The course will present some of the most widely used experimental techniques based on next-generation sequencing (NGS) for transcriptome characterization and quantification (RNA-Seq). Different state of the art approaches to the bioinformatic analysis of RNA-Seq data will be outlined both in theory and in practice. During lab classes, students will apply different bioinformatic tools and pipelines to selected case studies. The course is ideally linked to those dealing with functional genomics and bioinformatics.
Expected learning outcomes
At the end of this class , the students are expected to: - be familiar with the most widely used protocols for sample preparation for RNA sequencing; - know the computational and statistical basics of the main bioinformatic protocols for RNA sequencing (transcript assembly and gene annotation), as well as being able to apply them to real case studies; - know the computational and statistical basics of the main bioinformatic protocols for transcript quantification from RNA sequencing and for subsequent analyses (clustering, identification of differentially expressed genes, etc.), as well as being able to apply them to real case studies; - know the computational and statistical basics of the main bioinformatic protocols for single cell RNA-Seq analysis, as well as being able to apply them to real case studies.
The course will present the algorithmic and statistical basics of bionformatic analysis of transcriptomic data. Theoretical explanations will be integrated by hands on exercises in which students will apply the methods learnt to the analysis of real case studies. In detail, the course will cover:
- Experimental design of RNA sequencing - De novo and genome-guided assembly of RNAs - Gene expression quantification, from qPCR to RNA-Seq - Identification of differential expression - Machine learning approaches to expression data analysis (clustering, dimensionality reduction, principal component analysis) - Small and long non coding RNA identification and analysis - Single cell RNA-Seq data analysis
Prerequisites for admission
Knowledge of the basics of genetics and molecular biology. Knowledge of the R and Python programming languages. Knowledge of the basics of probability and statistical testing.
Lectures supported by projected material will be alternated with practical sessions in which students will apply bioinformatic tools and pipelines to the analysis of real case studies.
Copies of the slides projected in the classroom as well as materials used in hands on exercises will be made available through the course Ariel page. The material is made available only to registered students of the Degree Course in Molecular Biotechnology and Bioinformatics and should not be distributed to others. Selected articles describing the bioinformatics tools employed, as well as the respective User's manuals will be also provided.
Assessment methods and Criteria
Students will be assigned projects, consisting in the analysis of different data sets, to be developed individually or in small groups. At the exam, students will discuss with the teachers the techniques employed and interpret the results obtained. The evaluation will take into account both the knowledge of the algorithms and statistical analyses employed, as well as the ability in extracting the biological meaning of the results obtained.