BIOINFORMATICS II
(objectives)
The course aims to train students in various advanced sectors of Bioinformatics, from the study of 'omics' data, produced by next generation sequencing platforms (Next Generation Sequencing - NGS) in the different areas of: genomics, transcriptomics, epigenomics , metagenomics; computational techniques for virtual screening, docking and molecular simulations of biological macromolecules; introduction to systems biology for gene expression modeling.
In detail, the training objectives concern: 1) Basic knowledge on next generation sequencing platforms; 2) Raw data formats produced by NGS sequencers; 3) Pre-processing programs: quality control and trimming of short reads; 4) Algorithms for mapping reads on the reference genome; 5) Programs for the calling variant; 6) Algorithms for the assembly of genomes or transcriptomes; 7) Statistics and R libraries for the analysis of the differential expression of genes and transcripts; 8) Study of pipelines for epigenomics and metagenomics; 10) Database and algorithms for virtual screening; 11) Stochastic algorithms for molecular docking; 12) Algorithms for energy minimization, thermalization of the system macromolecular and classical molecular dynamics (all atoms); 13) Equations for the description of dynamic models of gene regulation.
EXPECTED LEARNING RESULTS:
KNOWLEDGE AND UNDERSTANDING. Students will have to show that they have learned bioinformatics topics included in the course, namely: acquisition of NGS data analysis methods, ability to design and develop new pipelines for analysis of omics data, ability to model structural data of macromolecules, ability of configuration and molecular dynamics analysis of biological macromolecules, acquisition of introductory concepts of system biology with application to dynamic models of gene regulation.
ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING. Students should have an understanding of the computational approaches discussed in class and be able to apply them to specific biological problems.
AUTONOMY OF JUDGMENT. Students must be able to critically interpret the results obtained through the bioinformatics tools discussed in class, as well as choose the most suitable ones to reach a specific goal.
COMMUNICATION SKILLS. Students must have the ability to transmit the knowledge acquired in a clear and understandable way, even to non-competent people, and must demonstrate the ability to present the information acquired.
LEARNING ABILITY. Students should be able to describe the various topics of Bioinformatics 2, in oral form. This ability will be developed through active involvement through oral discussions in the classroom and exercises carried out in the computer room on specific topics related to the course.
|
Teacher
|
CASTRIGNANO TIZIANA
(syllabus)
Introduction to first, second, and third-generation sequencing platforms (Next Generation Sequencing - NGS). Understanding of the main data formats produced by NGS sequencers. Introduction to NGS preprocessing programs: quality control and trimming of short reads. Heuristic algorithms for aligning short reads to the reference genome or transcriptome. Algorithms for genome and transcriptome assembly. Methods for constructing graphs for assembly. Methods for genome and transcriptome annotation. Study of assembly pipelines for genomes and transcriptomes, genomics (Whole genome sequencing - WGS), transcriptomics (RNA-seq data analysis), epigenomics (Chip-seq analysis), metagenomics, population analysis (Rad-seq analysis). Machine learning methods for predicting the three-dimensional structure of proteins (Alphafold 2). Stochastic algorithms for molecular docking. Introduction to Molecular Dynamics (MD). Algorithms for energy minimization, thermalization of the macromolecular system, and classical molecular dynamics (all-atoms). Insights into dynamic models of gene regulation: Equations for the description of a simple model.
(reference books)
Manuela Helmer Citterich Fabrizio Ferrè Giulio Pavesi Graziano Pesole Chiara Romualdi First principles of bioinformatics 2018
Stefano Pascarella Alessandro Paiardini Bioinformatics From sequence to protein structure 2011
|
Dates of beginning and end of teaching activities
|
From to |
Delivery mode
|
Traditional
|
Attendance
|
not mandatory
|
Evaluation methods
|
Oral exam
A project evaluation
|
|