Programma

The course consists of online and laboratory lessons, focusing primarily on gene sequence alignment techniques. We will also cover biological networks and agent-based models in the biological domain.
Regarding alignment techniques, we will explore DNA/RNA sequencing and alignment algorithms, as well as phylogenetic tree reconstruction methods. Specifically, we will study techniques for whole genome reconstruction (Whole Genome Sequencing) using de Bruijn graphs and the alignment of pairwise and multiple subsequences, which are employed to identify the biological functions associated with different genes.
We will examine some of the main genetic databases and the online tools available for performing alignments (NCBI, BLAST, Clustal)..
Next, we will study some characteristics and methodologies used in the analysis of complex networks, such as centrality measures and clustering, applied to protein networks and gene regulatory networks.
Finally, we will introduce some agent-based modeling techniques used in clinical and immunological applications.
- Topics:
- Introduction to Biology
- Sequencing techniques, genome sequencing, de Bruijn graphs
- Sequence alignment: Knuth-Morris-Pratt algorithm, online databases, pairwise alignment, scoring matrices, Needleman-Wunsch algorithm, BLAST tool
- Multiple sequence alignment: Markov chains, phylogenetic trees, Clustal, UPGMA, Neighbor-Joining algorithm
- Biological networks: protein networks and gene regulatory networks
- Cellular automata and agent-based models

Testi Adottati

Python:https://github.com/steguar/DAIL/blob/main/Lecture_1/Lecture_1_Python_crash_ course.ipynb
• Understanding Bioinformatics, Marketa Zvelebil & Jeremy O. Baum
• Biological sequence analysis, R. Durbin et al. (CAP 1,2,6,7)
• Bioinformatics Algorithms: an Active Learning Approach, Pavel A. Pevzner and Phillip Compeau
• Bioinformatics - an Introduction, Jeremy Ramsden
• Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
• Statistical Methods in Bioinformatics, An Introduction, Warren J. Ewens , Gregory Grant
• Networks, M. Newman (ER and CM random graphs, Epidemics on Networks)

Modalità Frequenza

In-person attendance, with the possibility of occasionally following the course remotely.

Modalità Valutazione

Drafting a project on a topic agreed upon with the instructor, questions about the project and the course content covered.

Programma

The course consists of online and laboratory lessons, focusing primarily on gene sequence alignment techniques. We will also cover biological networks and agent-based models in the biological domain.
Regarding alignment techniques, we will explore DNA/RNA sequencing and alignment algorithms, as well as phylogenetic tree reconstruction methods. Specifically, we will study techniques for whole genome reconstruction (Whole Genome Sequencing) using de Bruijn graphs and the alignment of pairwise and multiple subsequences, which are employed to identify the biological functions associated with different genes.
We will examine some of the main genetic databases and the online tools available for performing alignments (NCBI, BLAST, Clustal)..
Next, we will study some characteristics and methodologies used in the analysis of complex networks, such as centrality measures and clustering, applied to protein networks and gene regulatory networks.
Finally, we will introduce some agent-based modeling techniques used in clinical and immunological applications.
- Topics:
- Introduction to Biology
- Sequencing techniques, genome sequencing, de Bruijn graphs
- Sequence alignment: Knuth-Morris-Pratt algorithm, online databases, pairwise alignment, scoring matrices, Needleman-Wunsch algorithm, BLAST tool
- Multiple sequence alignment: Markov chains, phylogenetic trees, Clustal, UPGMA, Neighbor-Joining algorithm
- Biological networks: protein networks and gene regulatory networks
- Cellular automata and agent-based models

Testi Adottati

Python:https://github.com/steguar/DAIL/blob/main/Lecture_1/Lecture_1_Python_crash_ course.ipynb
• Understanding Bioinformatics, Marketa Zvelebil & Jeremy O. Baum
• Biological sequence analysis, R. Durbin et al. (CAP 1,2,6,7)
• Bioinformatics Algorithms: an Active Learning Approach, Pavel A. Pevzner and Phillip Compeau
• Bioinformatics - an Introduction, Jeremy Ramsden
• Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
• Statistical Methods in Bioinformatics, An Introduction, Warren J. Ewens , Gregory Grant
• Networks, M. Newman (ER and CM random graphs, Epidemics on Networks)

Modalità Frequenza

In-person attendance, with the possibility of occasionally following the course remotely.

Modalità Valutazione

Drafting a project on a topic agreed upon with the instructor, questions about the project and the course content covered.