ArticleG3 (Bethesda, Md.)2019
Molecular Traits of Long Non-protein Coding RNAs from Diverse Plant Species Show Little Evidence of Phylogenetic Relationships.
Article in G3 (Bethesda, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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.
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Who cites it
10 citing papers in PubMed.
- Integrative multi-omics analysis widens annotation and functional insights into long non-coding RNAs of Arabidopsis thaliana.Biology direct · 2026Article
- Diversity of lncRNAs in the pan-transcriptome of maize inbred lines.BMC genomics · 2026Article
- Long Noncoding RNAs in Response to Hyperosmolarity Stress, but Not Salt Stress, Were Mainly Enriched in the Rice Roots.International journal of molecular sciences · 2024Article
- Linking discoveries, mechanisms, and technologies to develop a clearer perspective on plant long noncoding RNAs.The Plant cell · 2023Review
- Revealing the novel complexity of plant long non-coding RNA by strand-specific and whole transcriptome sequencing for evolutionarily representative plant species.BMC genomics · 2022Article
- The evolutionary landscape and expression pattern of plant lincRNAs.RNA biology · 2022Article
- A vast pool of lineage-specific microproteins encoded by long non-coding RNAs in plants.Nucleic acids research · 2021Article
- Roles of Non-Coding RNAs in Response to Nitrogen Availability in Plants.International journal of molecular sciences · 2020Review
- Long non-coding RNAs: emerging players regulating plant abiotic stress response and adaptation.BMC plant biology · 2020Review
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long non-coding RNAs (lncRNAs) represent a diverse class of regulatory loci with roles in development and stress responses throughout all kingdoms of life. LncRNAs, however, remain under-studied in plants compared to animal systems. To address this deficiency, we applied a machine learning prediction tool, Classifying RNA by Ensemble Machine learning Algorithm (CREMA), to analyze RNAseq data from 11 plant species chosen to represent a wide range of evolutionary histories. Transcript sequences of all expressed and/or annotated loci from plants grown in unstressed (control) conditions were assembled and input into CREMA for comparative analyses. On average, 6.4% of the plant transcripts were identified by CREMA as encoding lncRNAs. Gene annotation associated with the transcripts showed that up to 99% of all predicted lncRNAs for
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Registered trials
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