Evidence map›Paper›PMID 20520764›Full record

ArticlePloS one2010

Analysis of antisense expression by whole genome tiling microarrays and siRNAs suggests mis-annotation of Arabidopsis orphan protein-coding genes.

Casey R Richardson, Qing-Jun Luo, Viktoria Gontcharova, Ying-Wen Jiang, Manoj Samanta, Eunseog Youn, Christopher D Rock

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.2field-weighted citation impact, top 17% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Protein structure annotation resources.Methods in molecular biology (Clifton, N.J.) · 2015
    Review
  2. Article
  3. Genome-wide landscape of polyadenylation in Arabidopsis provides evidence for extensive alternative polyadenylation.Proceedings of the National Academy of Sciences of the United States of America · 2011
    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

7 authors at 1 institution in 1 country.

Casey R RichardsonDepartment of Biological Sciences, Texas Tech University, Lubbock, Texas, USA.
Qing-Jun Luo
Viktoria Gontcharova
Ying-Wen Jiang
Manoj Samanta
Eunseog Youn
Christopher D Rock
Texas Tech University · US

Funding

Computational analysis of causality between miRNAs and antisense transcriptionR21GM077245 · NIGMS · TEXAS TECH UNIVERSITY · PI ROCK, CHRISTOPHER DALE · 2007 to 2009
$519k
NIGMS NIH HHS R21 GM077245NIGMS NIH HHS R21GM077245
6 · The paper itself

Abstract

backgroundMicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20-22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery. PRINCIPAL

findingsWe explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis 'orphan' hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the "ancient" (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for "new" rapidly-evolving MIRNA genes.

conclusionsAntisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation.

Indexed as

Gene Expression Regulation, PlantAlgorithmsArabidopsisArabidopsis ProteinsDatabases, GeneticGene Expression ProfilingGenes, PlantOligonucleotide Array Sequence AnalysisOpen Reading FramesRNA, AntisenseRNA, PlantRNA, Small InterferingSequence Homology, Nucleic AcidArabidopsis ProteinsRNA, AntisenseRNA, PlantRNA, Small Interfering

Identifiers

PMID20520764
PMCPMC2877095
OpenAlexW2016627309

What OpenQuestion holds

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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.