MEDFL5255 – Multi-omic data analysis and integration for precision medicine

Schedule, syllabus and examination date

Course content

The aim of this course is to teach students new approaches of multi-omic data analysis to study gene expression regulation in healthy and disease tissues.

The course will present different computational methods to analyze multi-omic datasets in healthy and disease settings. A specific focus will be given to the analysis and integration of datasets dedicated to the study of transcriptional gene regulation, systems biology, and cancer.

The students will get acquainted with good practices and hands-on experience to process, quality-control, visualize, summarize, and analyze large-scale multi-omics data sets. During the course, the students will be exposed to machine learning and computational approaches for managing, analysing, and interpreting next-generation sequencing data (e.g, ChIP-sequencing, mRNA sequencing, ATAC sequencing).

Learning outcome

The students will learn how to computationally process, handle, and analyze multi-omics datasets to study transcriptional gene regulation in healthy tissues and diseases. Specifically, students will get familiar with:

  • Quality control, basic alignment and pre-processing of Illumina sequencing data
  • Analysis and quantification of gene expression data
  • Processing and analysis of ChIP-sequencing data, IDR analysis and peak calling
  • Processing and analysis of ATAC-seq data for chromatin accessibility, TF motif foot printing.
  • Computational modeling of transcription factor (TF)-DNA interactions
  • Quality-control for TF ChIP-seq data analyses
  • Prediction of TF binding sites
  • Computational prediction of transcriptional regulators acting upon gene expression regulation from omics data
  • Prediction of cancer driver non-coding somatic mutations
  • Pre-processing of data for network inference
  • Integration of multi-modal data using network approaches
  • Modelling gene regulatory networks for individual patients
  • Comparative analysis of large-scale gene regulatory networks
  • Discovery of somatic driver genes in cancer
  • Prioritization of somatic driver genes based on the integration of cancer genomes and transcriptomes
  • Discuss scientific papers describing multi-omics data analysis

Admission to the course

Maximum number of participants is 15.

The course is