K
Kristian Vlahoviček
Researcher at University of Zagreb
Publications - 71
Citations - 4005
Kristian Vlahoviček is an academic researcher from University of Zagreb. The author has contributed to research in topics: Genome & Gene. The author has an hindex of 27, co-authored 66 publications receiving 3452 citations. Previous affiliations of Kristian Vlahoviček include Friedrich Miescher Institute for Biomedical Research & University of Skövde.
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Journal ArticleDOI
Histone modification levels are predictive for gene expression
TL;DR: It is found that histone modification levels and gene expression are very well correlated, and it is shown that only a small number of histone modifications are necessary to accurately predict gene expression.
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Cell-of-origin chromatin organization shapes the mutational landscape of cancer
Paz Polak,Paz Polak,Rosa Karlic,Amnon Koren,Amnon Koren,Robert E. Thurman,Richard Sandstrom,Michael S. Lawrence,Alex Reynolds,Eric Rynes,Kristian Vlahoviček,Kristian Vlahoviček,John A. Stamatoyannopoulos,Shamil R. Sunyaev,Shamil R. Sunyaev +14 more
TL;DR: It is shown that chromatin accessibility and modification, together with replication timing, explain up to 86% of the variance in mutation rates along cancer genomes, and that the cell type of origin of a cancer can be accurately determined based on the distribution of mutations along its genome.
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A Retrotransposon-Driven Dicer Isoform Directs Endogenous Small Interfering RNA Production in Mouse Oocytes
Matyas Flemr,Radek Malik,Vedran Franke,Jana Nejepinska,Radislav Sedlacek,Kristian Vlahoviček,Kristian Vlahoviček,Petr Svoboda +7 more
TL;DR: The alternative Dicer isoform, whose phylogenetic origin demonstrates evolutionary plasticity of RNA-silencing pathways, is the main determinant of endogenous RNAi activity in the mouse female germline.
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genomation: a toolkit to summarize, annotate and visualize genomic intervals
TL;DR: An R package, genomation, to expedite the extraction of biological information from high throughput data that works with a variety of genomic interval file types and enables easy summarization and annotation of high throughputData sets with given genomic annotations.
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Prediction of protein-protein interaction sites in sequences and 3D structures by random forests.
TL;DR: The results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information, and are demonstrated by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces.