ArticleNucleic acids research2017
COME: a robust coding potential calculation tool for lncRNA identification and characterization based on multiple features.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed, 127 citations in OpenAlex.
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- Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.Cell communication and signaling : CCS · 2025Review
- Role of non-coding RNAs in quality improvement of horticultural crops: computational tools, databases, and algorithms for identification and analysis.Functional & integrative genomics · 2025Review
- Long non-coding RNA-encoded micropeptides: functions, mechanisms and implications.Cell death discovery · 2024Review
- Current understanding of functional peptides encoded by lncRNA in cancer.Cancer cell international · 2024Review
- ntEmbd: Deep learning embedding for nucleotide sequences.bioRxiv : the preprint server for biology · 2024Article
- Prediction of lncRNA functions using deep neural networks based on multiple networks.BMC genomics · 2023Article
- RNAcode_Web - Convenient identification of evolutionary conserved protein coding regions.Journal of integrative bioinformatics · 2023Article
- RNAincoder: a deep learning-based encoder for RNA and RNA-associated interaction.Nucleic acids research · 2023Article
- Small Open Reading Frame-Encoded Micro-Peptides: An Emerging Protein World.International journal of molecular sciences · 2023Review
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- Exploring the landscape of tools and resources for the analysis of long non-coding RNAs.Computational and structural biotechnology journal · 2023Review
- LncRNAs in neuropsychiatric disorders and computational insights for their prediction.Molecular biology reports · 2022Review
- A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs.Nucleic acids research · 2022Article
- LncDC: a machine learning-based tool for long non-coding RNA detection from RNA-Seq data.Scientific reports · 2022Article
- The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges.Animals : an open access journal from MDPI · 2022Review
- Deep learning tools are top performers in long non-coding RNA prediction.Briefings in functional genomics · 2022Article
- Review
1 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent genomic studies suggest that novel long non-coding RNAs (lncRNAs) are specifically expressed and far outnumber annotated lncRNA sequences. To identify and characterize novel lncRNAs in RNA sequencing data from new samples, we have developed COME, a coding potential calculation tool based on multiple features. It integrates multiple sequence-derived and experiment-based features using a decompose-compose method, which makes it more accurate and robust than other well-known tools. We also showed that COME was able to substantially improve the consistency of predication results from other coding potential calculators. Moreover, COME annotates and characterizes each predicted lncRNA transcript with multiple lines of supporting evidence, which are not provided by other tools. Remarkably, we found that one subgroup of lncRNAs classified by such supporting features (i.e. conserved local RNA secondary structure) was highly enriched in a well-validated database (lncRNAdb). We further found that the conserved structural domains on lncRNAs had better chance than other RNA regions to interact with RNA binding proteins, based on the recent eCLIP-seq data in human, indicating their potential regulatory roles. Overall, we present COME as an accurate, robust and multiple-feature supported method for the identification and characterization of novel lncRNAs. The software implementation is available at https://github.com/lulab/COME.
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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.