Foto del docente

Daniele Mercatelli

Junior assistant professor (fixed-term)

Department of Pharmacy and Biotechnology

Academic discipline: BIO/18 Genetics

Teaching

Dissertation topics suggested by the teacher.

Background: biological mechanisms can be represented as complex networks between molecules of increasing complexity, from metabolites to protein complextes, whose interaction carries out every cellular mechanism. One of the recent challenges of Bioinformatics has been to realistically underestand and reconstruct the design of these cellular networks, by developing in silico inference techniques. One of these techniques is based on the principle of coexpression, which states that multiple transcripts whose abundances are correlated across different cellular states should have a functional relationship. Hundreds of biological network “reverse-engineering” have been developed in the last decade base on the coexpression principle and the vast amount of transcriptome-wide data obtained from microarray platforms and RNA Next Generation Sequencing. This provided both general tools for network reconstruction but also a great understanding on the behavior of transcriptional regulation. Recently, networks centered around individual Transcription Factors have been used to infer their transcriptional activity in specific histological contexts (ref. Alvarez et al., Nature Genetics 2016, Pubmed id 27322546; Mercatelli et al., Bioinformatics 2020, Pubmed id 32232425).


Project: during this project, the prospective student will learn how to build network representations of transcriptional activity and how to apply them to understand the transcriptional variations occurring during the human tumorigenesis process. The student will infer and observe the transcriptional activity of 1800 Transcription Factors and assess differences between tissues and between tumor and adjacent normal cells. The student will investigate the potential of such networks in increasing the signal/noise ratio in intrinsically noisy datasets, such as low coverage multiplexed RNA-Seq or single-cell sequencing.


Required skills: the prospective student should possess a good knowledge of molecular mechanisms at work in RNA transcription and signal transduction cascades, especially those related to human proliferation and tumorigenesis (MAPK, STAT3, E2F, etc.). A requirement in this project is the knowledge of R, a bioinformatics and biostatics development environment and programming language, which should be known at least at a basic level (with the willingness to learn more during the project and a general “nerd” learning attitude). Bonus skills are basic statistics knowledge (correlation, gaussian models, etc.) and Next-Generation Sequencing (RNA-Seq, DNA-Seq, ChIP-Seq). The project will benefit from short optional visits to the Next Generation Sequencing center IGA of Udine.


Suggested for: whoever would like to deepen their knowledge of cutting-edge bioinformatics tools, develop innovative strategies and algorithms, and be challenged in a truly pioneering branch of Transcriptomics.