The objective of the course is to introduce the fundamentals of modern analysis in genomics and transcriptomics, and the most relevant and widely used approaches to the bionformatic analysis of data derived from the sequencing of nucleic acids (DNA and RNA).
Expected learning outcomes
At the end of this class , the students are expected to obtain an in-depth knowledge on the most widely used platforms for DNA and RNA sequencing; know theory and practice of the main bioinformatic approaches to the assembly and annotation of genomic sequences; know theory and practice of bioinformatic approaches to variant calling in genomic sequences; know theory and practice of the most widely used bioinformatic pipelines for the characterization and quantification of RNAs, also at the single cell level.
Lesson period: Second semester
(In case of multiple editions, please check the period, as it may vary)
· Genomics o Experimental design o DNA/cDNA/RNA Sequencing including library preparation and QC o Sequence assembly o Sequence annotation (structural and functional) including GO and metabolic pathways annotations o Reduced representation approaches o Variant calling (including CNV and SV) o Phenotype to genotype association methods (QTL, GWAS) o Repeat annotation and analysis o Reference gene annotations (RefSeq, GENCODE) o Alternative splicing and alternative transcripts o Mining and visualizing data: genome browsers
· Transcriptomics o Experimental design o De novo and genome-guided assembly o Gene expression quantification, from qPCR to RNA-Seq o Identification of differential expression o Machine learning approaches to expression data analysis (clustering, dimensionality reduction, principal component analysis) o Small and long non coding RNA identification and analysis o Single cell RNA-Seq data analysis
Prerequisites for admission
Basic knowledge on genetics, molecular biology and biochemistry; basic knowledge of Python and R programming languages and statistics.
Class lectures and practices; during course practices, students will have the opportunity to use their laptop to develop and apply pipelines for the analysis of reference datasets.
Slides, notes and selected articles will be shared with students.
Assessment methods and Criteria
Students will be assigned projects, to be developed in small groups. At the exam, students will present and discuss with the teachers the results obtained.