Insegnamenti

• Applied bioinformatics (NEU0293)
  Biotechnology for Neuroscience
• Bioinformatica introduttiva (SVB0029B)
  Corso di laurea in Scienze Biologiche (L-13)
• Bioinformatics (NEU0264B)
  Biotechnology for Neuroscience
• DataScience (NEU0264)
  Biotechnology for Neuroscience
• Informatica per le scienze naturali (II SEM) (INT 1415)
  Scuola di Studi Superiori "Ferdinando Rossi" dell'Università degli Studi di Torino - SSST
• Introduzione alla ricerca bibliografica e alla stesura dell'elaborato finale. (SVB0217)
  Corso di laurea in Scienze Biologiche (L-13)
• LABORATORIO BIOMOLECOLARE (SVB0029)
  Corso di laurea in Scienze Biologiche (L-13)
• LABORATORIO DI METODI IN BIOLOGIA MOLECOLARE E GENETICA (SVB0202)
  Corso di laurea in Scienze Biologiche (L-13)
• LABORATORIO DI METODI IN BIOLOGIA MOLECOLARE E GENETICA mod. 1 (SVB0202A)
  Corso di laurea in Scienze Biologiche (L-13)
• Next generation sequencing data analysis using linux power tools (NEU0293A)
  Biotechnology for Neuroscience

Temi di ricerca

Internship proposals

Each of the following proposed internships requires mastering some informatic concepts, both in theory and in practice. Previous experience in informatics is advisable but not mandatory, however students who want to undertake these internships should follow an initial training in informatics, the effort required in this step could be more or less intense according to previous knowledge and competencies.

Similarly students that do not have molecular biology or bioinformatic background will do a zeroing course on these issues.

Even when not formally required, each student who wants to embark on these projects will have to produce a recorded video presentation at the end of the internship, describing its work.

Moreover, I strongly encourage students to publish their results in open repositories like github or biorxiv and I will support this effort.

Not Just Another RNAseq Pipeline (nJARP)

nJARP is an RNA-seq analysis pipeline that focuses on the integration of automatic state-of-the-art procedures and allows customizations in a reproducible and quick way, adhering to UNIX programming principles.

We would like to migrate the pipeline manager from GNU-make to Snakemake. 

The results of the project, when mature enough, will be published on github and biorxiv, giving visibility to the work of involved students.

Further reading:

• https://snakemake.readthedocs.io/en/stable/
• https://en.wikipedia.org/wiki/Unix_philosophy
• https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html 

Artificial Intelligence for the study of Transcription Factor Binding Sites (AI4TFBS)

Despite great hype on AI and the ChIP-seq big-data available, the most used algorithms devoted to Transcription Factor Binding Sites (TFBS) discovery and modelling are still stuck to Position Weight Matrices processed without Machine Learning (ML) or neural network approaches. The students involved in this project will be introduced to ML computational approaches and will experiment with some of the published AI algorithms applied to the study of TFBS.

The goal of the project is to find and master the most promising AI alternatives to the common MEME and HOMER tools.

Further reading

• TFimpute https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345877/

The role of 3D DNA structure in RNA-DNA triple helices formation.

In some genomic regions the DNA shows peculiar deviations from the standard double-helix structure. The relevance of these deviations to the regulation of gene expression is poorly understood.

RNA:DNA:DNA triple helices are an emerging phenomenon in gene regulation mediated by lncRNA.

The haim of the project is to use computational prediction of 3D DNA structure and to investigate its relevance in the formation of lncRNA-DNA triple helices.

Further reading:

• Detection of RNA–DNA binding sites in long noncoding RNAs https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6451187/
• Chiu, T. P., Xin, B., Markarian, N., Wang, Y., & Rohs, R. (2020). TFBSshape: An expanded motif database for DNA shape features of transcription factor binding sites. Nucleic Acids Research, 48(D1), D246–D255. https://doi.org/10.1093/nar/gkz970

Organi

• Consiglio di Dipartimento

• 11. Commissione per l'Innovazione della Didattica

Ricevimento studenti

Da concordare previo appuntamento via mail.

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