ArticlePLoS computational biology2015
A Large-Scale Assessment of Nucleic Acids Binding Site Prediction Programs.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation.Biomolecules · 2025Article
- DescribePROT Database of Residue-Level Protein Structure and Function Annotations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- Structure-based prediction of protein-nucleic acid binding using graph neural networks.Biophysical reviews · 2024Review
- HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins.Nucleic acids research · 2024Article
- DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options.Nucleic acids research · 2024Article
- Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet.Nature communications · 2023Article
- Availability of web servers significantly boosts citations rates of bioinformatics methods for protein function and disorder prediction.Bioinformatics advances · 2023Article
- qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids.Protein science : a publication of the Protein Society · 2023Article
- Protein-RNA interaction prediction with deep learning: structure matters.Briefings in bioinformatics · 2022Article
- A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences.Computational and structural biotechnology journal · 2022Article
- Complementarity of the residue-level protein function and structure predictions in human proteins.Computational and structural biotechnology journal · 2022Article
- A Max-Margin Model for Predicting Residue-Base Contacts in Protein-RNA Interactions.Life (Basel, Switzerland) · 2021Article
- dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.Nucleic acids research · 2021Article
- DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.International journal of molecular sciences · 2021Article
- DescribePROT: database of amino acid-level protein structure and function predictions.Nucleic acids research · 2021Article
- Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type.International journal of molecular sciences · 2020Review
- PSIONplusBiomolecules · 2020Article
- Multiple protein-DNA interfaces unravelled by evolutionary information, physico-chemical and geometrical properties.PLoS computational biology · 2020Article
- iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks.BMC bioinformatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational prediction of nucleic acid binding sites in proteins are necessary to disentangle functional mechanisms in most biological processes and to explore the binding mechanisms. Several strategies have been proposed, but the state-of-the-art approaches display a great diversity in i) the definition of nucleic acid binding sites; ii) the training and test datasets; iii) the algorithmic methods for the prediction strategies; iv) the performance measures and v) the distribution and availability of the prediction programs. Here we report a large-scale assessment of 19 web servers and 3 stand-alone programs on 41 datasets including more than 5000 proteins derived from 3D structures of protein-nucleic acid complexes. Well-defined binary assessment criteria (specificity, sensitivity, precision, accuracy…) are applied. We found that i) the tools have been greatly improved over the years; ii) some of the approaches suffer from theoretical defects and there is still room for sorting out the essential mechanisms of binding; iii) RNA binding and DNA binding appear to follow similar driving forces and iv) dataset bias may exist in some methods.
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.