Computational Approaches for Omics Data
A.Y. 2020/2021
Learning objectives
Undefined
Expected learning outcomes
Undefined
Lesson period: Second 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
Teaching methods.
Lessons, webinars, seminars and Journal Clubs will be held in the classroom if possible, according to Government, Region and University rules. All lessons will be video-recorded and made available through the Ariel platform. If necessary, lessons will be held through Microsoft Teams (or other remote platform) and will be available both synchronously at the scheduled time, and asynchronously through Ariel. Laboratory practices will be held according to the rules of the hosting laboratories, if suitably equipped for the COVID emergency, otherwise they will be substituted with live or recorded video tutorials.
Program and reference material.
The program of the lectures will not be changed. All supporting material will be made available through the Ariel platform.
Methods of learning verification and evaluation criteria.
Exams will be held in preferentially in the classrooms, as explained in "Examination Modalities". Alternatively, the examination will take place using the Microsoft Teams or other remote platform.
Lessons, webinars, seminars and Journal Clubs will be held in the classroom if possible, according to Government, Region and University rules. All lessons will be video-recorded and made available through the Ariel platform. If necessary, lessons will be held through Microsoft Teams (or other remote platform) and will be available both synchronously at the scheduled time, and asynchronously through Ariel. Laboratory practices will be held according to the rules of the hosting laboratories, if suitably equipped for the COVID emergency, otherwise they will be substituted with live or recorded video tutorials.
Program and reference material.
The program of the lectures will not be changed. All supporting material will be made available through the Ariel platform.
Methods of learning verification and evaluation criteria.
Exams will be held in preferentially in the classrooms, as explained in "Examination Modalities". Alternatively, the examination will take place using the Microsoft Teams or other remote platform.
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
Basic knowledge on genetics, molecular biology and biochemistry; basic knowledge of Python and R programming languages and statistics.
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:
Pavesi Giulio
Professor(s)
Reception:
Tuesday or Friday, h. 15.00- 17.00
Via Celoria 26 (Department of Biosciences)/Online