ArticleNucleic acids research2019
CPPred: coding potential prediction based on the global description of RNA sequence.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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73 citing papers in PubMed.
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- Reference-guided genome assembly of long non-coding rna transcripts reveals target genes associated with Crohn's disease.Scientific reports · 2026Article
- Integrative transcriptomic and network analysis reveals small open reading frames associated with cellular adaptive responsees and methylation-linked pathogenicity in Leptospira interrogans.Functional & integrative genomics · 2026Article
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- Language model-based self-training reduces labeled data requirements by 99% for biological sequence classification.Briefings in functional genomics · 2026Article
- A Model for the Development of Alzheimer's Disease.Genomics, proteomics & bioinformatics · 2025Article
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- Interpretation of RNA Universe and Coding Potential Using IntRNA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Reference-guided Genome Assembly of Long Non-coding RNA Transcripts Reveals Target Genes Associated With Crohn's Disease.bioRxiv : the preprint server for biology · 2025Article
- Evaluating computational tools for protein-coding sequence detection: Are they up to the task?RNA (New York, N.Y.) · 2025Article
- Leveraging explainable multi-scale features for fine-grained circRNA-miRNA interaction prediction.BMC biology · 2025Article
- Enhancing LncRNA-miRNA interaction prediction with multimodal contrastive representation learning.Briefings in bioinformatics · 2025Article
- Human long noncoding RNAJournal of virology · 2025Article
- misORFPred: A Novel Method to Mine Translatable sORFs in Plant Pri-miRNAs Using Enhanced Scalable k-mer and Dynamic Ensemble Voting Strategy.Interdisciplinary sciences, computational life sciences · 2025Article
- SORFPP: Enhancing rich sequence-driven information to identify SEPs based on fused framework on validation datasets.PloS one · 2025Article
- Identification and functional analysis of growth rate associated long non-coding RNAs inComputational and structural biotechnology journal · 2025Article
- TetraRNA, a tetra-class machine learning model for deciphering the coding potential derivation of RNA world.Computational and structural biotechnology journal · 2025Article
13 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
The rapid and accurate approach to distinguish between coding RNAs and ncRNAs has been playing a critical role in analyzing thousands of novel transcripts, which have been generated in recent years by next-generation sequencing technology. Previously developed methods CPAT, CPC2 and PLEK can distinguish coding RNAs and ncRNAs very well, but poorly distinguish between small coding RNAs and small ncRNAs. Herein, we report an approach, CPPred (coding potential prediction), which is based on SVM classifier and multiple sequence features including novel RNA features encoded by the global description. The CPPred can better distinguish not only between coding RNAs and ncRNAs, but also between small coding RNAs and small ncRNAs than the state-of-the-art methods due to the addition of the novel RNA features. A recent study proposes 1335 novel human coding RNAs from a large number of RNA-seq datasets. However, only 119 transcripts are predicted as coding RNAs by the CPPred. In fact, almost all proposed novel coding RNAs are ncRNAs (91.1%), which is consistent with previous reports. Remarkably, we also reveal that the global description of encoding features (T2, C0 and GC) plays an important role in the prediction of coding potential.
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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 Socratic graph.