First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
Faculty of Medicine and Surgery
Course unit
MEM0016063, A.A. 2013/14

Information concerning the students who enrolled in A.Y. 2012/13

Information on the course unit
Degree course 6 years single cycle degree in
ME1728, Degree course structure A.Y. 2009/10, A.Y. 2013/14
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Degree course track Common track
Number of ECTS credits allocated 4.0
Type of assessment Mark
Course unit English denomination BIOINFORMATICS
Department of reference Department of Medicine
Mandatory attendance
Language of instruction Italian
Single Course unit The Course unit CANNOT be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge STEFANO TOPPO BIO/10

Course unit code Course unit name Teacher in charge Degree course code

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines BIO/10 Biochemistry 4.0

Course unit organization
Period Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 4.0 40 60.0 No turn

Start of activities 03/03/2014
End of activities 14/06/2014
Show course schedule 2019/20 Reg.2015 course timetable

Examination board
Board From To Members of the board
1 BIOINFORMATICA - COMMISSIONE D'ESAME A.A. 2013/2014 01/10/2013 31/12/2018 TOPPO STEFANO (Presidente)
FALDA MARCO (Membro Effettivo)

Prerequisites: Good knowledge of spoken and written English is required
Target skills and knowledge: The course aims to provide students with basic concepts of bioinformatics in the field of alignments, the study of proteins and analysis of DNA sequences and genome projects
Examination methods: The examination is typically 1 hour and a half in length and closed book in format. The test is with open-ended questions on the entire contents of the course. Also planned seminar activity and evaluation
Assessment criteria: The objective is to assess students’ ability to engage critically with the material covered on the course and provide personal interpretation
Course unit contents: BIOINFORMATICS
1) Sequence alignments algorithms: motivation
2) Brief introductions to issues and unsolved questions from phylogenetic analysis to protein structure and function. Structural evolutionary models, folds and protein disorders
3) Dot Plot analysis, repeat and inverse repeats
4) Sequence alignment scores: random vs. match model to calculate the alignment score
5) Scoring matrices: PAM and BLOSUM
6) Local (Smith and Waterman), global (Needleman-Wunsch), freeshift algorithms
7) K-tuple algorithms: BLAST and FASTA for database searches, introduction to raw score, bitscore, e-value, detailed FASTA, BLAST algorithms steps
8) Confusion matrices, ROC curves
9) Multiple alignments: progressive and iterative strategies. The operation of CLUSTAL , introduction of molecular phylogeny concepts, neighbor joining
10) Patterns: how to build a sequence pattern
11) Frequency matrices and protein profiles
12) PSI-BLAST and the PSSM
13) Markov chains and Hidden Markov Chains (HMM). Training and searching algorithms (Baum-welch, Viterbi). Application to transmembrane protein prediction algorithms
14) Machine learning: Neural Networks an introduction.
15) Protein structure prediction from 3D structures; DSSP and STRIDE
16) Secondary protein structure prediction: from Chou-Fasmann, GOR to second generation techniques based on multiple alignments and third generation strategies based on Neural Networks as PSIPRED and SSPRO. Pros and Cons, Benchmarking evaluation scores. The meta server approach
17) Structural modeling from Comparative modeling to Fold recognition techniques
18) CASP (Critical Assessment of protein Structure Prediction) experience.
19) Comparative modeling: steps to create the final model; template, alignment, raw model, loop modeling, sidechain placement, refinement. Different issues to take care of and strategies to adopt in each of the previous steps. Statistical potential or knowledge-based potential concept (RAPDF, salvation, torsional). Alternative target-template alignments and raw models constructions ranked on the basis of statistical energies.
20) Fold recognition strategies. Threading techniques, back-validation of low scoring hits, secondary structures alignments, meta-servers


21) Whole genome shotgun projects
22) Next Generation Sequencing Technologies (454, Illumina, SOLiD, Ion Proton, Pacific Biosciences)
23) How to assemble a whole genome. Alignment algorithms.
24) Overlap Layout Consensus (OLC) algorithms
25) Graph based and greedy algorithms (Hamilton path)
26) De Brujin graph strategy
27) Assembly of repeats sequences
28) RNA-seq sequencing
29) De Novo assembly vs. Mapping algorithms
30) Gene prediction algorithm (content sensors and signal sensors)
Planned learning activities and teaching methods: lectures and learning through the preparation of journal clubs
Additional notes about suggested reading: material provided in class. Slides of the lectures
Textbooks (and optional supplementary readings)