Methods for Biodata Analysis in different Omic Contexts

Baldazzi, Davide (2022) Methods for Biodata Analysis in different Omic Contexts, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Data science and computation, 33 Ciclo.
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Abstract

The world of Computational Biology and Bioinformatics presently integrates many different expertise, including computer science and electronic engineering. A major aim in Data Science is the development and tuning of specific computational approaches to interpret the complexity of Biology. Molecular biologists and medical doctors heavily rely on an interdisciplinary expert capable of understanding the biological background to apply algorithms for finding optimal solutions to their problems. With this problem-solving orientation, I was involved in two basic research fields: Cancer Genomics and Enzyme Proteomics. For this reason, what I developed and implemented can be considered a general effort to help data analysis both in Cancer Genomics and in Enzyme Proteomics, focusing on enzymes which catalyse all the biochemical reactions in cells. Specifically, as to Cancer Genomics I contributed to the characterization of intratumoral immune microenvironment in gastrointestinal stromal tumours (GISTs) correlating immune cell population levels with tumour subtypes. I was involved in the setup of strategies for the evaluation and standardization of different approaches for fusion transcript detection in sarcomas that can be applied in routine diagnostic. This was part of a coordinated effort of the Sarcoma working group of "Alleanza Contro il Cancro". As to Enzyme Proteomics, I generated a derived database collecting all the human proteins and enzymes which are known to be associated to genetic disease. I curated the data search in freely available databases such as PDB, UniProt, Humsavar, Clinvar and I was responsible of searching, updating, and handling the information content, and computing statistics. I also developed a web server, BENZ, which allows researchers to annotate an enzyme sequence with the corresponding Enzyme Commission number, the important feature fully describing the catalysed reaction. More to this, I greatly contributed to the characterization of the enzyme-genetic disease association, for a better classification of the metabolic genetic diseases.

Abstract
Tipologia del documento
Tesi di dottorato
Autore
Baldazzi, Davide
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
33
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Data Science; Bioinformatics; Cancer Genomics; Enzyme Proteomics; Diseases; Functional Oncogenetics and Oncogenomics; Tumor Microenvironment; Immune Infiltrate; Transcriptomics; Next Generation Sequencing; Machine Learning; Web Server; Functional Prediction; Gene Fusions; Mutation Effects.
URN:NBN
Data di discussione
21 Marzo 2022
URI

Altri metadati

Gestione del documento: Visualizza la tesi

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