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Stephan H. Bernhart

Researcher at Leipzig University

Publications -  90
Citations -  13056

Stephan H. Bernhart is an academic researcher from Leipzig University. The author has contributed to research in topics: RNA & Gene. The author has an hindex of 35, co-authored 73 publications receiving 9836 citations. Previous affiliations of Stephan H. Bernhart include Max Planck Society & University of Vienna.

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ViennaRNA Package 2.0

TL;DR: In this article, exact dynamic programming algorithms can be used to compute ground states, base pairing probabilities, as well as thermodynamic properties of nucleic acids based on carefully measured thermodynamic parameters.
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The Vienna RNA Websuite

TL;DR: The Vienna RNA Websuite provides a web interface to the most commonly used programs of the Vienna RNA package and provides analysis of folding landscapes using the barriers program and structural RNA alignments using LocARNA.
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Pan-cancer analysis of whole genomes

Peter J. Campbell, +1332 more
- 06 Feb 2020 - 
TL;DR: The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.
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The landscape of genomic alterations across childhood cancers

Susanne Gröbner, +185 more
- 15 Mar 2018 - 
TL;DR: The data suggest that 7–8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.
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RNAalifold: improved consensus structure prediction for RNA alignments

TL;DR: The accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices.