Evidence map›Paper›PMID 23431329›Full record

ArticleRNA (New York, N.Y.)2013

Incorporating the human gene annotations in different databases significantly improved transcriptomic and genetic analyses.

Geng Chen, Charles Wang, Leming Shi, Xiongfei Qu, Jiwei Chen, Jianmin Yang, Caiping Shi, Long Chen, Peiying Zhou, Baitang Ning and 2 more

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 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

14 citing papers in PubMed.

  1. Article
  2. Impact of genome build on RNA-seq interpretation and diagnostics.medRxiv : the preprint server for health sciences · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Exploring Additional Valuable Information From Single-Cell RNA-Seq Data.Frontiers in cell and developmental biology · 2020
    Review
  8. Review
  9. Article
  10. Article
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  12. 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

12 authors.

Geng ChenEast China Normal University, Shanghai, China.
Charles Wang
Leming Shi
Xiongfei Qu
Jiwei Chen
Jianmin Yang
Caiping Shi
Long Chen
Peiying Zhou
Baitang Ning
Weida Tong
Tieliu Shi

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human gene annotation is crucial for conducting transcriptomic and genetic studies; however, the impacts of human gene annotations in diverse databases on related studies have been less evaluated. To enable full use of various human annotation resources and better understand the human transcriptome, here we systematically compare the human annotations present in RefSeq, Ensembl (GENCODE), and AceView on diverse transcriptomic and genetic analyses. We found that the human gene annotations in the three databases are far from complete. Although Ensembl and AceView annotated more genes than RefSeq, more than 15,800 genes from Ensembl (or AceView) are within the intergenic and intronic regions of AceView (or Ensembl) annotation. The human transcriptome annotations in RefSeq, Ensembl, and AceView had distinct effects on short-read mapping, gene and isoform expression profiling, and differential expression calling. Furthermore, our findings indicate that the integrated annotation of these databases can obtain a more complete gene set and significantly enhance those transcriptomic analyses. We also observed that many more known SNPs were located within genes annotated in Ensembl and AceView than in RefSeq. In particular, 1033 of 3041 trait/disease-associated SNPs involved in about 200 human traits/diseases that were previously reported to be in RefSeq intergenic regions could be relocated within Ensembl and AceView genes. Our findings illustrate that a more complete transcriptome generated by incorporating human gene annotations in diverse databases can strikingly improve the overall results of transcriptomic and genetic studies.

Indexed as

Databases, GeneticGenome, HumanMolecular Sequence AnnotationTranscriptomeCell LineChromosomes, HumanDiseaseGene Expression ProfilingHumansOrgan SpecificityPolymorphism, Single NucleotideReference Standards

Identifiers

PMID23431329
PMCPMC3677258

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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.