Author

Matej Lexa

Masaryk UniversityORCID 0000-0002-4213-5259Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 3 Organism:plant (other)· 3 Approach:experimental assay· 2 Assay:Hi-C· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Eduard KejnovskýiD· 2 shared Pavel JedličkaiD· 2 shared Zdeněk KubátiD· 2 shared Abdenour KheloufiiD· 1 shared Adrian WysockiiD· 1 shared Agnieszka NobisiD· 1 shared Agnieszka RewicziD· 1 shared Albane BignoniD· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersBioinformatics

Eduard KejnovskýORCID

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4 papers

Recent·Most cited

Climate-driven in-situ trait variation in an annual ruderal grass across Europe

Helene Villhauer, Timo Hellwig, Anna E. Backhaus, Przemysław Baranow, Albane Bignon, José M. Blanco‐Moreno, Denise V. M. Blume, Anna Bomanowska, Milan Brankov, Luís Henrique Mendes da Silva, Andreas D. Drouzas, Walter Durka, Severin Einspanier, Arndt Hampe, Miloš Ilić, , Anna Bucharová

This study examines climate-driven trait variation in Hordeum murinum across Europe, finding that colder and wetter areas support larger plants with heavier seeds, while local vegetation density influences growth and reproductive traits.

Annals of Botany · Jun 27, 2026Peer-reviewedEcology

Y chromosome-coded HSATII repeats may contribute to higher incidence of cancer in men

Hédi Hegyi, Matej Lexa

The paper investigates how Y chromosome-coded HSATII repeats may contribute to higher cancer incidence in men by analyzing their chromatin interactions and triplex formation.

bioRxiv · Aug 16, 2024 · PreprintBioinformatics

Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning.

Jakub Horvath, Pavel Jedlička, Marie Krátká, Zdeněk Kubát, Eduard Kejnovský, Matej Lexa

The paper uses machine learning to classify plant LTR retrotransposons and identifies biologically relevant motifs through explainability analysis.

bioRxiv · Jun 11, 2024 · PreprintBioinformatics

HiC-TE: a computational pipeline for Hi-C data analysis shows a possible role of repeat family interactions in the genome 3D organization

Matej Lexa, Monika Čechová, Son Hoang Nguyen, Pavel Jedlička, Viktor Tokan, Zdeněk Kubát, Roman Hobza, Eduard Kejnovský

The paper develops a Hi-C data analysis pipeline to study repeat family interactions in plant genome 3D organization, finding preferential homotypic interactions among ribosomal DNA, transposons, and some LTR retrotransposons.

Bioinformatics · Jul 4, 2022Peer-reviewedBioinformatics