Evidence map›Paper›PMID 24833804›Full record

ArticleBioinformatics (Oxford, England)2014

Memory-efficient RNA energy landscape exploration.

Martin Mann, Marcel Kucharík, Christoph Flamm, Michael T Wolfinger

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Design of Artificial Riboswitches as Biosensors.Sensors (Basel, Switzerland) · 2017
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Martin MannBioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany, Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, and Department of Biochemistry and Molecular Cell Biology, Max F. Perutz Laboratories, University of Vienna, A-1030 Vienna, Austria.
Marcel KucharíkBioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany, Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, and Department of Biochemistry and Molecular Cell Biology, Max F. Perutz Laboratories, University of Vienna, A-1030 Vienna, Austria.
Christoph FlammBioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany, Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, and Department of Biochemistry and Molecular Cell Biology, Max F. Perutz Laboratories, University of Vienna, A-1030 Vienna, Austria.
Michael T WolfingerBioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany, Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, and Department of Biochemistry and Molecular Cell Biology, Max F. Perutz Laboratories, University of Vienna, A-1030 Vienna, Austria Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany, Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, and Department of Biochemistry and Molecular Cell Biology, Max F. Perutz Laboratories, University of Vienna, A-1030 Vienna, Austria Bioinformatics Group, Department of Computer Science, University of Freiburg, 79110 Freiburg, Germany, Institute for Theoretical Chemistry, University of Vienna, 1090 Vienna, Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, and Department of Biochemistry and Molecular Cell Biology, Max F. Perutz Laboratories, University of Vienna, A-1030 Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationEnergy landscapes provide a valuable means for studying the folding dynamics of short RNA molecules in detail by modeling all possible structures and their transitions. Higher abstraction levels based on a macro-state decomposition of the landscape enable the study of larger systems; however, they are still restricted by huge memory requirements of exact approaches.

resultsWe present a highly parallelizable local enumeration scheme that enables the computation of exact macro-state transition models with highly reduced memory requirements. The approach is evaluated on RNA secondary structure landscapes using a gradient basin definition for macro-states. Furthermore, we demonstrate the need for exact transition models by comparing two barrier-based approaches, and perform a detailed investigation of gradient basins in RNA energy landscapes. AVAILABILITY AND IMPLEMENTATION: Source code is part of the C++ Energy Landscape Library available at http://www.bioinf.uni-freiburg.de/Software/.

Indexed as

Nucleic Acid ConformationAlgorithmsComputational BiologyKineticsProbabilityRNASoftwareThermodynamicsRNA

Identifiers

PMID24833804
PMCPMC4155248

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.