First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
BIOENGINEERING
Course unit
COMPUTATIONAL GENOMICS
INP7080567, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
BIOENGINEERING
IN0532, Degree course structure A.Y. 2011/12, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination COMPUTATIONAL GENOMICS
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN0532-000ZZ-2017-INP7080567-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge BARBARA DI CAMILLO INF/01

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP7080567 COMPUTATIONAL GENOMICS BARBARA DI CAMILLO IN2371
INP7080567 COMPUTATIONAL GENOMICS BARBARA DI CAMILLO IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/06 Electronic and Information Bioengineering 6.0

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

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Board From To Members of the board
1 A.A. 2018/2019 01/10/2018 15/03/2020 DI CAMILLO BARBARA (Presidente)
TOFFOLO GIANNA MARIA (Membro Effettivo)
DALLA MAN CHIARA (Supplente)
FACCHINETTI ANDREA (Supplente)
PEDERSEN MORTEN GRAM (Supplente)
SACCOMANI MARIAPIA (Supplente)
SAWACHA ZIMI (Supplente)
SPARACINO GIOVANNI (Supplente)

Syllabus
Prerequisites: Basic knowledge of statistics and computer science
Target skills and knowledge: Learning Objectives:
In this course you will learn
1. Methodologies for statistical analysis and data mining of high-throughput genomic data
2. How to apply these methodologies to real data using algorithms and software tools

Knowledge to be acquired:
1. Basic notions of molecular biology and sequencing technologies
2. Data preprocessing and normalization
3. Basic notions of statistics and inferential tests
4. Clustering and supervised classification methods
5. R scripting language

Specific skills to be acquired:
1. Ability to apply the different methodologies to the genomics field, in particular to genetic variants and expression RNAseq data
2. Ability to solve a complex problem being able to decompose it in smaller problems, implementing the solution in R language

General skills to be acquired:
ability to decide autonomously about the analysis pipeline; to critically evaluate the obtained results, to communicate the reasons of your choice; to organize the R scripts so that other users/colleagues can easily interact
Examination methods: Written exam
Assessment criteria: The acquired knowledge will be assessed through the exam. In particular, the ability to applied the different methodologies to real data will be assessed to the ability to solve pratical problems implementing and using R software programs.
Course unit contents: Mathematical and computational solutions to biological problems, using statistics, machine learning and data mining

Specific Content:

1. High-throughput technologies for genome and transcriptome analysis (DNA and RNA-sequencing)

2. Data preprocessing. Quantifying data reproducibility and experimental noise. Normalization and scaling methods.

3. Methods for selection of differentially expressed genes and SNP associated to different phenotypes. test correction to deal with high-throughput data and multiple testing

4. Functional interpretation of the results. Annotation and functional enrichment tests

5. Clustering: distance based methods (Hierarchical Clustering, K-means, Self-Organizing Maps) and model based (Maximum Likelihood and Bayesian Clustering).

6. Supervised Classification: Support Vector Machine (and/or Neural Networks), Feature Selection. Stable biomarker selection.

7. Lab lectures: Using Bioconductor function in R environment and implementing your own software to analyze genomic data.
Planned learning activities and teaching methods: - Frontal lessons
- Task based learning
- Lab implementing software solutions
- Collaborative learning opportunities where Ss can participate by exploring, reflecting and thinking critically together.
Additional notes about suggested reading: Slide and class notes and papers, books made available through elearning
Textbooks (and optional supplementary readings)
  • Bishop, Pattern recognition and machine learning. --: --, 2006. Testo utile per approfondire gli argomenti relativi alla classificazione Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Working in group
  • Questioning
  • Problem solving
  • Peer assessment
  • Active quizzes for Concept Verification Tests and class discussions
  • Use of online videos
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • R language

Sustainable Development Goals (SDGs)
Quality Education