Methods in bioinformatics
A.A. 2018/2019
Learning objectives
The aim of the course is to provide a primer to R programming and Next Generation Sequencing data analysis to students with a background in biological sciences. Teachings will introduce to the basic programming paradigms of the R programming language in order to provide a solid ground for the introduction and discussion of state of the art computational techniques and software- packages for the analysis of NGS biological data. Classes will combine an intuitive description of programming notions and computational biology methods with practicals where concepts will be applied to realistic data analysis use cases. Students are expected to acquire a portfolio of theoretical instruments and practical skills for the handling, analysis and representation of multi-omics biological data, with a strong focus on those generated by modern ultra-high throughput sequencing platforms.
Expected learning outcomes
Non definiti
Periodo: Primo semestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Course syllabus and organization
Edizione unica
Responsabile
Programma
- Main data structures in R: vectors, factors, matrices, arrays, lists and environments.
- Control of execution flow: blocks, conditional statements, loops.
- Functions and scripts
- I/O functions and operators; R data import/export
- Graphical representation of the data , heatmaps, boxplots and Venn diagrams
- Packages and R "extensions"
- Analyzing NGS data with R, Bioconductor
- Analyses of RNA-seq data (1): Normalization, PCA and quality control
- Analyses of RNA-seq data (2): Differential expression analysis
- Analyses of RNA-seq data (3): Post processing and functional enrichment analyses in R
- Control of execution flow: blocks, conditional statements, loops.
- Functions and scripts
- I/O functions and operators; R data import/export
- Graphical representation of the data , heatmaps, boxplots and Venn diagrams
- Packages and R "extensions"
- Analyzing NGS data with R, Bioconductor
- Analyses of RNA-seq data (1): Normalization, PCA and quality control
- Analyses of RNA-seq data (2): Differential expression analysis
- Analyses of RNA-seq data (3): Post processing and functional enrichment analyses in R
Prerequisiti
Oral discussion of the contents of the course and of a small project to address a typical bioinformatics analysis workflow.
Regular attendance to lectures and practicals is highly recommended.
Regular attendance to lectures and practicals is highly recommended.
Metodi didattici
Lectures and lab practicals where students will have personal computers at their disposal.
Materiale di riferimento
Slides, notes and scientific papers will be available at the course web site: http://fzambellimb.ariel.ctu.unimi.it/v3/Home/
Professor(s)
Ricevimento:
Giovedì(Thursday) 15:00-17:00
Secondo piano torre B
Ricevimento:
Venerdì 15.00-16.00 previo appuntamento
Beacon Lab, Piano 2, Torre B, Dip. Bioscienze o su MS Teams