Mattia Furlan

Post Doc
Post Doc

Research Lines

Genomic Science




Via Adamello 16, Milano, Italy

Social profiles

googlescholar twitter linkedin


Ph.D. in Complex Systems for Life Sciences

Dates: 2016 - 2019 (Discussion: 14/07/2020)
Institution: University of Turin
Thesis title: Modelling the dynamics of transcriptional and post-transcriptional regulation from RNA-Seq data
Supervisor: Dr. Mattia Pelizzola, IIT
Supervisor: Dr. Michele Caselle, UNITO

Master's degree in Complex Systems

Dates: 2014 - 2016
Institution: University of Turin
Mark: 110/110
Thesis title: Inference of Kinetic Rates in Time-course RNA-Seq Experiments
Supervisor: Dr. Michele Caselle, UNITO
Co-supervisor: Dr. Mattia Pelizzola, IIT

Bachelor's degree in Physics

Dates: 2011 - 2014
Institution: University of Turin
Mark: 110/110
Thesis title: Moist Convection in Planetary Boundary Layer
Supervisor: Dr. Elisa Palazzi, CNR-ISAC
Co-supervisor: Dr. Alexandre B. Pieri, CNR-ISAC


2019 - Present: Post Doc, Epigenomics and Transcriptional Regulation Group (IIT)
2016 - 2019: Ph.D. student, Epigenomics and Transcriptional Regulation Group (IIT), Computational Biology Group (INFN)
2014: Three months internship, Institute of Atmospheric Sciences and Climate (ISAC-CNR)


Computational Biology Bioinformatics RNA Metabolism Complex Systems


The main focus of my research activity is the study of the RNA life cycle, I address this topic with computational methods based on the mathematical modelling of RNA sequencing data.


RNA is one of the fundamental actors in the context of cellular biology and it is involved, directly or indirectly, in any cellular process. Therefore, the composition of the transcriptome is kept under strict regulation by the cell and adapted in response to external and internal stimuli. This control is mediated by many regulatory mechanisms involving any step of the RNA life cycle and their characterisation is mandatory to acquire a full comprehension of cellular biology.
The most common approach to investigate the RNA life cycle is to collect information about gene expression through RNA sequencing experiments, however, this datum reflects the choral effect of this complex plethora of modulations, for this reason, it must be carefully analysed to deconvolve all these contributes and avoid incorrect conclusions. For instance, the increase of a gene expression level between two conditions is usually assumed to be a proxy of its transcriptional induction, nonetheless, the same observation could be due to a modulation of transcripts stability.
To sum up, the investigation of RNA metabolism is indispensable and requires the development and application of dedicated computational approaches which combine mathematical modelling and inference; this is the scope of my research.


INSPEcT is R/bioconductor package (INSPEcT) for the genome-wide inference and study of RNA life cycle dynamics. It first release (de Pretis et al. 2015) allowed the estimation of synthesis, processing and degradation rates from the joint analysis of Total and Nascent RNA sequencing data; both at steady state and in time-course.
The quantification of Nascent RNA requires sophisticated techniques which significantly increase costs and complexity of the experiments. For this reason, I spent my PhD contributing to the extension of the package with novel routines for the study of transcriptional and post-transcriptional dynamics from Total RNA-seq data only (Furlan et al. 2019).
INSPEcT is now suitable to estimate the full set of kinetic rates from standard time-course RNA-seq experiments. At steady state, the tool provides the quantification of the processing over degradation rate ratio which is not informative about any specific layer of the RNA life cycle but allows the identification of post-transcriptional regulations between conditions.



I am currently collaborating with colleagues from the Polytechnic of Turin on the development of a bayesian framework for the study of RNA dynamics based on Gaussian Mixture Models. This approach, which includes genes clustering in the inference procedure, allows to exploit all the information available in the dataset with a consequent improvement of the quality of the models. Moreover, it provides a method for the automatic clustering of the kinetic rates according to non-trivial features (Mastrantonio et al. 2020).

I am also working on the study of RNA dynamics in the context of m6A metabolism. Specifically, I am developing a computational approach based on Nanopore sequencing data to investigate, at the single molecule and isoform resolution, the effect of RNA methylation on transcriptional and post-transcriptional dynamics (Furlan et al. 2020).


Selected Publications

First authorship

  1. Furlan, M., de Pretis S., Pelizzola M. (2020). Dynamics of transcriptional and post-transcriptional regulation. Brief. Bioinformatics
  2. Furlan, M., Galeota E., Del Gaudio N., Dassi E., Caselle M., de Pretis S., Pelizzola M. (2020). Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling. Genome Res. doi:10.1101/gr.260984.120.
  3. Furlan, M., Tanaka, I., Leonardi, T., de Pretis, S., and Pelizzola, M. (2020). Direct RNA Sequencing for the Study of Synthesis, Processing, and Degradation of Modified Transcripts. Front. Genet. doi:10.3389/fgene.2020.00394.
  4. Furlan, M., Galeota, E., de Pretis, S., Caselle, M., and Pelizzola, M. (2019). m6A Dependent RNA Dynamics in T Cell Differentiation. Genes doi:10.3390/ genes10010028.


  1. de Pretis, S., Furlan, M., and Pelizzola, M. (2020). INSPEcT-GUI reveals the impact of the kinetic rates of RNA synthesis, processing, and degradation, on premature and mature RNA species. Front. Genet. doi:10.3389/fgene.2020.00759.
  2. Tesi, A., de Pretis, S., Furlan, M., Filipuzzi, M., Morelli, M. J., Andronache, A., Doni, M., Verrecchia, A., Pelizzola, M., Amati, B., and Sabò, A. (2019). An early Myc-dependent transcriptional program orchestrates cell growth during B-cell activation. EMBO Rep. doi:10.15252/embr.201947987.


  1. Mastrantonio, G., Bibbona, E., and Furlan, M. (2020). Multiple latent clusterisation model for the inference of RNA life-cycle kinetic rates from sequencing data. biorXiv. doi:


I received my Ph.D. Cum Laude
I received my Master's degree Cum Laude and Honourable Mention