Evidence map›Paper›PMID 25654231›Full record

ArticleInternational journal of molecular sciences2015

Predicted trans-acting siRNAs in the human brain.

Xiaoshuang Liu, Guangxin Zhang, Changqing Zhang, Jin Wang

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.6field-weighted citation impact, top 14% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. 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

4 authors at 2 institutions in 1 country.

Xiaoshuang LiuThe State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210093, China. xsliunju@sina.com.
Guangxin ZhangThe State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210093, China. zhangguangxin1234@126.com.
Changqing ZhangCollege of Horticulture, Jinling Institute of Technology, Nanjing 210038, China. zcq@jit.edu.cn.
Jin WangThe State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210093, China. jwang@nju.edu.cn.
Nanjing University · CNJinling Institute of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endogenous small non-coding RNAs play pivotal roles in regulating gene expression in eukaryotes. Many studies have investigated the function and molecular mechanism of microRNAs in the development and disease of various organisms via mRNA repression of protein-coding genes. Recent findings indicate microRNAs might trigger the generation of trans-acting small interfering RNAs (ta-siRNAs). The interaction among different types of small RNA molecules reveals an even more complicated and elaborate pattern of RNA regulation during gene expression than previously thought. We developed a method for mining ta-siRNA sequences and evaluated the performance of our novel method using data from Arabidopsis thaliana. Additionally, using small RNA and degradome data for the human brain, we identified 155 small RNAs that satisfied ta-siRNA characteristics. The DRAXIN and ATCAY genes, which are preferentially expressed in the human brain, were predicted to be the targets of 12 potential ta-siRNAs.

Indexed as

Gene Expression RegulationRNA InterferenceAnimalsBase PairingBase SequenceBinding SitesBrainComputational BiologyDatasets as TopicHumansMiceRNA, Small InterferingRNA, Small Interfering

Identifiers

PMID25654231
PMCPMC4346901
OpenAlexW1964199714

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.