Cellular and Molecular Biology

BIOINFORMATICS

BIOINFORMATICS

Academic year 2024/2025

Sommario del corso

• Course objectives
• Results of learning outcomes
• Program
• Course delivery
• Learning assessment methods
• Suggested readings and bibliography
• Teaching tools
• Course card

Course objectives

This teaching contributes to the learning objectives included into the area of Biomolecular in the Master in Cellular and Molecular Biology - Biologia Cellulare e Molecolare, providing knowledge and applicative abilities to analyze deep sequencing data obatined from high-throughput techniques.

In the last years become mandatory understand and known how design and analyze a biological experiment based on high-throughput techniques. Since the complete sequencing of the human genome is available to the researchers, the diagnosis and treatment of human diseases has evolved on the basis of new insights in genomics and trascriptomic. The emerging field of bioinformatics is crucial to process, to mine the data, and also to generate new testable hypotheses.

The course provides a general overview of the main technologies used to investigate the genomic and trascriptomic layers in the past, microarray, and in the present, next generation sequencing. A complete overview about the major computational pipeline and algorithms necessary to analyze these deep sequencing data will be provided.

Students will acquire an advanced level of knowledge in term of experimental design and they will be able to known which are the main steps to deal with during the analysis and visualization of deep sequencing data.

The course is organized in two modules in strict connection between them. On one side the course provides an introduction to computer science concepts, local and global alignment algorithms, and clustering algorithms.
Those concepts will be integrated in the explanation about how analyze deep sequencing data and interpret the results obtained.

Results of learning outcomes

The student will be able to understand the biological problem and to suggest an optimal experimental design to aswer to the biologcial questions proposed. The students will be also able to understand and discusss scientific papers based on the usage of the technologies discussed during the course.

In details:

Theoretical knowledge

Program

High throughput technologies introduction

History of DNA sequencing

Next Generation Sequencing:
Coverage in massive parallel experiments, multiplexing library, SE versus PE reads
Library Preparation, FastQ file.
Quality control, PCA and HCL
Basic notions about alignment. How mapping the reads from deep sequencing data.
Tophat algorithm for reads alignment, annotation files and SAM/BAM output file format.
Mapping and Transcripts reconstruction
Transcript quantification, differential expression analysis, and results visualization

Interpretation of the results:
Gene Ontology: structure and annotation. Hypergeometric test.
Gene Ontology Applications and Gene Set Enrichment Analysis

Single-cell deep sequencing technology.

Metagenomics.

The dynamic programming algorithm for sequence alignment algorithms: Local, global, and multiple sequence alignment algorithms.
Clustering and Classification Algorithms.

Course delivery

The course is articulated in formal in-class lectures.

The record of the lessons will NOT be available.

Emphasis is given to the scientific topics in the syllabus by surveying the status of art.

All lessons will be delivered in their presence. According to university recommendations, alternative online teaching (by streaming) may be introduced.

Learning assessment methods

Exercises and discussions with the students during the lessons are the main tools for controlling the learning experience.

A final written test checking the acquired knowledge lasts 90 minutes. It evaluates the student's ability to answer theoretical questions and solve problems related to the topics introduced during the course. Generally will be proposed 7/8 open questions and/or exercises. The score associated with each question is indicated on the exam text and is proportional to the complexity of the question. 

The oral interview is NOT mandatory. If the student would like to increase the score up to two points he/she can perform the oral interview. Note that if the student does not answer correctly to the questions the score could be downgraded up to two points.  In any case, the teachers can ask the student for an oral interview in cases of suspicious behaviors.

Exams will take place exclusively in presence.

RNA-seq Data Analysis: A Practical Approach Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong

Bioinfomatics algorithms  - An active learning approach by Pavel Pevzner and Phillip Compeau

• Teaching material
• Exams and finals
• Class schedule
• Go to Moodle
• Register for the course
• Enrolled students
• Invia email agli studenti registrati
• Export table