Evidence map›Paper›PMID 41034743›Full record

ArticleBMC bioinformatics2025

Can geometric combinatorics improve RNA branching predictions?

Svetlana Poznanović, Owen Cardwell, Christine Heitsch

Abstract read
In one paragraph

Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Svetlana PoznanovićSchool of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, 29634, USA. spoznan@clemson.edu.
Owen CardwellSchool of Mathematics, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Christine HeitschSchool of Mathematics, Georgia Institute of Technology, Atlanta, GA, 30332, USA. heitsch@math.gatech.edu.

Funding

National Science Foundation 1764406National Science Foundation 1815044NIGMS NIH HHS GM126554Simons Foundation 594594
6 · The paper itself

Abstract

backgroundPrior results for tRNA and 5S rRNA demonstrated that secondary structure prediction accuracy can be significantly improved by modifying the parameters in the multibranch loop entropic penalty function. However, for reasons not well understood at the time, the scale of improvement possible across both families was well below the level for each family when considered separately.

resultsWe resolve this dichotomy here by showing that each family has a characteristic target region geometry, which is distinct from the other and significantly different from their own dinucleotide shuffles. This required a much more efficient approach to computing the necessary information from the branching parameter space, and a new theoretical characterization of the region geometries.

conclusionsThe insights gained point strongly to considering multiple possible secondary structures generated by varying the multiloop parameters. We provide proof-of-principle results that this significantly improves prediction accuracy across all 8 additional families in the Archive II benchmarking dataset.

Indexed as

Computational BiologyNucleic Acid ConformationRNARNA, TransferSequence Analysis, RNAAlgorithmsRNA, Ribosomal, 5SRNARNA, Ribosomal, 5SRNA, TransferBranching parametersMultiloopsNNTMSecondary structure

Identifiers

PMID41034743
PMCPMC12487464

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LicenceCC BY-NC-ND
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Registered trials

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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.