Evidence map›Paper›PMID 24135875›Full record

ArticleInternational journal of molecular sciences2013

Identification of novel microRNAs in primates by using the synteny information and small RNA deep sequencing data.

Zhidong Yuan, Hongde Liu, Yumin Nie, Suping Ding, Mingli Yan, Shuhua Tan, Yuanchang Jin, Xiao Sun

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 95% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

8 authors at 2 institutions in 1 country.

Zhidong YuanSchool of Life Sciences, Hunan University of Science and Technology, Xiangtan 411201, China. xsun@seu.edu.cn.
Hongde Liu
Yumin Nie
Suping Ding
Mingli Yan
Shuhua Tan
Yuanchang Jin
Xiao Sun
Hunan University of Science and Technology · CNState Key Laboratory of Digital Medical Engineering

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current technologies that are used for genome-wide microRNA (miRNA) prediction are mainly based on BLAST tool. They often produce a large number of false positives. Here, we describe an effective approach for identifying orthologous pre-miRNAs in several primates based on syntenic information. Some of them have been validated by small RNA high throughput sequencing data. This approach uses the synteny information and experimentally validated miRNAs of human, and incorporates currently available algorithms and tools to identify the pre-miRNAs in five other primates. First, we identified 929 potential pre-miRNAs in the marmoset in which miRNAs have not yet been reported. Then, we predicted the miRNAs in other primates, and we successfully re-identified most of the published miRNAs and found 721, 979, 650 and 639 new potential pre-miRNAs in chimpanzee, gorilla, orangutan and rhesus macaque, respectively. Furthermore, the miRNA transcriptome in the four primates have been re-analyzed and some novel predicted miRNAs have been supported by the small RNA sequencing data. Finally, we analyzed the potential functions of those validated miRNAs and explored the regulatory elements and transcription factors of some validated miRNA genes of interest. The results show that our approach can effectively identify novel miRNAs and some miRNAs that supported by small RNA sequencing data maybe play roles in the nervous system.

Indexed as

AnimalsComputational BiologyHigh-Throughput Nucleotide SequencingHumansMicroRNAsPrimatesSequence Analysis, RNASyntenyTranscription FactorsTranscriptomeMicroRNAsTranscription Factors

Identifiers

PMID24135875
PMCPMC3821645
OpenAlexW1972726243

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.