Lunches on course days:

Double Delight Restaurant : 6 days

07,09,10,14,16,17 of July

Address: Budapest, Gábor Dénes u. 2, 1117

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).

Course summary

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).

What you will learn

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.

Main practical topics

The practical content is structured into focused modules covering:

1. Introduction to the computational environment: Linux/Bash fundamentals, remote computing, and good data/project organisation practices. 

2. Next-generation sequencing overview: sequencing platforms, experimental design considerations, and interpretation using genome browsers (e.g., IGV). 

3. Genomics: handling reference genomes/annotations; mapping and variant-oriented workflows; working with standard formats and command-line tooling. 

4. Transcriptomics: RNA-Seq processing, single-cell RNA-Seq, and differential expression analysis.

5. ChIP-seq: mapping, peak calling, binding region definition, and differential analyses. 

6. Functional enrichment analysis: Gene Ontology and pathway-based interpretation of gene sets and differential results. 

7. Marker-gene (16S rRNA) metagenomics: amplicon processing, taxonomic assignment, and diversity analyses.

Course format and learning environment

The course is strongly hands-on: each session combines short conceptual introductions with guided practical work on real datasets, supported by reusable scripts, example reports, and structured tasks. Materials are distributed through a course GitHub repository to encourage reproducibility and good versioning practices.

Course period and venue

7 July 2026 - 17 July 2026

ELTE Institute of Biology, Pázmány Péter sétány 1/C, South Building, 7th floor, room 7.424 

Course requirements

At least level B2 (CEFR) English proficiency.

Basic molecular, genetic, and evolutionary biology knowledge (DNA/RNA/amino acid sequences; gene structure, nucleus/mitochondria, mutation, evolution), and be willing to work with code (no advanced programming required).

The course is suitable for advanced undergraduate, MSc, PhD, and early-career researchers interested in life sciences who want practical competence in omics data analysis. Prior programming experience is helpful but not required: the course starts from the essentials of working on the command line and progressively builds toward complete analysis workflows.

Meet the lecturers

Instruction is provided by an interdisciplinary and international team with expertise spanning genetics, computational methods, and applied omics analysis.

Eszter Ari

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. She is also a research fellow at the HUN-REN Biological Research Centre (Szeged), where she applies comparative genomics and phylogenetics to study microbial evolution, with a particular focus on the emergence and spread of antibiotic resistance and virulence determinants. Her lab develops reusable bioinformatics resources, including the TFLink transcription factor–target interaction database, and contributes software tools such as the mulea R package and the treepruner packages to support reproducible downstream analyses. In addition to research, she is active in training and mentoring across MSc/PhD levels and regularly teaches various bioinformatics methods.

Orsolya Pipek

Orsolya Pipek is a research fellow in the Department of Physics of Complex Systems at the ELTE Faculty of Science. Originally trained as a physicist, she now takes part in interdisciplinary research projects in computational biology and bioinformatics. Her work focuses on analyzing large-scale genomic datasets using machine learning methods for dimensionality reduction, clustering, and prognostic forecasting to identify biomarkers and therapeutic targets in cancer. She developed the IsoMut mutation detection software and has contributed to genomic surveillance projects, including SARS-CoV-2 evolution studies and population genomics from wastewater samples. She also designs epigenetic clock models that estimate age from DNA methylation data to study the process of biological ageing. She teaches graduate courses in Python programming, computer simulations, OMICS methods, and bioinformatics.

Marco Trevisan-Herraz

Marco Trevisan-Herraz is a Research Associate in Bioinformatics in the Biosciences Institute at Newcastle University, where he applies computational and statistical approaches to biological and clinical datasets. He currently works at the laboratory of Prof Gavin Richardson, contributing to research in cardiovascular ageing and cellular senescence. Originally a physicist, he earned a PhD in bioinformatics applied to proteomics at the Centro Nacional de Investigaciones Cardiovasculares (CNIC) in Madrid, where he developed statistical models for the identification, quantification, and systems-level interpretation of mass spectrometry-based proteomics experiments. He is the creator and was the main developer of the SanXoT package for modular quantitative proteomics analysis, co-authoring methods such as the Systems Biology Triangle to detect coordinated protein regulation in proteomics data. He moved to Newcastle University in 2018, where he has worked on projects applying AI to genomics, epigenomics, and single-cell RNA-Seq, including Chromatinsight (a machine learning model for the discovery of differential histone mark patterns).

Ágoston Hunya

Ágoston Hunya is a PhD student in the Genetics program of the Biological Doctoral School at Eötvös Loránd University (ELTE), with a background in Bioinformatics. He conducts his doctoral research under the supervision of Eszter Ari (ELTE) and Balázs Papp at the HUN-REN Biological Research Centre (BRC) in Szeged. His research focuses on the evolution of antimicrobial resistance and virulence determinants with computational and systems-level approaches. In addition to his research activities, he contributes to bioinformatics tool development as a developer of the R implementation of the treetune package. He is actively involved in mentoring MSc students and has teaching experience across multiple bioinformatic disciplines, including sequence alignment, network biology, and marker gene metagenomics. 

Course Syllabus