CoChat
Sign Up
PreprintCited by 12RNA and protein synthesis mechanismsProtein Structure and DynamicsGenomics and Phylogenetic Studies

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality

Open access
Bernard, Clément, Postic, Guillaume, Ghannay, Sahar, Tahi, Fariza·2023·Briefings in Bioinformatics
DOI 10.1101/2023.11.14.567018PMC PMC10939302

Preprint — published as 10.1093/bib/bbae064 · Paper page

Open this paper in CoChat — free accountOpen PDF

Abstract

RNA is a complex macromolecule that plays central roles in the cell. While it is well-known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr .

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2023.11.14.567018

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 41

  1. 10.1073/pnas.0703836104 · DOI
  2. 10.1016/j.str.2020.05.011 · DOI
  3. 10.1063/1.4881424 · DOI
  4. 10.1021/acs.jctc.5b00200 · DOI
  5. 10.1093/bioinformatics/btv221 · DOI
  6. Zeming Lin , Halil Akin , Roshan Rao , Brian Hie , Zhongkai Zhu , Wenting Lu , Nikita Smetanin , Allan dos Santos Costa , Maryam Fazel-Zarandi , Tom Sercu , Sal Candido , et al. Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv, 2022.
  7. 10.1002/prot.20264 · DOI · Paper page
  8. 10.1261/rna.1700409 · DOI
  9. 10.1007/s10100-013-0296-5 · DOI
  10. 10.1261/rna.1270809 · DOI
  11. 10.1093/nar/gkv1479 · DOI
  12. 10.1021/acs.jctc.0c01148 · DOI
  13. Dong Zhang , Shi jie Chen , and Ruhong Zhou . Modeling Noncanonical RNA Base Pairs by a Coarse-Grained IsRNA2 Model. The journal of physical chemistry. B, 2021. · DOI
  14. 10.1093/nar/gkad122 · DOI
  15. 10.1093/bioinformatics/btr093 · DOI
  16. 10.1016/j.bpj.2021.11.016 · DOI
  17. 10.1093/bioinformatics/btr093 · DOI
  18. 10.1093/nar/gku972 · DOI
  19. 10.1093/nar/gkm216 · DOI
  20. 10.1002/(SICI)1097-0134(1999)37:3+<22::AID-PROT5>3.0.CO;2-W · DOI
  21. 10.1002/prot.24172 · DOI
  22. 10.1093/bioinformatics/btt473 · DOI · Paper page
  23. 10.1261/rna.1837410 · DOI
  24. 10.1002/prot.24121 · DOI
  25. 10.1006/jmbi.1997.1479 · DOI
  26. 10.1002/prot.1087 · DOI
  27. 10.1002/prot.21279 · DOI
  28. 10.1110/ps.0217002 · DOI · Paper page
  29. 10.1110/ps.062416606 · DOI
  30. 10.1371/journal.pone.0015386 · DOI
  31. 10.1261/rna.069872.118 · DOI
  32. 10.1093/nar/gkv141 · DOI
  33. 10.1371/journal.pcbi.1006514 · DOI
  34. 10.1126/science.abe5650 · DOI
  35. 10.1093/nar/gkac372 · DOI
  36. 10.1261/rna.031054.111 · DOI
  37. 10.1097/01.NND.0000320699.47006.a3 · DOI
  38. 10.1006/jmbi.1993.1626 · DOI · Paper page
  39. 10.1093/nar/gks339 · DOI
  40. 10.3390/ijms20174116 · DOI
  41. Alexandre Lacoste , Alexandra Luccioni , Victor Schmidt , and Thomas Dandres . Quantifying the carbon emissions of machine learning. In Climate Change workshop, NeurIPS 2019, 2019.

Metadata

Authors
Bernard, C., Postic, G., Ghannay, S., & Tahi, F.
Affiliations
  • Université Paris-Saclay (FR)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
Tahi, Fariza
ORCID
Venue
Briefings in Bioinformatics
Publisher
openRxiv
Published
2023-11-16
Volume
25
Issue
2
Pages
bbae064
ISSN
1467-5463 (ISSN-L)
DOI
10.1101/2023.11.14.567018
PMC
PMC10939302
OpenAlex
W4392393183
Cited by
12
References
1
Citations by year
2026: 3 · 2025: 4 · 2024: 5
FWCI
1.63
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, other-oa)
  • HAL (submittedVersion)
Funders
  • Agence Nationale de la Recherche (ANR-11-IDEX-0003, ANR-11-LABX-0045, ANR-17-CONV-0003, ANR-20-THIA-0013, ANR-17)
  • Grand Équipement National De Calcul Intensif (AD011014250)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
RNA structure prediction, model quality assessment, scoring functions, benchmarking, RNA tertiary structure
MeSH terms
Models, Structural, RNA, Software, Benchmarking

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review