Evidence map›Paper›PMID 19357091›Full record

ArticleNucleic acids research2009

Mining small RNA sequencing data: a new approach to identify small nucleolar RNAs in Arabidopsis.

Ho-Ming Chen, Shu-Hsing Wu

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Nucleic acids research, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

21 citing papers in PubMed, 32 citations in OpenAlex.

  1. RNA regulation in plants.Science China. Life sciences · 2026
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  10. The Existence and Localization of Nuclear snoRNAs inPlants (Basel, Switzerland) · 2020
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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

2 authors at 1 institution in 1 country.

Ho-Ming ChenInstitute of Plant and Microbial Biology, Academia Sinica, Taiwan International Graduate Program, National Chung-Hsing University and Academia Sinica, Taipei, Taiwan.
Shu-Hsing Wu
National Chung Hsing University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small nucleolar RNAs (snoRNAs) are noncoding RNAs that direct 2'-O-methylation or pseudouridylation on ribosomal RNAs or spliceosomal small nuclear RNAs. These modifications are needed to modulate the activity of ribosomes and spliceosomes. A comprehensive repertoire of snoRNAs is needed to expand the knowledge of these modifications. The sequences corresponding to snoRNAs in 18-26-nt small RNA sequencing data have been rarely explored and remain as a hidden treasure for snoRNA annotation. Here, we showed the enrichment of small RNAs at Arabidopsis snoRNA termini and developed a computational approach to identify snoRNAs on the basis of this characteristic. The approach successfully uncovered the full-length sequences of 144 known Arabidopsis snoRNA genes, including some snoRNAs with improved 5'- or 3'-end annotation. In addition, we identified 27 and 17 candidates for novel box C/D and box H/ACA snoRNAs, respectively. Northern blot analysis and sequencing data from parallel analysis of RNA ends confirmed the expression and the termini of the newly predicted snoRNAs. Our study especially expanded on the current knowledge of box H/ACA snoRNAs and snoRNA species targeting snRNAs. In this study, we demonstrated that the use of small RNA sequencing data can increase the complexity and the accuracy of snoRNA annotation.

Indexed as

Sequence Analysis, RNAArabidopsisBase SequenceBlotting, NorthernComputational BiologyGene Expression Regulation, PlantGenome, PlantMolecular Sequence DataRNA, PlantRNA, Small NucleolarRNA, PlantRNA, Small Nucleolar

Identifiers

PMID19357091
PMCPMC2685112
OpenAlexW2156617674

What OpenQuestion holds

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LicenceCC BY-NC
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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.