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Matej Lexa

Researcher at Masaryk University

Publications -  55
Citations -  956

Matej Lexa is an academic researcher from Masaryk University. The author has contributed to research in topics: Retrotransposon & Genome. The author has an hindex of 17, co-authored 54 publications receiving 719 citations. Previous affiliations of Matej Lexa include University of Padua & University of Illinois at Urbana–Champaign.

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pqsfinder: an exhaustive and imperfection-tolerant search tool for potential quadruplex-forming sequences in R.

TL;DR: A newly developed Bioconductor package for identifying potential quadruplex‐forming sequences (PQS), which allows for sequence searches that accommodate possible divergences from the optimal G4 base composition and demonstrates that the algorithm behind the searches has a 96% accuracy.
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The Impact of DNA Extraction Methods on Stool Bacterial and Fungal Microbiota Community Recovery.

TL;DR: Overall, standardized IHMS protocol Q, recommended by the International Human Microbiome Consortium, performed the best when considering all the parameters analyzed, and thus could be applied not only in bacterial, but also in fungal microbiome research.
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Contrasting patterns of transposable element and satellite distribution on sex chromosomes (XY1Y2) in the dioecious plant Rumex acetosa.

TL;DR: In this article, the authors performed low-pass 454 sequencing and similarity-based clustering of male and female genomic 454 reads to identify and characterize major groups of R. acetosa repetitive DNA.
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Dynamics of endogenous cytokinin pools in tobacco seedlings: a modelling approach.

TL;DR: Modelling and measuring of the dynamics of endogenous cytokinins in tobacco plants grown on media supplemented with isopentenyl adenine, zeatin and dihydrozeatin riboside and a simple mathematical model of cytokinin metabolism in developing seedlings found a close match between measured and simulated data.
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RAP: a new computer program for de novo identification of repeated sequences in whole genomes

TL;DR: The Repeat Analysis Program (RAP) is a new word-counting algorithm optimized for high resolution repeat identification using gapped words that results in better specificity both in terms of low-frequency detection, being able to identify sequences repeated only once, and highly divergent detection.