Evidence map›Paper›PMID 23598527›Full record

ArticleRNA (New York, N.Y.)2013

Analysis and design of RNA sequencing experiments for identifying RNA editing and other single-nucleotide variants.

Jae-Hyung Lee, Jason K Ang, Xinshu Xiao

Abstract read
In one paragraph

Article in RNA (New York, N.Y.), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

45 citing papers in PubMed.

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  15. Modern Approaches for Transcriptome Analyses in Plants.Advances in experimental medicine and biology · 2021
    Article
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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

3 authors.

Jae-Hyung LeeDepartment of Integrative Biology and Physiology, Bioinformatics Interdepartmental Program, and the Molecular Biology Institute, University of California, Los Angeles, California 90095, USA.
Jason K Ang
Xinshu Xiao

Funding

Functional genetic variations in splicing regulationR01HG006264 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI XIAO, XINSHU GRACE · 2012 to 2016
$1.5M
Analysis of causal genetic variants in pre-mRNA processing using ENCODE dataU01HG007013 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI XIAO, XINSHU GRACE · 2012 to 2015
$1.1M
NHGRI NIH HHS R01 HG006264NHGRI NIH HHS R01HG006264NHGRI NIH HHS U01 HG007013
6 · The paper itself

Abstract

RNA-sequencing (RNA-Seq) technologies hold enormous promise for novel discoveries in genomics and transcriptomics. In the past year, a surge of reports has analyzed RNA-Seq data to gain a global view of the RNA editome. Opposing results have been presented, giving rise to extensive debate surrounding one of the first such studies in which a daunting list of all 12 types of RNA-DNA differences (RDDs) were identified. Although a consensus is forming that some of the initial "paradigm-shifting" results of this study may be questionable, recent reports on this topic differed in terms of the number and relative abundance of each type of RDD. Many outstanding issues exist, most importantly, the choice of bioinformatic approaches. Here we discuss the critical data analysis and experimental design issues of such studies to enable improved systematic investigation of the largely unexplored frontier of single-nucleotide variants in RNA.

Indexed as

Polymorphism, Single NucleotideResearch DesignRNA EditingAllelesBase SequenceDatabases, Nucleic AcidGene Expression ProfilingHumansMolecular Sequence AnnotationReproducibility of ResultsRNA Splice SitesSensitivity and SpecificitySequence AlignmentSequence Analysis, RNARNA Splice Sitesmapping errorread mappingRNA–DNA differenceRNA editingRNA-Seq

Identifiers

PMID23598527
PMCPMC3683905

What OpenQuestion holds

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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.