First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICAL SCIENCES
Course unit
COMPUTATIONAL BIOSTATISTICS AND BIOINFORMATICS
SCP4063318, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
STATISTICAL SCIENCES
SS1736, Degree course structure A.Y. 2014/15, A.Y. 2019/20
N0
bring this page
with you
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination COMPUTATIONAL BIOSTATISTICS AND BIOINFORMATICS
Website of the academic structure http://www.stat.unipd.it/studiare/ammissione-laurea-magistrale
Department of reference Department of Statistical Sciences
E-Learning website https://elearning.unipd.it/stat/course/view.php?idnumber=2019-SS1736-000ZZ-2018-SCP4063318-N0
Mandatory attendance No
Language of instruction Italian
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 DAVIDE RISSO SECS-S/01

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses SECS-S/02 Statistics for Experimental and Technological Research 9.0

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

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 3.0 24 51.0 No turn
Lecture 6.0 40 110.0 No turn

Calendar
Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2014 course timetable

Examination board
Board From To Members of the board
5 Commissione a.a.2019/20 01/10/2019 30/09/2020 RISSO DAVIDE (Presidente)
BRAZZALE ALESSANDRA ROSALBA (Membro Effettivo)
CORTESE GIULIANA (Membro Effettivo)

Syllabus
Prerequisites: Statistics (advanced), Probability Theory, Statistical Models 2
Target skills and knowledge: Introduction to biological problems that can be explored with data from new generation sequencing technologies.
Introduction to statistical models for transcriptomic and genomic data.
Ability to perform a complete data analysis: from raw data to the interpretation of the results.
Ability to write a short report on the analysis of a dataset assigned by the instructor.
Examination methods: Written exam and short report on the analysis of a dataset assigned by the instructor.
Assessment criteria: The evaluation criteria include: the clarity of the explanations in the written report, the correct choice of the statistical methods for the data analysis, the correctness and completeness of the answers in the written exam.
Additional criteria: the critical analysis of the results, the independence in the data analysis project.
Course unit contents: With the completion of the human genome project, and with the systematic genome sequencing of several complex organisms, a massive amount of genomic, proteomic, and transcriptomic data is now publicly accessible. The wide availability of biological data is revolutionizing genetic research and our comprehension of several biological mechanisms, such as gene regulation, protein interactions, and the activation and suppression of metabolic pathways. In this context, the amount and complexity of the data make the statistical analysis challenging.
The course will cover the following topics:
- Introduction to genomics, transcriptomics, and epigenomics.
- Sequence alignment. Alignment algorithms, global and local alignments, application to the quantification of RNA expression.
- Analyisis of gene expression data from RNA-seq experiments. Data normalization, global and local methods (lowess), variance-stabilizing transformations, discriminant and cluster analysis. Hypothesis testing for the identification of differentially expressed genes, moderated tests, permutational approaches. Multiple testing problems, control of the False Discovery Rate (FDR), classification methods, gene set analysis.
- Introduction to the analysis of other types of genomic data, such as DNA sequencing, chromatin accessibility, protein-RNA interactions (immunoprecipation).
Planned learning activities and teaching methods: Lectures and computer labs
Additional notes about suggested reading: Teaching material provided by the instructor
Textbooks (and optional supplementary readings)
  • Irizarry, Rafael A.; Love, Michael I., Data Analysis for the Life Sciences with R. Boca Raton: Chapmann and Hall CRC, 2017. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Case study
  • Working in group
  • Peer feedback
  • Loading of files and pages (web pages, Moodle, ...)

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

Sustainable Development Goals (SDGs)
Good Health and Well-Being