Bioinformatics (BINF)

Biological sciences are being revolutionized by various high-throughput technologies that produce massive amounts of data, by robotics and automation in biological laboratories, and, more recently, by the emergence of artificial intelligence. This is a fantastic opportunity that enables researchers to gain a much deeper understanding on all aspects of life, including for example how species have evolved, how certain mutations cause cancer, what types of cells make up the human body, how the brain functions and ages, how can we find better drugs and biomarkers for treatment and diagnosis of various diseases, how to design better crops and so on.

Bioinformatics is an emerging multidisciplinary field of science at the “intersection” of biology, computer science, and mathematics/statistics. Consequently, there is an increasing demand for bioinformaticians worldwide, i.e., for experts who can solve problems arising from the management and analyses of biological data using various computational and statistical tools. The Bioinformatics Specialization at ELTE builds on core biological knowledge and introduces the essential programming, algorithmic, data-analytic, and AI-based skills needed to address contemporary biological problems using computational approaches.

Without requiring prior programming experience, biology students learn foundational programming skills in Python and acquire data science competencies using R. The specialization further covers bioinformatics algorithms, molecular phylogenetics, structural bioinformatics, and the bioinformatics aspects of diverse omics data. In collaboration with the Institute of Physics and Astronomy, it also offers courses in the rapidly growing areas of data science and machine learning. All courses place strong emphasis on practical work, promoting active thinking and problem-solving rather than passive learning.

The core modules are:

Python programming for biologists

During the course, students will become familiar with the basics of programming using Python, one of the most commonly used programming languages in bioinformatics. During this practice-oriented course, they will write short scripts and encode simple algorithms that are relevant in biology and bioinformatics.

Computational biology algorithms

Students will gain practical experience with core bioinformatics algorithms by implementing and applying them to real biological problems. The course also introduces basic machine learning methods. Through these exercises, students will further develop their programming skills and strengthen their algorithmic and analytical thinking.

Analysis of OMICS data

Students will learn the fundamentals of computational analysis of large, high-throughput biological datasets. They will learn about how to download, process, analyze, visualize, and understand data generated by various OMICS approaches, including genomics, transcriptomics, metagenomics, and proteomics. They will learn how to interpret the results in a biological context and identify and apply follow-up analyses based on them.

Structural bioinformatics

Macromolecular structures are central to understanding biochemical processes and to designing molecules that modulate them. This course focuses primarily on proteins and covers experimental structure-determination methods, structural classification, modern structure-prediction approaches, including recent AI-based methods, and fundamental techniques for analyzing protein dynamics.

Phylogenetics

This course provided a succinct introduction to modern phylogenetic methods with an emphasis on probabilistic methods. Starting with the conceptual foundations of molecular evolution, the course proceeds by introducing and applying substitution models, maximum likelihood and Bayesian inference methods, bootstrap analysis, inference of time-scaled phylogenetic trees, rooting, pruning, and inference of gain and loss of certain genes along the trees.

Introduction to UNIX systems for biologists

The aim of this course is to introduce students to Linux/UNIX-based systems, their practical use, advantages, and limitations. Key topics include networked and remote computing environments, working with modern computational architectures, and the fundamentals of shell scripting and the basics of programming logic.

Bioinformatics is becoming part of research in basically every Department at the Institute of Biology, as well as in some Departments of the Institute of Physics and Astronomy. Genomics, transcriptomics, and proteomics data are used to understand basic biological processes underlying neurological development and diseases, the origin of immune response and antibiotic resistance, or to explore the microbiome of water and soil. They develop novel computational tools to identify cancer mutations, understand the structural and functional properties of proteins, and interpret the effects of various mutations. Theoretical and bioinformatic studies carried out at the University help to understand basic evolutionary events. Bioinformatics is a key component of a project at our university that aims to find new biomarkers for various diseases, such as rheumatoid arthritis, cancer, or neurodegenerative diseases.

Eszter Ari investigates evolutionary processes using genomic and bioinformatic approaches. In collaboration with researchers at the HUN-REN Biological Research Centre, Szeged, she studies the spread of antibiotic-resistant pathogens and strategies to halt their dissemination. Under her supervision, more R packages have been developed for functional enrichment analysis and post-processing of phylogenetic trees. Her group created and continues to expand the popular transcription factor–target gene database, the TFLink.

Zsuzsanna Dosztányi performs studies on intrinsically disordered proteins and protein structures using mainly bioinformatic tools. Her current research is mainly focused on so-called linear binding motif-based protein-protein interactions. She developed various highly cited bioinformatics tools and is the winner of the Momentum Grant.

Gábor Erdős’s research focuses on development of prediction methods to study protein dynamics with a special interest in intrinsically disordered proteins and their functions. He is an expert in machine learning and artificial intelligence. He developed multiple highly cited state of the art methods for this field.

Jónás Dávid is investigating the genetic background of aging in dogs as a model organism. He studied animal breeding and genetics during his formal university training and previously worked on artificial selection projects, pedigree and whole-genome sequence data analysis, on RNA- and ChIP sequencing experiments as well as on genome-wide association studies.

Zoltán Gáspári studies the structural and dynamical properties of proteins with both experimental and computational methods. His current focus is on proteins involved in postsynaptic signaltransduction. He uses structure modelling and prediction tools as well as calculations that incorporate experimental parameters derived from NMR measurements.

Ádám Kun is a theoretical evolutionary biologist studying the origin of life and the origin and evolution of cooperation. He has a formal training in computer science and has been teaching computer programming for biologists since 1999. He has learnt bioinformatics from Pauline Hogeweg, the researcher coining the term.

Bioinformaticians enjoy a wide selection of career paths with a generally positive job outlook. They can pursue academic careers: enrol in PhD programs and become part of research groups at prestigious universities and academic research centres in Hungary and abroad. They can go on to establish independent research groups by developing novel computational tools or by addressing original research questions, leveraging publicly available datasets. Bioinformaticians can also hold positions at core facilities that assist other academic or clinical researchers. There is also a growing demand for bioinformaticians in industry. Job opportunities are typically, though not exclusively, found at companies performing large-scale analyses of genomic and transcriptomic data, often with the aim of supporting developments and therapies in personalized medicine.