66563 - LABORATORY OF BIOINFORMATICS 1

Scheda insegnamento

Anno Accademico 2017/2018

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.

Programma/Contenuti

Laboratory of Bioinformatics I (first semester, 5CFU)

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, 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 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 cloud sharing

Orario di ricevimento

Consulta il sito web di Rita Casadio

Consulta il sito web di Emidio Capriotti

Consulta il sito web di Allegra Via