ArticleBioinformatics (Oxford, England)2014
Basin Hopping Graph: a computational framework to characterize RNA folding landscapes.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- StraD: a coarse-grained simulation of co-transcriptional RNA folding.NAR genomics and bioinformatics · 2025Article
- DrTransformer: heuristic cotranscriptional RNA folding using the nearest neighbor energy model.Bioinformatics (Oxford, England) · 2023Article
- A Local Optima Network View of Real Function Fitness Landscapes.Entropy (Basel, Switzerland) · 2022Article
- RNAxplorer: harnessing the power of guiding potentials to sample RNA landscapes.Bioinformatics (Oxford, England) · 2021Article
- A Polymer Physics Framework for the Entropy of Arbitrary Pseudoknots.Biophysical journal · 2019Article
- Design of Artificial Riboswitches as Biosensors.Sensors (Basel, Switzerland) · 2017Review
- Efficient approximations of RNA kinetics landscape using non-redundant sampling.Bioinformatics (Oxford, England) · 2017Article
- Pseudoknots in RNA folding landscapes.Bioinformatics (Oxford, England) · 2016Article
- Understanding the kinetic mechanism of RNA single base pair formation.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Random versus Deterministic Descent in RNA Energy Landscape Analysis.Advances in bioinformatics · 2016Article
- Network Properties of the Ensemble of RNA Structures.PloS one · 2015Article
- Memory-efficient RNA energy landscape exploration.Bioinformatics (Oxford, England) · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationRNA folding is a complicated kinetic process. The minimum free energy structure provides only a static view of the most stable conformational state of the system. It is insufficient to give detailed insights into the dynamic behavior of RNAs. A sufficiently sophisticated analysis of the folding free energy landscape, however, can provide the relevant information.
resultsWe introduce the Basin Hopping Graph (BHG) as a novel coarse-grained model of folding landscapes. Each vertex of the BHG is a local minimum, which represents the corresponding basin in the landscape. Its edges connect basins when the direct transitions between them are 'energetically favorable'. Edge weights endcode the corresponding saddle heights and thus measure the difficulties of these favorable transitions. BHGs can be approximated accurately and efficiently for RNA molecules well beyond the length range accessible to enumerative algorithms. AVAILABILITY AND IMPLEMENTATION: The algorithms described here are implemented in C++ as standalone programs. Its source code and supplemental material can be freely downloaded from http://www.tbi.univie.ac.at/bhg.html.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.