive amounts of data, with this level of complexity, are challenging on many aspects, as they constantly prompt for innovations and developments in computational and quantitative disciplines.
We aim to develop bioinformatics methods and software for interpreting and integrating different omics layers with relevant clinical outcomes, enabling researchers to obtain a more comprehensive view of phenomena such as the regulatory transcriptome landscape. These methods need to be leveraged in a comprehensible manner by many researchers, with diverse backgrounds and skills; for this reason, we intend to implement analysis frameworks that guarantee accessible yet robust extraction of insight, by means of the combination of interactive and reproducible approaches.
Interactivity & Reproducibility
The choice of proper analysis tools, to be either adopted or newly developed, when addressing the challenges deriving from the specific scientific questions, is an essential decision for any scientist. Two essential aspects of scientific software are interactivity and reproducibility, which can be leveraged in concert to provide interfaces and frameworks where researchers can produce data analyses in an accessible, comprehensive and robust way.
Building this common ground for better understanding of the complex biological systems under inspection will be essential to foster interdisciplinary exchanges, reach better insights in efficient manners, and understand and build upon others’ ideas, data, observations, and results.
Open Science as Better Science
We believe that Open Science is better science. More rigorous, more inclusive, more efficient, more trustworthy, more reproducible. And ultimately more impactful for society and the health of patients.
We strive to contribute to open and reproducible science in the following manners:
- We aim to develop (release and maintain) high-quality statistical and computational tools, methods, software, frameworks, to make these well documented and widely applicable. We contribute to the Bioconductor project, an open source software collaborative effort and community for the analysis and understanding of genome-scale data
- We create code repositories containing the analyses included in our manuscripts, so that others can understand and build further on our contributions
- We post our manuscripts as preprints, e.g. on bioRxiv, as we believe this is an essential component for the transition towards modern and transparent modes for scientific dissemination. And of course, we invite our collaboration partners to do the same when we work together.