Overview

Thanks to high-throughput biotechnologies, we are assisting a massive stacking of genomic, transcriptomic, proteomic and metabolomic data. Molecular biology is nowadays turning into a "data-rich" science. This unprecedented richness of raw data is meaningless until we cannot extract the interesting and useful "needles" of information from the big "haystack" of available data. This allows us to have at the same time both a "bird’s eye" and a "magnified" overview on a biological sample from a wide range of medically relevant disorders. "The answer is there, blowing in the bulk" (thanks Zimmerman 😃). However, this huge amount of raw data is of little use without groundbreaking bioinformatics and biostatistics methodologies able to disentangle, process, analyze and interpret them.

In my work, I strike a balance between two ways of doing science. On one hand I focus on the development and application of statistical and machine learning approaches for analyzing high-throughput and heterogeneous biomedical data. On the other hand, I package such approaches into modular, scalable and user-friendly tools, in order to share them with the scientific community.

Skills by Interest Area

  1. Omics Data Analysis
    • Tailored analytics solutions for NGS (WES, bulk RNAseq, scRNAseq, ChIP-Seq, CUT&RUN, CRISPR-screen, Visium 10X Genomics) and proteomics data (LC-MS/MS)
    • Variant calling analysis to detect point and structural mutations
    • Differential gene/protein expression and pathway enrichment analysis
    • Differential binding/accessibility analysis
    • Clustering analysis to detect patient subgroups on the basis of protein expression profiles
    • Motif analysis
    • Spatial transcriptomics data analysis
    • Quantification of chromatin signal metaprofiles
    • Programmatic configuration and browsing of genomic data via UCSC track hubs
    • Shiny web applications to interactively explore multi-omics data
  2. Machine Learning
    • Implementation of methods for analyzing biontologies
    • Implementation of modules for building bioinformatics pipelines
  3. Graphs
    • Analysis of biomolecular networks and biontologies