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BARNACLE specifications

Information


Unique identifier OMICS_23809
Name BARNACLE
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Python
License GNU General Public License version 2.0
Computer skills Advanced
Version 0.21
Stability Beta
Maintained Yes

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Documentation


Maintainer


  • person_outline Thomas Hamelryck

Publication for BARNACLE

BARNACLE citations

 (7)
library_books

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

2018
PMCID: 5920944
PMID: 29297679
DOI: 10.1021/acs.chemrev.7b00427

[…] set of torsion angles, which are obtained according to a knowledge-based CG probability distribution taking into account correlations between the consecutive torsions. This method was inspired by the BARNACLE algorithm, and makes use of the RASP scoring function to evaluate the energy during sampling and posterior refinement steps. It is intended to be used as a refinement tool, having been tested […]

library_books

Capturing RNA Folding Free Energy with Coarse Grained Molecular Dynamics Simulations

2017
Sci Rep
PMCID: 5385882
PMID: 28393861
DOI: 10.1038/srep45812

[…] d predict relevant 3-D RNA structures. Template based modeling uses predefined, small motifs to assemble RNA structures from their sequence. Template based models include the MC-Fold/MC-Sym pipeline, BARNACLE, RSIM, 3dRNA, RNAComposer, Vfold, RNA-MoIP and FARNA/FARFAR available in the Rosetta package. Similar to template based modeling, ASSEMBLE and RNA2D3D use homologous RNA structures to predict […]

library_books

Structure Prediction of RNA Loops with a Probabilistic Approach

2016
PLoS Comput Biol
PMCID: 4975501
PMID: 27494763
DOI: 10.1371/journal.pcbi.1005032

[…] e of their approach is its ability to go beyond the native structures by accounting for the full free energy landscape, including all the non-native folds. Frellsen and colleagues developed a program BARNACLE (BAyesian network model of RNA using Circular distributions and maximum Likelihood Estimation) to remove some important limitations associated with the discrete nature of fragment assembly me […]

library_books

Harvest locations of goose barnacles can be successfully discriminated using trace elemental signatures

2016
Sci Rep
PMCID: 4904244
PMID: 27292413
DOI: 10.1038/srep27787

[…] Goose barnacle samples were analyzed for barium (Ba), boron (B), calcium (Ca), cadmium (Cd), chromium (Cr), lithium (Li), magnesium (Mg), manganese (Mn), phosphorous (P), lead (Pb), strontium (Sr) and zinc […]

library_books

Predicting Helical Topologies in RNA Junctions as Tree Graphs

2013
PLoS One
PMCID: 3753280
PMID: 23991010
DOI: 10.1371/journal.pone.0071947

[…] to fold RNAs with the guidance of physics or knowledge-based energy functions; MC-Sym predicts all-atom models of RNA by inserting small cyclic motif fragments, collected from solved RNA structures. BARNACLE uses a coarse-grained probabilistic model of RNA to predict atomic models by efficient sampling of RNA conformations. MOSAIC is another approach to efficiently and accurately model RNAs by […]

library_books

Towards 3D structure prediction of large RNA molecules: an integer programming framework to insert local 3D motifs in RNA secondary structure

2012
Bioinformatics
PMCID: 3371858
PMID: 22689763
DOI: 10.1093/bioinformatics/bts226

[…] popular ones are FARNA (), the MC-Pipeline (), iFoldRNA () and NAST (). A recent review by ) proposes a comprehensive overview of these strategies. Conditional random fields techniques implemented in BARNACLE () and TreeFolder () also appear as a promising approach.However, unlike classical secondary structure predictors such as RNAstructure (), RNAfold (), unafold (), contrafold () or contextfold […]

Citations

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BARNACLE institution(s)
The Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark; Department of Statistics, University of Leeds, Leeds, UK; DTU Elektro, Technical University of Denmark, Lyngby, Denmark
BARNACLE funding source(s)
Supported by Carlsbergs Mindelegat, the Danish Research Council for Technology and Production Sciences, the Danish National Research Foundation and the University of Copenhagen.

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