- Docente: Rita Casadio
- Crediti formativi: 10
- SSD: BIO/10
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
- Corso: Laurea Magistrale in Bioinformatics (cod. 8020)
Conoscenze e abilità da conseguire
At the end of the course, the student has the basic knowledge for developing and using tools for sequence and structure analysis of biomolecules and more generally for annotation problems in the genomic era. In particular, the student will be able to: discuss the theoretical basics of some machine learning tools (Neural Networks, Hidden Markov Models); selecting programs for problem solving; writing programs.
Contenuti
Laboratory of Bioinformatics I (first semester, 5CFU)
Theory and application on :
1) The role of Bioinformatics2) Archives and Next Generation Sequencing experiments
3) The problem of sequence annotation
4) Protein sequence, structure and function
5) Protein structure comparison: generating rules for sequence comparison
6) Local and global alignment methods; data base search with BLAST
7) Extreme value statistics
8) The protein universe and UniProtKB
9) Evolution did it: what can we learn from a pairwise structure comparison over the entire PDB
10) Theoretical foundation of building by homology
11) From sequence to structure and function
12) When a protein is a protein
Best practice on:
1) Handling of the different alignment methods
2) Modeller and statistical validation of computed 3D models
3) Comparison with SwissModeller
Laboratory of Bioinformatics I (second semester, 5CFU)
Theory and application on:
1) Protein geometrical features
2) Protein 3D, secondary and covalent structure
3) Protein Domains: SCOP and CATH
4) The notion of functional domains/Go terms
5) Functional domains and evolution
6) Protein families
7) Biosequence analysis: a historical perspective
8) Mapping structures into sequences and back
9) Propensity scales and propensity plots
10) The concept of averaging over a sliding window
11) Conditional probability and secondary structure prediction
12) Basics of feed-forward neural networks.
13) Training, testing and applications of NN
14) Critical evaluation of machine learning methods: HMM vs NN
15) Protein prediction under 30% sequence identity
Best practice on:
1) How to model a protein domain with a HMM
2) Best practice of hmmr and statistical validation of a computed protein domain
3) Comparison with PFAM
Testi/Bibliografia
Online, articoli e riviste selezionate in cloud sharing
Metodi didattici
Lezioni, esercitazioni in classe, sviluppo di tools
Modalità di verifica e valutazione dell'apprendimento
La prova finale è volta alla verifica del raggiungimento degli
obiettivi didattici. Include la verifica degli insegnamenti teorici
e pratici con test scritti (in itinere o alla fine del corso) e una
prova orale finale che verifichi la preparazione negli
argomenti come svolti nei due semestri di attività didattica
(si rammenta che il corso è tenuto in lingua Inglese).
Dato l'articolarsi del corso su due semestri, alla fine di ogni
semestre si prevede un test scritto atto a valutare l'apprendimento
dello studente durante il corso. I due test valutano
l'idoneità alla prova orale finale. Se lo studente non ha
partecipato ai test, prima della prova orale è tenuto a svolgere un
test scritto su tutti gli argomenti del corso. Per accedere
alla prova orale finale lo studente deve inoltre presentare due
relazioni scritte sulle esercitazioni fatte in classe con
tutoraggio. Nella prova orale finale lo studente deve dare prova
della sua indubbia capacità a sviluppare i seguenti
argomenti:
Laboratory of Bioinformatics I (first semester)
Theory and application on:
1) The role of Bioinformatics
2) Archives and Next Generation Sequencing
experiments
3) The problem of sequence annotation
4) Protein sequence, structure and function
5) Protein structure comparison: generating rules
for sequence comparison
6) Local and global alignment methods; data base
search with BLAST
7) Extreme value statistics
8) The protein universe and UniProtKB
9) Evolution did it: what can we learn from a
pairwise structure comparison over the entire PDB
10) Theoretical foundation of building by homology
11) From sequence to structure and function
12) When a protein is a protein
Best practice on:
1) Handling of the different alignment
methods
2) Modeller and statistical validation of
computed 3D models
3) Comparison with SwissModeller
Laboratory of Bioinformatics I (second semester)
Theory and application on:
1) Protein geometrical features
2) Protein 3D, secondary and covalent
structure
3) Protein Domains: SCOP and CATH
4) The notion of functional domains/Go
terms
5) Functional domains and evolution
6) Protein families
7) Biosequence analysis: a historical
perspective
8) Mapping structures into sequences and
back
9) Propensity scales and propensity plots
10) The concept of averaging over a sliding
window
11) Conditional probability and secondary
structure prediction
12) Basics of feed-forward neural networks.
13) Training, testing and applications of
NN
14) Critical evaluation of machine learning
methods: HMM vs NN
15) Protein prediction under 30% sequence
identity
Best practice on:
1) How to model a protein domain with a HMM
2) Best practice of hmmr and statistical
validation of a computed protein domain
3) Comparison with PFAM
Strumenti a supporto della didattica
Online, Data Base Pubblici, PubMed, e materiale (pdf delle lezioni e articoli selezionati) in cluod sharing
Orario di ricevimento
Consulta il sito web di Rita Casadio