ArticleNucleic acids research2013
CoRAL: predicting non-coding RNAs from small RNA-sequencing data.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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.
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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.
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Who cites it
15 citing papers in PubMed.
- STAT3 signaling in cancer: mechanisms and targeting therapeutic.Frontiers in immunology · 2026Review
- A Glimpse of Noncoding RNAs: Secondary Structure, Emerging Trends, and Potential Applications in Human Diseases.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Comparison and benchmark of deep learning methods for non-coding RNA classification.PLoS computational biology · 2024Article
- Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets.Functional & integrative genomics · 2023Review
- DASHR 2.0: integrated database of human small non-coding RNA genes and mature products.Bioinformatics (Oxford, England) · 2019Article
- SPAR: small RNA-seq portal for analysis of sequencing experiments.Nucleic acids research · 2018Article
- In Silico Identification of RNA Modifications from High-Throughput Sequencing Data Using HAMR.Methods in molecular biology (Clifton, N.J.) · 2017Article
- A Review on Recent Computational Methods for Predicting Noncoding RNAs.BioMed research international · 2017Review
- DASHR: database of small human noncoding RNAs.Nucleic acids research · 2016Article
- Integrating Epigenomics into the Understanding of Biomedical Insight.Bioinformatics and biology insights · 2016Review
- C/D-box snoRNA-derived RNA production is associated with malignant transformation and metastatic progression in prostate cancer.Oncotarget · 2015Article
- miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences.PloS one · 2015Article
- Computational Approaches for the Analysis of ncRNA through Deep Sequencing Techniques.Frontiers in bioengineering and biotechnology · 2015Review
- NoFold: RNA structure clustering without folding or alignment.RNA (New York, N.Y.) · 2014Article
- Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs.Methods (San Diego, Calif.) · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length and cleavage specificity to distinguish between different ncRNA populations. We evaluated CoRAL using genome-wide small RNA sequencing data sets from four human tissue types and were able to classify six different types of RNAs with ∼80% cross-validation accuracy. Analysis by CoRAL revealed that microRNAs, small nucleolar and transposon-derived RNAs are highly discernible and consistent across all human tissue types assessed, whereas long intergenic ncRNAs, small cytoplasmic RNAs and small nuclear RNAs show less consistent patterns. The ability to reliably annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using small RNA sequencing data in less well-characterized organisms.
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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.