Evidence map›Paper›PMID 26110515›Full record

ArticleBMC genomics2015

Comparison of GENCODE and RefSeq gene annotation and the impact of reference geneset on variant effect prediction.

Adam Frankish, Barbara Uszczynska, Graham R S Ritchie, Jose M Gonzalez, Dmitri Pervouchine, Robert Petryszak, Jonathan M Mudge, Nuno Fonseca, Alvis Brazma, Roderic Guigo and 1 more

Abstract read
In one paragraph

Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

0numbers the graph read from it
0cells of the map it votes in
59citing 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

59 citing papers in PubMed.

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  13. Impact of genome build on RNA-seq interpretation and diagnostics.medRxiv : the preprint server for health sciences · 2024
    Article
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  20. LAFITE Reveals the Complexity of Transcript Isoforms in Subcellular Fractions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    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

11 authors.

Adam Frankish
Barbara Uszczynska
Graham R S Ritchie
Jose M Gonzalez
Dmitri Pervouchine
Robert Petryszak
Jonathan M Mudge
Nuno Fonseca
Alvis Brazma
Roderic Guigo
Jennifer Harrow

Funding

GENCODE Resource ProjectU41HG007234 · NHGRI · SANGER INSTITUTE · PI FLICEK, PAUL · 2013 to 2020
$20.3M
Landscape of transcription in human and mouseU54HG007004 · NHGRI · COLD SPRING HARBOR LABORATORY · PI GINGERAS, THOMAS RAYMOND · 2012 to 2017
$10.6M
EDAC: ENCODE Data Analysis CenterU41HG007000 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI WENG, ZHIPING · 2012 to 2016
$9.8M
NHGRI NIH HHS U41 HG007000NHGRI NIH HHS U41 HG007234NHGRI NIH HHS U54 HG007004Wellcome Trust WT098051
6 · The paper itself

Abstract

backgroundA vast amount of DNA variation is being identified by increasingly large-scale exome and genome sequencing projects. To be useful, variants require accurate functional annotation and a wide range of tools are available to this end. McCarthy et al recently demonstrated the large differences in prediction of loss-of-function (LoF) variation when RefSeq and Ensembl transcripts are used for annotation, highlighting the importance of the reference transcripts on which variant functional annotation is based.

resultsWe describe a detailed analysis of the similarities and differences between the gene and transcript annotation in the GENCODE and RefSeq genesets. We demonstrate that the GENCODE Comprehensive set is richer in alternative splicing, novel CDSs, novel exons and has higher genomic coverage than RefSeq, while the GENCODE Basic set is very similar to RefSeq. Using RNAseq data we show that exons and introns unique to one geneset are expressed at a similar level to those common to both. We present evidence that the differences in gene annotation lead to large differences in variant annotation where GENCODE and RefSeq are used as reference transcripts, although this is predominantly confined to non-coding transcripts and UTR sequence, with at most ~30% of LoF variants annotated discordantly. We also describe an investigation of dominant transcript expression, showing that it both supports the utility of the GENCODE Basic set in providing a smaller set of more highly expressed transcripts and provides a useful, biologically-relevant filter for further reducing the complexity of the transcriptome.

conclusionsThe reference transcripts selected for variant functional annotation do have a large effect on the outcome. The GENCODE Comprehensive transcripts contain more exons, have greater genomic coverage and capture many more variants than RefSeq in both genome and exome datasets, while the GENCODE Basic set shows a higher degree of concordance with RefSeq and has fewer unique features. We propose that the GENCODE Comprehensive set has great utility for the discovery of new variants with functional potential, while the GENCODE Basic set is more suitable for applications demanding less complex interpretation of functional variants.

Indexed as

Computational BiologyGenome, HumanMolecular Sequence AnnotationSoftwareAlternative SplicingDatabases, GeneticHumansProtein IsoformsTranscriptomeProtein Isoforms

Identifiers

PMID26110515
PMCPMC4502323

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LicenceCC BY
Read underepoch 390

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.