Evidence map›Paper›PMID 24038472›Full record

ArticleNucleic acids research2013

Long non-coding RNA identification over mouse brain development by integrative modeling of chromatin and genomic features.

Jie Lv, Hongbo Liu, Zhijun Huang, Jianzhong Su, Hongjuan He, Youcheng Xiu, Yan Zhang, Qiong Wu

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
3.5field-weighted citation impact, top 7% of its field
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

39 citing papers in PubMed, 79 citations in OpenAlex.

  1. Review
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  6. Review
  7. Article
  8. Article
  9. The computational approaches of lncRNA identification based on coding potential:Computational and structural biotechnology journal · 2020
    Review
  10. Review
  11. Article
  12. Article
  13. Article
  14. The novel lncRNAeLife · 2019
    Article
  15. Article
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  17. Review
  18. Article
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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

8 authors at 2 institutions in 1 country.

Jie LvSchool of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150001, China and College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Hongbo Liu
Zhijun Huang
Jianzhong Su
Hongjuan He
Youcheng Xiu
Yan Zhang
Qiong Wu
Harbin Institute of Technology · CNHarbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In silico prediction of genomic long non-coding RNAs (lncRNAs) is prerequisite to the construction and elucidation of non-coding regulatory network. Chromatin modifications marked by chromatin regulators are important epigenetic features, which can be captured by prevailing high-throughput approaches such as ChIP sequencing. We demonstrate that the accuracy of lncRNA predictions can be greatly improved when incorporating high-throughput chromatin modifications over mouse embryonic stem differentiation toward adult Cerebellum by logistic regression with LASSO regularization. The discriminating features include H3K9me3, H3K27ac, H3K4me1, open reading frames and several repeat elements. Importantly, chromatin information is suggested to be complementary to genomic sequence information, highlighting the importance of an integrated model. Applying integrated model, we obtain a list of putative lncRNAs based on uncharacterized fragments from transcriptome assembly. We demonstrate that the putative lncRNAs have regulatory roles in vicinity of known gene loci by expression and Gene Ontology enrichment analysis. We also show that the lncRNA expression specificity can be efficiently modeled by the chromatin data with same developmental stage. The study not only supports the biological hypothesis that chromatin can regulate expression of tissue-specific or developmental stage-specific lncRNAs but also reveals the discriminating features between lncRNA and coding genes, which would guide further lncRNA identifications and characterizations.

Indexed as

AnimalsBrainCell DifferentiationChromatinEmbryonic Stem CellsGene Expression Regulation, DevelopmentalGenomicsLogistic ModelsMiceRNA, Long NoncodingChromatinRNA, Long Noncoding

Identifiers

PMID24038472
PMCPMC3905897
OpenAlexW2100100194

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 Socratic graph.