I am a computational biologist studying the role of chromatin structure/dynamics and non-coding regions including enhancers, promoters, insulators and their role in gene regulation. The mission of my lab is the integration of omics data to explore and better understand the functional mechanisms of the non-coding genome and to provide accessible tools for the community to accelerate discovery in this field. The long-term goal of my research is to develop innovative computational approaches and to use cutting-edge experimental assays, such as single cell and genome editing, to systematically analyze sources of genetic and epigenetic variation that affect gene regulation in development and in different human traits and diseases.
I have developed several computational strategies to unravel the relationship between epigenetic regulators, chromatin structure and DNA sequence, and how these factors influence gene expression patterns. For example, HAYSTACK (http://github.com/pinellolab/haystack_bio ) uses histone modification and gene expression data measured across multiple cell-lines to identify the most epigenetically variable regions of the genome to find cell-type specific regulators and to predict cell-type specific chromatin patterns.
I fully embraced the revolution in functional genomics made possible by the novel genome editing approaches such as CRISPR/Cas9. I have developed computational tools to quantify and visualize the outcome of genome editing experiments, that are nowadays the standard the facto for the community. For example, CRISPResso (http://github.com/pinellolab /CRISPResso2 ), an integrated software pipeline for the analysis and visualization of CRISPR/Cas9 outcomes from deep sequencing experiments. This analysis can be used for the assessment of on-target editing efficiency as well as for analysis of off-target editing at selected loci with increased likelihood of off-target mutagenesis. A streamlined and user-friendly website is also available at crispresso2.pinellolab.org
I am also actively involved in the single-cell community and I am part of the Human Cell Atlas initiative, proposing computational strategies to model gene expression variability, its relationship with chromatin accessibility and DNA methylation, and to reconstruct developmental trajectories. Recently we have developed a method called STREAM (Single-cell Trajectory Reconstruct Exploration and Mapping). This is one of the first methods to perform trajectory inference not only from transcriptomic data but as well epigenomic data such as scATAC-seq (https://github.com/pinellolab/stream). We have also created an interactive website that can be used interactively to compute trajectories and a reference database of precomputed trajectories for several published studies available at stream.pinellolab.org.
The full list of software developed in my lab is available at: github.com/pinellolab