Computational Approaches for Omics Data
A.Y. 2023/2024
Learning objectives
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
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Course syllabus
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
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
Courses of either of the "Alignment plans" of the 1st semester.
Teaching methods
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.
Teaching Resources
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.
INF/01 - INFORMATICS
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Lessons: 96 hours
Professor(s)
Reception:
Tuesday or Friday, h. 15.00- 17.00
Via Celoria 26 (Department of Biosciences), 2nd Floor Tower B
Reception:
Friday 15.00-16.00 by appointment
Beacon Lab, 2nd floor, B Tower, Dept. of Biosciences / MS Teams