Evidence map›Paper›PMID 23968248›Full record

ArticleBMC genomics2013

Whole transcriptome RNA-Seq allelic expression in human brain.

Ryan M Smith, Amy Webb, Audrey C Papp, Leslie C Newman, Samuel K Handelman, Adam Suhy, Roshan Mascarenhas, John Oberdick, Wolfgang Sadee

Abstract read
In one paragraph

Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

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  11. The Role ofGenes · 2019
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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

9 authors.

Ryan M SmithDepartment of Pharmacology, Program in Pharmacogenomics; College of Medicine, The Ohio State University Wexner Medical Center, 5184A Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA. Ryan.Smith2@osumc.edu
Amy Webb
Audrey C Papp
Leslie C Newman
Samuel K Handelman
Adam Suhy
Roshan Mascarenhas
John Oberdick
Wolfgang Sadee

Funding

PHARMACOGENETICS OF MEMBRANE TRANSPORTERSU01GM061390 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GIACOMINI, KATHLEEN M · 2000 to 2009
$29.2M
The Ohio State University Center for clinical and Translational ScienceUL1TR001070 · NCATS · OHIO STATE UNIVERSITY · PI JACKSON, REBECCA D · 2013 to 2017
$22.6M
Pharmacogenomics of Membrane TransportersU19GM061390 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WITTE, JOHN S. · 2010 to 2014
$16.3M
Expression Genetics in Drug TherapyU01GM092655 · NIGMS · OHIO STATE UNIVERSITY · PI SADEE, WOLFGANG · 2010 to 2014
$8.6M
NCATS NIH HHS UL1 TR001070NIGMS NIH HHS U01 GM061390NIGMS NIH HHS U01GM092655NIGMS NIH HHS U19 GM061390
6 · The paper itself

Abstract

backgroundMeasuring allelic RNA expression ratios is a powerful approach for detecting cis-acting regulatory variants, RNA editing, loss of heterozygosity in cancer, copy number variation, and allele-specific epigenetic gene silencing. Whole transcriptome RNA sequencing (RNA-Seq) has emerged as a genome-wide tool for identifying allelic expression imbalance (AEI), but numerous factors bias allelic RNA ratio measurements. Here, we compare RNA-Seq allelic ratios measured in nine different human brain regions with a highly sensitive and accurate SNaPshot measure of allelic RNA ratios, identifying factors affecting reliable allelic ratio measurement. Accounting for these factors, we subsequently surveyed the variability of RNA editing across brain regions and across individuals.

resultsWe find that RNA-Seq allelic ratios from standard alignment methods correlate poorly with SNaPshot, but applying alternative alignment strategies and correcting for observed biases significantly improves correlations. Deploying these methods on a transcriptome-wide basis in nine brain regions from a single individual, we identified genes with AEI across all regions (SLC1A3, NHP2L1) and many others with region-specific AEI. In dorsolateral prefrontal cortex (DLPFC) tissues from 14 individuals, we found evidence for frequent regulatory variants affecting RNA expression in tens to hundreds of genes, depending on stringency for assigning AEI. Further, we find that the extent and variability of RNA editing is similar across brain regions and across individuals.

conclusionsThese results identify critical factors affecting allelic ratios measured by RNA-Seq and provide a foundation for using this technology to screen allelic RNA expression on a transcriptome-wide basis. Using this technology as a screening tool reveals tens to hundreds of genes harboring frequent functional variants affecting RNA expression in the human brain. With respect to RNA editing, the similarities within and between individuals leads us to conclude that this post-transcriptional process is under heavy regulatory influence to maintain an optimal degree of editing for normal biological function.

Indexed as

AllelesGene Expression ProfilingSequence Analysis, RNAAdultBrainDNA, ComplementaryHumansMaleMiddle AgedPolymorphism, GeneticPrefrontal CortexRNARNA EditingYoung AdultDNA, ComplementaryRNA

Identifiers

PMID23968248
PMCPMC3765493

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LicenceCC BY
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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.