Evidence map›Paper›PMID 41587815›Full record

ArticleRNA (New York, N.Y.)2026

Identification of 3D motifs in Rfam with JAR3D.

James Roll, Craig L Zirbel

Abstract read
In one paragraph

Article in RNA (New York, N.Y.), 2026. 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

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

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

2 authors.

James RollDepartment of Computer Science, University of Findlay, Findlay, Ohio 45840, USA rollj@findlay.edu.ORCID 0000-0002-8197-5402
Craig L ZirbelDepartment of Mathematics and Statistics, Bowling Green State University, Bowling Green, Ohio 43403, USA.ORCID 0000-0002-3281-918X

Funding

RNA 3D Motif Search, Atlas, and Prediction from SequenceR01GM085328 · NIGMS · BOWLING GREEN STATE UNIVERSITY · PI ZIRBEL, CRAIG L · 2010 to 2023
$4.0M
NIGMS NIH HHS R01 GM085328
6 · The paper itself

Abstract

Many non-protein-coding RNAs are being discovered each year. At first we know them only by their sequences in a few organisms, but to understand their function and interactions, we need to understand what 3D structures they may form, in whole or in part. Many hairpin and internal loops are known to form recurrent structured 3D motifs, for example, kink turn and sarcin-ricin internal loops, and GNRA and T-loop hairpin loops. A new non-protein-coding RNA may have one or more known structured 3D loop motifs. Here we introduce a tool which identifies loops in Rfam seed alignments that match known 3D loop motifs and makes those identifications easily accessible. JAR3D was developed to map sequences of hairpin and internal loops to known 3D motifs, and was extended for this work to three-way and four-way junction motifs. We applied JAR3D to 4166 Rfam seed alignments from Rfam 15.0 and made the results accessible on the JAR3D web page, making it easy to evaluate the possible matches for each loop in each Rfam family. We provide several examples which validate JAR3D's ability to identify the correct loop motif, using 3D structures of RNAs outside of the training set. We created a page to search for instances of a particular loop motif across all Rfam families, to study how widespread the occurrence of each motif is. We provide statistics on how many Rfam loops appear to match well to a known 3D motif. Match rates are much higher for internal loops than for hairpins or multihelix junctions.

Indexed as

Nucleic Acid ConformationNucleotide MotifsRNA, UntranslatedSoftwareBase SequenceSequence AlignmentRNA, Untranslated3D structure predictionnoncoding RNARfamRNA motifsecondary structure

Identifiers

PMID41587815
PMCPMC13085918

What OpenQuestion holds

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LicenceCC BY-NC
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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.