ArticleRNA (New York, N.Y.)2021
A systematic evaluation of bioinformatics tools for identification of long noncoding RNAs.
Article in RNA (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Unveiling the potential regulatory functions of long non-coding RNAs in potato tuberization through integrated analysis of transcriptomics and phytohormone profiles.BMC plant biology · 2026Article
- Metamorphosis and lncRNAs: A Close Relationship.Genesis (New York, N.Y. : 2000) · 2026Review
- LncPlankton: a comprehensive database of candidate lncRNAs from marine microbial eukaryotes.NAR genomics and bioinformatics · 2025Article
- Identifying SUMOylation-related genes in liver fibrosis with bioinformatics and experimental models for diagnostic insights.Scientific reports · 2025Article
- Navigating the landscape of epitranscriptomics and host immunity.Genome research · 2024Review
- Long non-coding RNAs mediate fish gene expression in response to ocean acidification.Evolutionary applications · 2024Article
- Update on functional analysis of long non-coding RNAs in common crops.Frontiers in plant science · 2024Review
- The VAX2-LINC01189-hnRNPF signaling axis regulates cell invasion and migration in gastric cancer.Cell death discovery · 2023Article
- Analysis of lncRNAs inNon-coding RNA · 2023Article
- Long non‑coding RNAs in multiple myeloma (Review).International journal of oncology · 2023Review
- FishGET: A fish gene expression and transcriptome database with improved accuracy and visualization.iScience · 2023Article
- Review
- Transcriptomes of Zebrafish in Early Stages of Multiple Viral Invasions Reveal the Role of Sterols in Innate Immune Switch-On.International journal of molecular sciences · 2023Article
- The hitchhikers' guide to RNA sequencing and functional analysis.Briefings in bioinformatics · 2023Article
- Exploring the landscape of tools and resources for the analysis of long non-coding RNAs.Computational and structural biotechnology journal · 2023Review
- Article
- A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs.Nucleic acids research · 2022Article
- Dynamic Transcriptional Landscape of Grass Carp (International journal of molecular sciences · 2022Article
- p53-Induced LINC00893 Regulates RBFOX2 Stability to Suppress Gastric Cancer Progression.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput RNA sequencing unveiled the complexity of transcriptome and significantly increased the records of long noncoding RNAs (lncRNAs), which were reported to participate in a variety of biological processes. Identification of lncRNAs is a key step in lncRNA analysis, and a bunch of bioinformatics tools have been developed for this purpose in recent years. While these tools allow us to identify lncRNA more efficiently and accurately, they may produce inconsistent results, making selection a confusing issue. We compared the performance of 41 analysis models based on 14 software packages and different data sets, including high-quality data and low-quality data from 33 species. In addition, computational efficiency, robustness, and joint prediction of the models were explored. As a practical guidance, key points for lncRNA identification under different situations were summarized. In this investigation, no one of these models could be superior to others under all test conditions. The performance of a model relied to a great extent on the source of transcripts and the quality of assemblies. As general references, FEELnc_all_cl, CPC, and CPAT_mouse work well in most species while COME, CNCI, and lncScore are good choices for model organisms. Since these tools are sensitive to different factors such as the species involved and the quality of assembly, researchers must carefully select the appropriate tool based on the actual data. Alternatively, our test suggests that joint prediction could behave better than any single model if proper models were chosen. All scripts/data used in this research can be accessed at http://bioinfo.ihb.ac.cn/elit.
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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.