Omics 360: from code to discovery

Foundations for Advanced Multi-omics Integration is a two-week, comprehensive course designed to introduce the fundamentals of genomics, transcript omics, proteomics, epigenomics, metabolomics, and metagenomics. Through engaging lectures and hands-on data analysis sessions, participants will gain a solid foundation in each omics discipline. This course is tailored for aspiring researchers and professionals, setting the stage for our advanced 3-day workshop on multi-omics integration techniques, where sophisticated methods like MOFA will be explored in depth.

1. Comprehend the foundational principles of systems biology and the unique contributions of each omics layer—including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and metagenomics—in understanding biological systems.
2. Interpret and analyze high-throughput biological datasets using R programming, with hands-on experience in tools such as DESeq2, biomaRt, ggplot2, phyloseq, and MOFA2.
3. Design and evaluate omics experiments by understanding best practices in study design, data preprocessing, normalization, quality control, and statistical analysis.
4. Perform domain-specific analyses such as:
   • Variant annotation and filtering for genomics
   • Differential gene expression for transcriptomics
   • Protein quantification and enrichment for proteomics
   • Pathway mapping and clustering in metabolomics
5. Integrate and interpret multi-omics datasets using unsupervised latent factor models (MOFA2), gaining insights into complex biological patterns across different molecular layers.
6. Utilize public databases and bioinformatics resources (e.g., Ensemble, NCBI, UniProt, HMDB, KEGG, SILVA) for data annotation, validation, and biological interpretation.
7. Visualize and communicate biological findings through advanced plotting and dimensionality reduction techniques including PCA, heat maps, volcano plots, and diversity analysis.
8. Critically assess challenges in omics data analysis, including missing data, batch effects, sample complexity, and the importance of reproducibility in bioinformatics workflows.

Two Weeks (8 days)

(Monday, Tuesday, Wednesday, Thursday)

• Senior undergraduates and graduate students in biology, pharmacy, or bioinformatics.
• Clinical researchers and junior scientists interested in omics data.
• Early-career professionals aiming to enhance their data analysis skills in R.
• Interns or staff affiliated with 57357 research programs.

In order to receive your certificate, you have to attend not less than 80%

• Hard copy for 300 Egyptian Pounds
• Soft copy for free