ID:
510324
Duration (hours):
72
CFU:
9
SSD:
BIOLOGIA MOLECOLARE
Year:
2025
Overview
Date/time interval
Secondo Semestre (02/03/2026 - 12/06/2026)
Syllabus
Course Objectives
At the end of the course, the student will be able to:
- understand and apply key statistical concepts, such as hypothesis testing, null hypothesis p-values and statistical modeling;
- identify the correct statistical tests to apply in different scenarios and appropriate to different data types;
- use the most appropriate computational tools (R, Nextflow) and the appropriate infrastructure (local, HPC, Cloud) to address questions on biological data;
- evaluate and compare the results of the analysis, in order to answer the initial question or take further experimental decisions;
- solve a biological question and communicate bioinformatics results in an integrated and coherent way, using reproducible research methods.
- understand and apply key statistical concepts, such as hypothesis testing, null hypothesis p-values and statistical modeling;
- identify the correct statistical tests to apply in different scenarios and appropriate to different data types;
- use the most appropriate computational tools (R, Nextflow) and the appropriate infrastructure (local, HPC, Cloud) to address questions on biological data;
- evaluate and compare the results of the analysis, in order to answer the initial question or take further experimental decisions;
- solve a biological question and communicate bioinformatics results in an integrated and coherent way, using reproducible research methods.
Course Prerequisites
The student will need basic-level knowledge of computers: how to copy files, install basic software, how to use a browser.
The student will be assumed to have basic knowledge of:
- molecular biology (structure and function of a gene, transcription and translation processes, splicing, sequencing)
- genetics (variants, allele fraction, population frequency, haplotype).
A positive outcome in the self-evaluation test of the online pre-courses (https://elearning.unipv.it/course/view.php?id=20) is required.
If not possessed already, principles of molecular biology can be acquired in the course “Basic Molecular Biology” (1st semester 1st year) or strengthened in the course “Advanced Molecular Biology (2nd semester 1st year); principles of genetics can be acquired in the course “Basics Genetics and Cell Biology” (2nd semester 1st year) or strengthened in the course “Human Molecular Genetics” (2nd semester 1st year).
Knowledge of biochemistry and cellular biology are not essential but recommended.
Basic knowledge of biostatistics will be useful.
The student will be assumed to have basic knowledge of:
- molecular biology (structure and function of a gene, transcription and translation processes, splicing, sequencing)
- genetics (variants, allele fraction, population frequency, haplotype).
A positive outcome in the self-evaluation test of the online pre-courses (https://elearning.unipv.it/course/view.php?id=20) is required.
If not possessed already, principles of molecular biology can be acquired in the course “Basic Molecular Biology” (1st semester 1st year) or strengthened in the course “Advanced Molecular Biology (2nd semester 1st year); principles of genetics can be acquired in the course “Basics Genetics and Cell Biology” (2nd semester 1st year) or strengthened in the course “Human Molecular Genetics” (2nd semester 1st year).
Knowledge of biochemistry and cellular biology are not essential but recommended.
Basic knowledge of biostatistics will be useful.
Teaching Methods
The course will significantly use “blended learning” tools, which assume that one-way information transfer is limited during classes activity. Students will be instead expected to use the Kiro platform for readings and self-evaluation activities.
Class activity will be focused to demonstrations, discussions and problem solving through interaction: demo, group work, quiz and real-time feedback.
Containers (docker), virtual machines and code editors will be used in classes, to improve learning python, R and the other command-line tools used in the course.
The most appropriate inclusive educational methods will be implemented, to meet the needs of specific categories of students defined by the University.
Class activity will be focused to demonstrations, discussions and problem solving through interaction: demo, group work, quiz and real-time feedback.
Containers (docker), virtual machines and code editors will be used in classes, to improve learning python, R and the other command-line tools used in the course.
The most appropriate inclusive educational methods will be implemented, to meet the needs of specific categories of students defined by the University.
Assessment Methods
The student will receive a simplified dataset, to be analised using one of the tools or methods learnt during the course.
The student will then be asked to explain the results of the analysis, and demonstrate a critical approach to answering the biological question proposed; the knowledge of tools and methods will be verified at this stage as well.
The student will then be asked to explain the results of the analysis, and demonstrate a critical approach to answering the biological question proposed; the knowledge of tools and methods will be verified at this stage as well.
Texts
The course will mostly use freely available material, video and tutorials.
The use of a textbook will be entirely optional, and we suggest the following:
- R Bioinformatics Cookbook
Dan MacLean
Packt Publishing, 2019
- Modern Statistics for Modern Biology
Holmes, Susan; Huber, Wolfgang
Cambridge University Press, 2019
- Tidy Modeling with R
Max Kuhn and Julia Silge
O’Reilly
Some of these textbooks will be made available by the Sciences Library in an e-book version, or are already freely available as a website (https://web.stanford.edu/class/bios221/book/, https://www.tmwr.org).
The teacher will provide supporting materials and tutorials throughout the classes.
The use of a textbook will be entirely optional, and we suggest the following:
- R Bioinformatics Cookbook
Dan MacLean
Packt Publishing, 2019
- Modern Statistics for Modern Biology
Holmes, Susan; Huber, Wolfgang
Cambridge University Press, 2019
- Tidy Modeling with R
Max Kuhn and Julia Silge
O’Reilly
Some of these textbooks will be made available by the Sciences Library in an e-book version, or are already freely available as a website (https://web.stanford.edu/class/bios221/book/, https://www.tmwr.org).
The teacher will provide supporting materials and tutorials throughout the classes.
Contents
In the first part, the course will cover the fundamentals of statistics applied to biology, with an additional focus on those methods deemed essential in big data analysis.
In the second part, the course will focus on computational tools, methods and environments used in big data analysis.
In particular the course will cover:
- statistical concepts, such as hypothesis testing, p-values, and the choice of the most appropriate statistical tests;
- the R environment, for statistics and modelling;
- statistical modelling: clustering, dimensionality reduction, supervised learning;
- unix environment and bash;
- high performance computing: environment, scheduler, and principles of distributed computation;
- workflows and workflows engine, with a focus on Nextflow;
- cloud computing.
In the second part, the course will focus on computational tools, methods and environments used in big data analysis.
In particular the course will cover:
- statistical concepts, such as hypothesis testing, p-values, and the choice of the most appropriate statistical tests;
- the R environment, for statistics and modelling;
- statistical modelling: clustering, dimensionality reduction, supervised learning;
- unix environment and bash;
- high performance computing: environment, scheduler, and principles of distributed computation;
- workflows and workflows engine, with a focus on Nextflow;
- cloud computing.
Course Language
English
More information
The teacher will be available via email and for meetings to be agreed on, as well as through collaborative tools: a dedicated channel will be setup on Slack, for interacting with students and discussing different topics.
Degrees
Degrees (2)
MOLECULAR BIOLOGY AND GENETICS
Master’s Degree
2 years
NEUROBIOLOGY
Master’s Degree
2 years
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