Analysis of Omics Data PR

About

Analysis of Omics Data is a practice-oriented summer course designed to give participants a coherent, end-to-end view of how modern high-throughput biological datasets are created, acquired, downloaded, processed, analyzed, understood, and communicated. The course focuses on the computational and statistical thinking that underpins contemporary bioinformatics workflows, with an emphasis on reproducible analysis, appropriate quality control, clear visualisation of results, and the interpretation of complex biological datasets.

Participants will learn how to work efficiently in a Unix/Linux environment, navigate public sequence repositories, and apply widely used community tools and R-based methods to real omics datasets. Practical sessions are built around realistic analysis tasks, guiding students from raw data to interpretable biological conclusions while highlighting common pitfalls (batch effects, mapping artefacts, multiple testing, and biased annotations) and best practices (documented pipelines and structured project layouts)

Course leader

Eszter Ari is an associate professor in the Department of Genetics at the Institute of Biology at ELTE and the leader of the Evolutionary Bioinformatics Research Group

Course aim

The course aims to provide a view and practical knowledge of data downloading, handling, processing, and visualization methods of various high-throughput experiments. The most important topics of the practical: (1) Introduction to the computer environment, bash, and workflow management, (2) Introduction to next-generation sequencing in general, (3) Genomics, (4) Chip-seq, (5) Transcriptomics (RNA-seq), (6) Functional enrichment analysis, (7) Marker gene amplification metagenomics (16S rRNA).
By the end of the course, participants will be able to:

Set up and use a command-line bioinformatics working environment (SSH, shell scripting basics, file manipulation, and project organisation).
Retrieve and manage large-scale omics datasets and reference resources from public repositories, and work with standard genomics file formats (FASTQ, SAM/BAM, BED, GFF/GTF).
Execute core NGS analysis steps with appropriate QC and reporting (quality filtering, alignment/mapping, quantification, and downstream interpretation).
Perform key applied analyses across multiple omics modalities, including genomics, transcriptomics (bulk and single-cell RNA-Seq), ChIP-seq, functional enrichment, and 16S rRNA marker-gene metagenomics.
Communicate results with publication-ready plots and concise, reproducible reports in R/RStudio.

The practical content is structured into focused modules covering:

Introduction to the computational environment: Linux/Bash fundamentals, remote computing, and good data/project organisation practices.
Next-generation sequencing overview: sequencing platforms, experimental design considerations, and interpretation using genome browsers (e.g., IGV).
Genomics: handling reference genomes/annotations; mapping and variant-oriented workflows; working with standard formats and command-line tooling.
Transcriptomics: RNA-Seq processing, single-cell RNA-Seq, and differential expression analysis.
ChIP-seq: mapping, peak calling, binding region definition, and differential analyses.
Functional enrichment analysis: Gene Ontology and pathway-based interpretation of gene sets and differential results.
Marker-gene (16S rRNA) metagenomics: amplicon processing, taxonomic assignment, and diversity analyses

Fee info

tuition fee, course materials, meals (coffee break), opening ceremony and the cost of the leisure time programs, weekend excursions