HUN-REN BRC CF.

Magyar

Bioinformatics

About us

The current paradigm of molecular biology is shifting towards the interpretation of data produced by high-throughput methods. The new data sources allow one to study system-wide biological problems in molecular terms. We are developing novel, generalized knowledge representation schemes for studying the very complex molecular systems of living cells, like the regulatory network of gene expression. The Bioinformatics Group has special expertise in large-scale bioinformatics data management systems that have become an integrating force in systems biology, by providing common platforms and databases for different high-throughput experimental technologies. Modern bioinformatics strategies are no longer limited to the specific evaluation of a particular experiment, but can also help to map the broader context of the results obtained by extensive data mining approaches based on the vast amounts of experimental data available in public databases.

Technology

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Classic Bioinformatics Tasks

Classic bioinformatics techniques have been established over the years to analyze biological data. These classic tools form the backbone of bioinformatics, enabling researchers to derive meaningful insights from biological data. Classical bioinformatics studies generally focus on one or at most a few biological objects (e.g. gene, transcript, protein). Typical examples of classical bioinformatics tasks include sequence alignment, conservation analysis, phylogenetic studies, structural bioinformatics investigation, motif finding, database searching, etc.

Next-Generation Bioinformatics Tasks

Over the past two decades, experimental molecular biology technologies have evolved to allow the simultaneous study of thousands of biological objects in a single experiment. Evaluating the results of these so-called high-throughput methods has required the development of entirely new bioinformatics approaches. The shift towards a new generation of tasks has led to the establishment of scalable data evaluation workflows capable of handling large amounts of data, which require specialized skills to install, operate, and develop. Next-generation bioinformatics involves several fundamental concepts essential for analyzing and interpreting the vast amount of data generated by large-scale experimental technologies. High-throughput bioinformatics workflows are organized processes that begin with stringent quality control of huge experimental datasets, proceed through a complex series of sequential data processing steps, and often finish with the creation of publication-quality visualizations for data evaluation. Data integration from various omics layers (genomics, transcriptomics, proteomics) is also a crucial task in this field essential for a comprehensive understanding of complex biological systems. Statistical analysis methods are employed for exact evaluation of high-throughput experimental data, to assess significance and manage variability. Effective visualization tools are essential for the intuitive presentation of the properties of the investigated complex datasets. Automation of the analysis process for various omics data types (e.g., RNA-seq, ChIP-seq) can also have a very high impact on data processing efficiency.

Custom Bioinformatics Tasks

In certain situations, researchers require custom software solutions to fulfill specific analytical needs that existing tools do not adequately address. This necessitates a thorough understanding of both the biological questions at hand and the computational techniques available. Custom tools are developed to align with the specific requirements of a research project, enabling more relevant analyses that correspond to unique research objectives. These tools can function as stand-alone applications, however nowadays in high-throughput bioinformatics, the most demand is for the creation of complex workflows that combine various tools and methods, and involve the integration of diverse datasets from multiple sources.

Articles

Key publications

  • Faragó Anna; Zvara Ágnes; Tiszlavicz László; Hunyadi-Gulyás Éva; Darula Zsuzsanna; Hegedűs Zoltán; Szabó Enikő; Surguta Sára Eszter; Tóvári József; Puskás László G et al.
    Lectin-Based Immunophenotyping and Whole Proteomic Profiling of CT-26 Colon Carcinoma Murine Model
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 25 : 7 Paper: 4022 , 21 p. (2024)
  • Huszár Krisztina; Welker Zsombor; Györgypál Zoltán; Tóth Eszter; Ligeti Zoltán; Kulcsár Péter István; Dancsó János; Tálas András; Krausz Sarah Laura; Varga Éva et al.
    Position-dependent sequence motif preferences of SpCas9 are largely determined by scaffold-complementary spacer motifs
    NUCLEIC ACIDS RESEARCH 51 : 11 pp. 5847-5863. , 17 p. (2023)
  • Boros Éva; Hegedűs Zoltán; Kellermayer Zoltán; Balogh Péter; Nagy István
    Global alteration of colonic microRNAome landscape associated with inflammatory bowel disease
    FRONTIERS IN IMMUNOLOGY 13 Paper: 991346 , 14 p. (2022)
  • Pap Gergely; Ádám Krisztián; Gyorgypal Zoltan; Toth Laszlo; Hegedus Zoltan
    Depthwise Convolutions using Physicochemical Features of DNA for Transcription Factor Binding Site Classification: Physicochemical Features for DNA-Protein Classification with Depthwise Convolutions
    In: Anon, A (eds.) ICAAI '22: Proceedings of the 6th International Conference on Advances in Artificial Intelligence
    New York City, United States of America : ACM (2022) 164 p. pp. 15-21. , 7 p.
  • Sepp Róbert; Hategan Lidia; Csányi Beáta; Borbás János; Tringer Annamária; Pálinkás Eszter Dalma; Nagy Viktória; Takács Hedvig; Latinovics Dóra; Nyolczas Noémi; Attila Pálinkás; Réka Faludi; Miklós Rábai; Gábor Tamás Szabó; Dániel Czuriga; László Balogh; Róbert Halmosi; Attila Borbély; Tamás Habon; Zoltán Hegedűs; István Nagy
    The Genetic Architecture of Hypertrophic Cardiomyopathy in Hungary: Analysis of 242 Patients with a Panel of 98 Genes
    DIAGNOSTICS 12 : 5 Paper: 1132 , 12 p. (2022)
  • Pap Gergely; Adam Krisztian; Gyorgypal Zoltan; Toth Laszlo; Hegedus Zoltan
    Training models employing physico-chemical properties of DNA for protein binding site detection
    In: Colomo-Palacios, Ricardo; Nawaz, Ripon Kazi Shah (eds.) 2021 International Conference on Applied Artificial Intelligence (ICAPAI) Halden, Norway : IEEE (2021) pp. 73-77. Paper: 9462057 , 5 p.
  • Pap Gergely; Györgypál Zoltán; Ádám Krisztián; Tóth László; Hegedűs Zoltán
    Transcription factor binding site detection using convolutional neural networks with a functional group-based data representation
    JOURNAL OF PHYSICS-CONFERENCE SERIES 1824 : 1 Paper: 012001 , 6 p. (2021)
  • Tálas András; Huszár Krisztina; Kulcsár Péter István; Varga Julia K; Varga Éva; Tóth Eszter; Welker Zsombor; Erdős Gergely; Pach Péter Ferenc; Welker Ágnes Zoltán Györgypál ; Gábor E Tusnády; Ervin Welker
    A method for characterizing Cas9 variants via a one-million target sequence library of self-targeting sgRNAs
    NUCLEIC ACIDS RESEARCH 49 : 6 Paper: e31 , 12 p. (2021)
  • Adam, Krisztian; Gyorgypal, Zoltan; Hegedus, Zoltan
    DNA Readout Viewer (DRV) : visualisation of specificity determining patterns of protein binding DNA segments
    BIOINFORMATICS 36 : 7 pp. 2286-2287. , 2 p. (2020)

Team

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Zoltán HEGEDŰS

group leader, senior research associate

Zoltán GYÖRGYPÁL

research associate