Evidence map›Paper›PMID 31589315›Full record

ArticleBioinformatics (Oxford, England)2020

ReQTL: identifying correlations between expressed SNVs and gene expression using RNA-sequencing data.

Liam F Spurr, Nawaf Alomran, Pavlos Bousounis, Dacian Reece-Stremtan, N M Prashant, Hongyu Liu, Piotr Słowiński, Muzi Li, Qianqian Zhang, Justin Sein and 4 more

Open access · hybridAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 32% 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

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. GeTallele: A Method for Analysis of DNA and RNA Allele Frequency Distributions.Frontiers in bioengineering and biotechnology · 2020
    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

14 authors at 4 institutions in 2 countries.

Liam F SpurrDepartment of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
Nawaf AlomranBiochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center.
Pavlos BousounisBiochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center.
Dacian Reece-StremtanComputer Applications Support Services, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20037, USA.
N M PrashantBiochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center.
Hongyu LiuBiochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center.
Piotr SłowińskiDepartment of Mathematics & Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK.
Muzi LiBiochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center.
Qianqian ZhangDepartment of Biochemistry and Molecular Medicine.
Justin SeinBiochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center.
Gabriel AsherBiochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center.
Keith A CrandallComputational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA.
Krasimira Tsaneva-AtanasovaDepartment of Mathematics & Living Systems Institute, University of Exeter, Exeter EX4 4QD, UK.
Anelia HorvathBiochemistry and Molecular Medicine, McCormick Genomics and Proteomics Center.
McCormick (United States) · USGeorge Washington University · USGenomics (United Kingdom) · GBUniversity of Exeter · GB

Funding

Clinical and Translational Science Institute at Children's NationalUL1TR000075 · NCATS · CHILDREN'S RESEARCH INSTITUTE · PI GUAY-WOODFORD, LISA MARIE · 2012 to 2015
$13.7M
NCATS NIH HHS UL1 TR000075
6 · The paper itself

Abstract

motivationBy testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets.

resultsWe introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci. AVAILABILITY AND IMPLEMENTATION: A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

RNASoftwareHumansNucleotidesPolymorphism, Single NucleotideQuantitative Trait LociSequence Analysis, RNANucleotidesRNA

Identifiers

PMID31589315
PMCPMC7058180
OpenAlexW2980141308

What OpenQuestion holds

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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.