Evidence map›Paper›PMID 26381377›Full record

ArticleGenome biology2015

Tools and best practices for data processing in allelic expression analysis.

Stephane E Castel, Ami Levy-Moonshine, Pejman Mohammadi, Eric Banks, Tuuli Lappalainen

Abstract read
In one paragraph

Article in Genome biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 248 papers.

0numbers the graph read from it
0cells of the map it votes in
248citing 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

248 citing papers in PubMed.

  1. Trial
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  5. Review
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  9. Article
  10. Article
  11. Article
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  13. Allele specific expression in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

188 more citing papers are in PubMed but not listed here.

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.

Stephane E CastelNew York Genome Center, New York, NY, USA. scastel@nygenome.org.
Ami Levy-MoonshineBroad Institute, Cambridge, MA, USA.
Pejman MohammadiNew York Genome Center, New York, NY, USA.
Eric BanksBroad Institute, Cambridge, MA, USA.
Tuuli LappalainenNew York Genome Center, New York, NY, USA. tlappalainen@nygenome.org.

Funding

Informatics Tools for High-Throughput Sequences Data AnalysisU01HG006569 · NHGRI · BROAD INSTITUTE, INC. · PI BANKS, ERIC · 2012 to 2015
$3.9M
LIMBIC &MEDULLARY MECH. IN COCAINE-RELATED SUDDEN DEATHR01DA006227 · NIDA · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI MASH, DEBORAH C. · 1990 to 2012
$3.7M
Methods for high-resolution analysis of genetic effects on gene expressionR01MH101814 · NIMH · UNIVERSITY OF GENEVA · PI BUSTAMANTE, CARLOS DANIEL, DERMITZAKIS, EMMANOUIL · 2013 to 2016
$2.3M
Statistical analysis of gene expression quantitative trait loci (eQTL)R01MH090951 · NIMH · UNIVERSITY OF CHICAGO · PI PRITCHARD, JONATHAN K · 2010 to 2012
$873k
Methods for high-resolution analysis of genetic effects on gene expressionR01MH090941 · NIMH · UNIVERSITY OF GENEVA · PI DERMITZAKIS, EMMANOUIL, GUIGO, RODERIC · 2010 to 2012
$863k
FACILITATING GTEx, DISEASE, AND GxE ANALYSES VIA FAST EXPRESSION (e)QTL MAPPINGR01MH090936 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI NOBEL, ANDREW B, RUSYN, IVAN · 2010 to 2012
$847k
Using the Transcriptome for SNP and Gene AnnotationR01MH090937 · NIMH · UNIVERSITY OF CHICAGO · PI COX, NANCY J, NICOLAE, DAN LIVIU · 2010 to 2012
$825k
Epistatic and Cross Tissue Analysis for Human Gene Expression TraitsR01MH090948 · NIMH · HARVARD UNIVERSITY · PI LIU, JUN S · 2010 to 2011
$588k
CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001ENHGRI NIH HHS 5U01HG006569NHGRI NIH HHS U01 HG006569NHLBI NIH HHS HHSN268201000029CNIDA NIH HHS R01 DA006227NIMH NIH HHS 3R01MH101814-02S1NIMH NIH HHS R01 MH090936NIMH NIH HHS R01 MH090937NIMH NIH HHS R01 MH090941NIMH NIH HHS R01 MH090948NIMH NIH HHS R01 MH090951NIMH NIH HHS R01 MH101814
6 · The paper itself

Abstract

Allelic expression analysis has become important for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. We analyze the properties of allelic expression read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting such errors, show that our quality control measures improve the detection of relevant allelic expression, and introduce tools for the high-throughput production of allelic expression data from RNA-sequencing data.

Indexed as

AllelesSoftwareCell LineData Interpretation, StatisticalGene ExpressionGene Expression ProfilingGenotyping TechniquesHumansSequence Analysis, RNA

Identifiers

PMID26381377
PMCPMC4574606

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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.