Evidence map›Paper›PMID 26938562›Full record

ArticleGenes2016

Genome-Wide Identification and Characterization of Long Non-Coding RNAs from Mulberry (Morus notabilis) RNA-seq Data.

Xiaobo Song, Liang Sun, Haitao Luo, Qingguo Ma, Yi Zhao, Dong Pei

Abstract read
In one paragraph

Article in Genes, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–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

14 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. A Novel LncRNA,Frontiers in plant science · 2018
    Article
  13. Article
  14. 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

6 authors.

Xiaobo SongCollege of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China. songxiaoboo@126.com.
Liang SunKey Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. luohaitao@ict.ac.cn.
Haitao LuoKey Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. biozy@ict.ac.cn.
Qingguo MaState Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China. maqgme@163.com.
Yi ZhaoKey Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. biozy@ict.ac.cn.
Dong PeiState Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China. peigu@caf.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Numerous sources of evidence suggest that most of the eukaryotic genome is transcribed into protein-coding mRNAs and also into a large number of non-coding RNAs (ncRNAs). Long ncRNAs (lncRNAs), a group consisting of ncRNAs longer than 200 nucleotides, have been found to play critical roles in transcriptional, post-transcriptional, and epigenetic gene regulation across all kingdoms of life. However, lncRNAs and their regulatory roles remain poorly characterized in plants, especially in woody plants. In this paper, we used a computational approach to identify novel lncRNAs from a published RNA-seq data set and analyzed their sequences and expression patterns. In total, 1133 novel lncRNAs were identified in mulberry, and 106 of these lncRNAs displayed a predominant tissue-specific expression in the five major tissues investigated. Additionally, functional predictions revealed that tissue-specific lncRNAs adjacent to protein-coding genes might play important regulatory roles in the development of floral organ and root in mulberry. The pipeline used in this study would be useful for the identification of lncRNAs obtained from other deep sequencing data. Furthermore, the predicted lncRNAs would be beneficial towards an understanding of the variations in gene expression in plants.

Indexed as

lncRNAsMorus notabilisRNA-seq

Identifiers

PMID26938562
PMCPMC4808792

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.