Evidence map›Paper›PMID 30239665›Full record

ArticleDatabase : the journal of biological databases and curation2018

Improved ontology for eukaryotic single-exon coding sequences in biological databases.

Roddy Jorquera, Carolina González, Philip Clausen, Bent Petersen, David S Holmes

Open access · goldAbstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.5field-weighted citation impact, top 36% 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

9 citing papers in PubMed, 14 citations in OpenAlex.

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  8. SinEx DB 2.0 update 2020: database for eukaryotic single-exon coding sequences.Database : the journal of biological databases and curation · 2021
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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

5 authors at 4 institutions in 3 countries.

Roddy JorqueraCenter for Bioinformatics and Genome Biology, Fundacion Ciencia & Vida, Avenida Zañartu 1482, Ñuñoa, Santiago, Chile.
Carolina GonzálezCenter for Bioinformatics and Genome Biology, Fundacion Ciencia & Vida, Avenida Zañartu 1482, Ñuñoa, Santiago, Chile.
Philip ClausenDepartment of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
Bent PetersenDepartment of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
David S HolmesCenter for Bioinformatics and Genome Biology, Fundacion Ciencia & Vida, Avenida Zañartu 1482, Ñuñoa, Santiago, Chile.
Fundación Ciencia and Vida · CLAIMST University · MYTechnical University of Denmark · DKUniversidad Andrés Bello · CL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Efficient extraction of knowledge from biological data requires the development of structured vocabularies to unambiguously define biological terms. This paper proposes descriptions and definitions to disambiguate the term 'single-exon gene'. Eukaryotic Single-Exon Genes (SEGs) have been defined as genes that do not have introns in their protein coding sequences. They have been studied not only to determine their origin and evolution but also because their expression has been linked to several types of human cancer and neurological/developmental disorders and many exhibit tissue-specific transcription. Unfortunately, the term 'SEGs' is rife with ambiguity, leading to biological misinterpretations. In the classic definition, no distinction is made between SEGs that harbor introns in their untranslated regions (UTRs) versus those without. This distinction is important to make because the presence of introns in UTRs affects transcriptional regulation and post-transcriptional processing of the mRNA. In addition, recent whole-transcriptome shotgun sequencing has led to the discovery of many examples of single-exon mRNAs that arise from alternative splicing of multi-exon genes, these single-exon isoforms are being confused with SEGs despite their clearly different origin. The increasing expansion of RNA-seq datasets makes it imperative to distinguish the different SEG types before annotation errors become indelibly propagated in biological databases. This paper develops a structured vocabulary for their disambiguation, allowing a major reassessment of their evolutionary trajectories, regulation, RNA processing and transport, and provides the opportunity to improve the detection of gene associations with disorders including cancers, neurological and developmental diseases.

Indexed as

Databases, GeneticGene OntologyBase SequenceEukaryotaExonsOpen Reading FramesProtein IsoformsProtein Isoforms

Identifiers

PMID30239665
PMCPMC6146118
OpenAlexW2891103854

What OpenQuestion holds

Textmetadata
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.