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In this course, you will learn the basics of medical genomics, with a special focus on cancer genomics, through lectures, practicals, and projects
Fig 1. Schematic of computational analyses used in the Computational Cancers Genomics Team
- medical genomic concepts
- knowledge of resources (references, databases, workflow repositories)
- sequencing techniques
- basics of molecular biology
- basics of next-generation sequencing and high-resolution sequencing (single-cell sequencing)
- R scripting
- python scripting
- basics of programming
Introduction: course objectives and organization
- Genomics: germline and somatic variation (SNVs, indels, structural variants, mutational signatures, cf DNA), resources (genome references, annotation, databases), sequencing strategies (whole-genome sequencing, whole-exome sequencing, arrays)
- Transcriptomics: heterogeneity and microenvironment, resources (tissue expression reference databases), sequencing strategies (single-cell, spatial), deconvolution
- Epigenomics and multi-modal integration: chromatin and histone modification, resources (annotations and databases for tissue-specific profiles), ATAC-seq, peak calling, differentially methylated positions and regions, deconvolution and identification of cell types, multi-modal integration
- AI: deep learning and multi-modal learning
- TP1: High-resolution sequencing data analysis in python
- TP2: Deep-learning analysis of patholgical images in python
Several projects will be proposed to process (bioinformatic workflow development) and analyze cancer data, related to the interests of researchers of the International Agency for Research on Cancer - WHO. Students will work in small groups (~3-4 people). Weekly meetings (in person or remotely) will take place with the supervisor.
The code used to perform the analyses will be annotated and given to the supervisor (e.g., R code or nextflow code depending on the project). A final project restitution and debriefing will be held at the end of the module, consisting of 10 min presentations by each group. Grades will be given to each group averaging:
- a project grade given by the supervisor, accounting for 50% of the final grade. It is based on the supervisor's assessment of how students addressed the project and related issues (focusing on the process rather than the end results).
- a presentation grade given by all supervisors, accounting for 50% of the final grade. It is based on the results from the project and the students' ability to clearly communicate them
Projects are the following:
- Project 1: Infering cell-cell interactions from visium
- Project 2: Call CNVs from single-cell data
- Project 3: Multimodal analysis of molecular data and encoded medical image data
- Project 4: Predict molecular alterations from medical image data
- Project 5: Predict cell types in bulk RNA-seq from single-cell RNAseq reference
- GATK Best practices
- jupyter notebook: jupyter