Omics Data Analysis

Below are the general educational objectives of the course in terms of expected learning outcomes:

• Knowledge and understanding: The course aims to build advanced knowledge and skills for the analysis, integration, and interpretation of various types of omics data, including genomic, transcriptomic, epigenomic, proteomic, and metabolomic data.
• Applying knowledge and understanding: The student will acquire practical skills regarding models and algorithms for omics data analysis, such as: variant calling, differential expression analysis, single-cell data analysis, functional enrichment analysis, peak calling, multi-omic integration, and the construction of reproducible workflows.
• Making judgements: Through practical examples and case studies, the student will be able to independently develop solutions to specific problems related to multi-omics data analysis, choosing the most appropriate pipelines and critically evaluating the results obtained.
• Communication skills: The student will acquire the necessary communication skills and expressive appropriateness in using technical language to effectively describe and present the results of complex analyses.
• Learning skills: The course aims to provide the student with the necessary theoretical and practical methodologies to independently tackle and solve new problems that may arise during a professional activity, enabling them to stay updated on emerging technologies and algorithms in the field of omics analysis.

Frontal lectures with numerous practical computer-based exercises.

Should the teaching be delivered in a mixed or distance mode, the necessary changes may be introduced with respect to what has been previously stated, in order to respect the planned program reported in the syllabus.

• Programming (preferably R or Python)
• Basic Statistics
• Molecular Biology and Genetics
• Fundamentals of Bioinformatics

Attendance in class is mandatory.

To better follow the lessons, slides will be made available by the instructor. Please note that the slides are not a substitute for study but provide a summary to help in learning the concepts illustrated in class. Studying the materials provided by the instructor, the recommended textbook, and completing the exercises is essential to fully grasp the concepts illustrated during the lessons.

Module 1 – Introduction to Multi-Omics Analysis

• Overview of omics data and course objectives: Introduction to the course and types of omics data. Motivations and challenges of multi-omics integration. Workflows and FAIR principles.

• Data retrieval, formats, and preprocessing: Standard formats (FASTQ, BAM, VCF, GTF, mzML). Public databases (GEO, SRA, ENCODE, PRIDE).

• Analysis of genomic data: Review of genomic variants (SNVs, indels, CNVs, SVs). Variant calling pipeline (BWA, GATK). QC and variant filtering.

• Interpretation of genomic data: Functional annotation (ANNOVAR, VEP) and clinical interpretation (ClinVar, SIFT). Exercise: annotation and visualization of variants.

• DNA methylation and chromatin structure: DNA methylation (bisulfite-seq) and introduction to epigenomic analysis.

• ChIP-seq analysis: Concepts of enrichment and peak calling (MACS2). Peak assignment and functional annotation.

• CLIP-seq and CLASH technologies: Overview of PAR-CLIP, iCLIP. miRNA-mRNA interactions and identification of RBP targets.

• Practical session on interaction data: Analysis and visualization of binding sites and interactions.

• RNA-seq: basic pipeline: Alignment, quantification, and normalization (STAR, Salmon, DESeq2).

• Differential expression and functional enrichment: Analysis with DESeq2/edgeR. Enrichment with GO, KEGG, GSEA.

• Transcriptomic visualization: PCA, MA-plot, heatmap, volcano plot.

• Introduction to scRNA-seq: Technologies, filtering, normalization, UMAP/PCA.

• Clustering and cell type identification: Seurat/Scanpy pipeline. Pseudotime and RNA Velocity analysis.

• Spatial transcriptomics: Technologies (Visium, seqFISH) and integration with scRNA-seq.

• Metabolomic data: Techniques (LC-MS, NMR), preprocessing (XCMS), enrichment, and pathway analysis.

• Introduction to proteomics: MS-based proteomics (DDA vs DIA). Open-source software (MaxQuant, OpenMS).

• Approaches to integration: Early, intermediate, late integration. Scaling issues and batch effects.

• Computational methods for integration: iCluster, MOFA, mixOmics. Multiome integration (RNA + ATAC).

• Practical session on real integration: Integration of RNA-seq + methylation or RNA-seq + proteomics.

• Case studies: cancer and immunology: Pan-cancer analysis, immunotherapy, and multi-omics data.

• Visualization and scientific storytelling: ggplot2, Shiny, plotly. Effective presentation of integrated data.

• Case studies: metabolic and neurological diseases: Alzheimer's, diabetes. Multi-omics perspectives.

Given the advanced nature of the subject, the main study material will consist of slides provided by the instructor and key scientific articles. The following texts are recommended for further reading:

• “Modern Statistics for Modern Biology”. Authors: Susan Holmes, Wolfgang Huber. Publisher: Cambridge University Press (2019).
• “Bioinformatics Data Skills”. Author: Vince Buffalo. Publisher: O'Reilly Media (2015).
• "R for Data Science". Authors: Hadley Wickham, Garrett Grolemund. Publisher: O'Reilly Media (2017).

Other updated resources will be indicated by the instructor in the slides used in class.

The final exam consists of a written test and an oral interview in which a project, agreed upon with the instructor, will be discussed.

• The written test and oral interview will be graded out of 30, and the final grade will be a weighted average of the written test score (weight: 25% of the final grade) and the oral exam score (weight: 75% of the final grade).
• The written test consists of a theory question on course topics, which the student must discuss to demonstrate a broad understanding of the subject.
• The minimum passing grade for the written test is 18/30. Students who do not pass the written test cannot take the oral exam. The written test can be reviewed with the instructor at any time.
• The minimum grade to pass the final exam is 18/30.
• The project must be completed within 1 month of passing the written test. The project can be agreed upon with the instructor at any time. If the written grade is refused, the project evaluation will be retained for the entire academic year. If the final grade is refused, the student must retake the entire exam (written test and project).
• The times and locations of the exams will be communicated through the university's official channels.

Notes:

• The use of any hardware (calculators, tablets, smartphones, cell phones, BT headsets, etc.), books, or personal documents is prohibited during the written exam.
• To take the exams, it is mandatory to register using the appropriate tools provided by the university.
• Late registrations via email are not permitted. Without registration, the exam cannot be recorded.
• The assessment may also be conducted online if conditions require it.

Examples of questions for the written exam will be illustrated in class.