Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Molecular and Medical Biotechnology - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
|---|
2 courses to be chosen among the followingOne course to be chosen among the following:3 courses to be chosen among the followingOne course to be chosen among the following2° Year activated in the A.Y. 2016/2017
| Modules | Credits | TAF | SSD |
|---|
2 courses to be chosen among the following:| Modules | Credits | TAF | SSD |
|---|
2 courses to be chosen among the followingOne course to be chosen among the following:3 courses to be chosen among the followingOne course to be chosen among the following| Modules | Credits | TAF | SSD |
|---|
2 courses to be chosen among the following:Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Algorithms for computational biology (2015/2016)
Teaching code
4S003660
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
I semestre dal Oct 1, 2015 al Jan 29, 2016.
Learning outcomes
1. to learn about some basic problems and algorithms behind common bioinformatics applications (sequence alignment, sequence similarity, phylogenetics), and 2. to get an idea of some basic computational issues (problem specification, efficiency of algorithms, limitations).
Program
ALGORITHM ANALYSIS
Introduction to algorithm analysis: running time and storage space analysis; notation for complexity analysis: Big-Oh-notation, growth of functions; formalism on strings/sequences; basic combinatorics on strings;
SEQUENCE ALIGNMENT
Applications; Pairwise sequence alignment: Exhaustive search, Dynamic programming (DP) algorithm of Needleman-Wunsch (global alignment), DP algorithm of Smith-Waterman (local alignment), other variants; multiple sequence alignment: DP algorithm and heuristics; Scoring matrices: PAM (computation, application); Heuristics for sequence alignment and database search: BLAST; string distance and similarity, edit distance;
PHYLOGENETICS
Introduction to graphs and trees; number of phylogenetic trees; distance-based data: algorithm UPGMA; character-based data: Perfect Phylogeny (PP), Small parsimony: Fitch' algorithm; Large parsimony: heuristics.
Examination Methods
Written exam, followed by oral exam.
