Evidence map›Paper›PMID 26485761›Full record

ArticleOncotarget2015

LncRNA ontology: inferring lncRNA functions based on chromatin states and expression patterns.

Yongsheng Li, Hong Chen, Tao Pan, Chunjie Jiang, Zheng Zhao, Zishan Wang, Jinwen Zhang, Juan Xu, Xia Li

Abstract read
In one paragraph

Article in Oncotarget, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Computational Resources for lncRNA Functions and Targetome.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  7. Article
  8. Noncoding RNAs as Key Regulators for Cardiac Development and Cardiovascular Diseases.Journal of cardiovascular development and disease · 2023
    Review
  9. Article
  10. Review
  11. Review
  12. Article
  13. Review
  14. Article
  15. Computational Models in Non-Coding RNA and Human Disease.International journal of molecular sciences · 2020
    Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yongsheng LiCollege of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Nangang, Harbin, Heilongjiang, China.
Hong ChenCollege of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Nangang, Harbin, Heilongjiang, China.
Tao PanCollege of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Nangang, Harbin, Heilongjiang, China.
Chunjie JiangCollege of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Nangang, Harbin, Heilongjiang, China.
Zheng ZhaoCollege of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Nangang, Harbin, Heilongjiang, China.
Zishan WangCollege of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Nangang, Harbin, Heilongjiang, China.
Jinwen ZhangCollege of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Nangang, Harbin, Heilongjiang, China.
Juan XuCollege of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Nangang, Harbin, Heilongjiang, China.
Xia LiCollege of Bioinformatics Science and Technology and Bio-Pharmaceutical Key Laboratory of Heilongjiang Province, Harbin Medical University, Nangang, Harbin, Heilongjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accumulating evidences suggest that long non-coding RNAs (lncRNAs) perform important functions. Genome-wide chromatin-states area rich source of information about cellular state, yielding insights beyond what is typically obtained by transcriptome profiling. We propose an integrative method for genome-wide functional predictions of lncRNAs by combining chromatin states data with gene expression patterns. We first validated the method using protein-coding genes with known function annotations. Our validation results indicated that our integrative method performs better than co-expression analysis, and is accurate across different conditions. Next, by applying the integrative model genome-wide, we predicted the probable functions for more than 97% of human lncRNAs. The putative functions inferred by our method match with previously annotated by the targets of lncRNAs. Moreover, the linkage from the cellular processes influenced by cancer-associated lncRNAs to the cancer hallmarks provided a "lncRNA point-of-view" on tumor biology. Our approach provides a functional annotation of the lncRNAs, which we developed into a web-based application, LncRNA Ontology, to provide visualization, analysis, and downloading of lncRNA putative functions.

Indexed as

Gene OntologyAlgorithmsCell LineCell Line, TumorCells, CulturedChromatinGene Expression ProfilingGene Regulatory NetworksHeLa CellsHep G2 CellsHistonesHumansK562 CellsLysineMethylationModels, GeneticChromatinHistonesLysineRNA, Long NoncodingRNA, Messengerchromatin patternintegrated modellncRNA functionslncRNA ontologylong non-coding RNA

Identifiers

PMID26485761
PMCPMC4741861

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.