Bioinformatics and Biostatistics
A.Y. 2019/2020
Learning objectives
Bioinformatics: the course aims at providing students with the basic theoretical and practical concepts of the most widely used bioinformatic analysis tools: sequencing, assembly and annotation of genomes; identification of alternative transcripts and splicing isoforms; functional annotation of genes and gene families; evolutionary analysis of genes and gene families; characterization and quantification of gene expression.
Biostatistics will introduce the basic concepts of probability theory and statistics, and their main applications in the analysis of biological data.
Biostatistics will introduce the basic concepts of probability theory and statistics, and their main applications in the analysis of biological data.
Expected learning outcomes
At the end of the course the students will be able to:
- be proficient in using the main bioinformatic resources, like genome browser and databases;
- perform simple sequence evolutionary analyses, and understand the results;
- understand and interpret the results obtained by methods for the quantification of gene expression;
- understand and apply to simple case studies the main techniques of probability theory and statistics, and to interpret correctly the results obtained by their application.
- be proficient in using the main bioinformatic resources, like genome browser and databases;
- perform simple sequence evolutionary analyses, and understand the results;
- understand and interpret the results obtained by methods for the quantification of gene expression;
- understand and apply to simple case studies the main techniques of probability theory and statistics, and to interpret correctly the results obtained by their application.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Bioinformatics (6 CFU)
Introduction: definition and aims of Bioinformatics. Genome projects and next-generation sequencing. Gene and genome annotations. A bioinformatic view of the structure of protein-coding genes: exons, introns, promoters, and alternative splicing. The structure of mature eukaryotic mRNAs. Primary and specialized biological databases. Genome browsers. Definition of sequence similarity, homology, orthology, and paralogy. Global and local alignments. Scoring matrices for nucleotide and amino acid sequence alignments (PAM and BLOSUM). BLAST sequence similarity search: algorithm and usage. Multiple sequence alignments. Expression data: microarray and RNA-Seq. Functional gene annotation and gene ontology.
Biostatistics (3 CFU)
Data representation and variable types. Tables, scatterplots and histograms. Mean, median, mode, percentiles. Variance and standard deviation. Definition of probability and conditional probability. Distributions of random variables: normal distribution, hypergeometric distribution, binomial distribution, Poisson distribution. Sampling and sampling distributions. Statistical hypothesis testing: Student's t-test.
Introduction: definition and aims of Bioinformatics. Genome projects and next-generation sequencing. Gene and genome annotations. A bioinformatic view of the structure of protein-coding genes: exons, introns, promoters, and alternative splicing. The structure of mature eukaryotic mRNAs. Primary and specialized biological databases. Genome browsers. Definition of sequence similarity, homology, orthology, and paralogy. Global and local alignments. Scoring matrices for nucleotide and amino acid sequence alignments (PAM and BLOSUM). BLAST sequence similarity search: algorithm and usage. Multiple sequence alignments. Expression data: microarray and RNA-Seq. Functional gene annotation and gene ontology.
Biostatistics (3 CFU)
Data representation and variable types. Tables, scatterplots and histograms. Mean, median, mode, percentiles. Variance and standard deviation. Definition of probability and conditional probability. Distributions of random variables: normal distribution, hypergeometric distribution, binomial distribution, Poisson distribution. Sampling and sampling distributions. Statistical hypothesis testing: Student's t-test.
Prerequisites for admission
Knowledge of the fundamentals of genetics, molecular biology and biochemistry.
Teaching methods
Classes will be held in a computer room, where every student will have a personal computer at his/her disposal. Theoretical lectures will be alternated with practical exercises with the PC.
Teaching Resources
Slides and handouts will be shared with students.
Reference textbook (suggested):
M. Helmer Citterich, F. Ferrè, G. Pavesi, C. Romualdi, G. Pesole, Fondamenti di bioinformatica, Zanichelli editore 2018
Reference textbook (suggested):
M. Helmer Citterich, F. Ferrè, G. Pavesi, C. Romualdi, G. Pesole, Fondamenti di bioinformatica, Zanichelli editore 2018
Assessment methods and Criteria
Bioinformatics: students will perform in small groups bioinformatics analyses, describing methods employed and results obtained in a lab notebook. At the exam, students will discuss the notebook with the teacher, and the grade will depend on their understanding of the methods employed as well as the results obtained.
Biostatistics: written multiple choice test.
The final grade will be the weighted mean of the grades obtained in Bioinformatics and Biostatistics.
Biostatistics: written multiple choice test.
The final grade will be the weighted mean of the grades obtained in Bioinformatics and Biostatistics.
FIS/01 - EXPERIMENTAL PHYSICS
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS
FIS/03 - PHYSICS OF MATTER
FIS/04 - NUCLEAR AND SUBNUCLEAR PHYSICS
FIS/05 - ASTRONOMY AND ASTROPHYSICS
FIS/06 - PHYSICS OF THE EARTH AND OF THE CIRCUMTERRESTRIAL MEDIUM
FIS/07 - APPLIED PHYSICS
FIS/08 - PHYSICS TEACHING AND HISTORY OF PHYSICS
INF/01 - INFORMATICS
MAT/01 - MATHEMATICAL LOGIC
MAT/02 - ALGEBRA
MAT/03 - GEOMETRY
MAT/04 - MATHEMATICS EDUCATION AND HISTORY OF MATHEMATICS
MAT/05 - MATHEMATICAL ANALYSIS
MAT/06 - PROBABILITY AND STATISTICS
MAT/07 - MATHEMATICAL PHYSICS
MAT/08 - NUMERICAL ANALYSIS
MAT/09 - OPERATIONS RESEARCH
SECS-S/01 - STATISTICS
SECS-S/02 - STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS
FIS/03 - PHYSICS OF MATTER
FIS/04 - NUCLEAR AND SUBNUCLEAR PHYSICS
FIS/05 - ASTRONOMY AND ASTROPHYSICS
FIS/06 - PHYSICS OF THE EARTH AND OF THE CIRCUMTERRESTRIAL MEDIUM
FIS/07 - APPLIED PHYSICS
FIS/08 - PHYSICS TEACHING AND HISTORY OF PHYSICS
INF/01 - INFORMATICS
MAT/01 - MATHEMATICAL LOGIC
MAT/02 - ALGEBRA
MAT/03 - GEOMETRY
MAT/04 - MATHEMATICS EDUCATION AND HISTORY OF MATHEMATICS
MAT/05 - MATHEMATICAL ANALYSIS
MAT/06 - PROBABILITY AND STATISTICS
MAT/07 - MATHEMATICAL PHYSICS
MAT/08 - NUMERICAL ANALYSIS
MAT/09 - OPERATIONS RESEARCH
SECS-S/01 - STATISTICS
SECS-S/02 - STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH
Single bench laboratory practical: 32 hours
Lessons: 56 hours
Lessons: 56 hours
Professor:
Pavesi Giulio
Shifts:
-
Professor:
Pavesi GiulioProfessor(s)
Reception:
Tuesday or Friday, h. 15.00- 17.00
Via Celoria 26 (Department of Biosciences)/Online