Evidence map›Paper›PMID 26223252›Full record

ArticleBMC bioinformatics2015

Equalizer reduces SNP bias in Affymetrix microarrays.

David Quigley

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.6field-weighted citation impact, top 31% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

1 author at 1 institution in 1 country.

David QuigleyHelen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, 94158, USA. David.Quigley@ucsf.edu.
UCSF Helen Diller Family Comprehensive Cancer Center · US

Funding

Identification of epithelial tumor susceptiility genesU01CA084244 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BALMAIN, ALLAN · 1999 to 2013
$13.3M
Science Leadership and IntegrationU01CA141455 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BALMAIN, ALLAN · 2009 to 2013
$3.2M
NCI NIH HHS CA084244-15NCI NIH HHS CA141455-01NCI NIH HHS U01 CA084244NCI NIH HHS U01 CA141455
6 · The paper itself

Abstract

backgroundGene expression microarrays measure the levels of messenger ribonucleic acid (mRNA) in a sample using probe sequences that hybridize with transcribed regions. These probe sequences are designed using a reference genome for the relevant species. However, most model organisms and all humans have genomes that deviate from their reference. These variations, which include single nucleotide polymorphisms, insertions of additional nucleotides, and nucleotide deletions, can affect the microarray's performance. Genetic experiments comparing individuals bearing different population-associated single nucleotide polymorphisms that intersect microarray probes are therefore subject to systemic bias, as the reduction in binding efficiency due to a technical artifact is confounded with genetic differences between parental strains. This problem has been recognized for some time, and earlier methods of compensation have attempted to identify probes affected by genome variants using statistical models. These methods may require replicate microarray measurement of gene expression in the relevant tissue in inbred parental samples, which are not always available in model organisms and are never available in humans.

resultsBy using sequence information for the genomes of organisms under investigation, potentially problematic probes can now be identified a priori. However, there is no published software tool that makes it easy to eliminate these probes from an annotation. I present equalizer, a software package that uses genome variant data to modify annotation files for the commonly used Affymetrix IVT and Gene/Exon platforms. These files can be used by any microarray normalization method for subsequent analysis. I demonstrate how use of equalizer on experiments mapping germline influence on gene expression in a genetic cross between two divergent mouse species and in human samples significantly reduces probe hybridization-induced bias, reducing false positive and false negative findings.

conclusionsThe equalizer package reduces probe hybridization bias from experiments performed on the Affymetrix microarray platform, allowing accurate assessment of germline influence on gene expression.

Indexed as

Polymorphism, Single NucleotideSoftwareAnimalsCell LineFemaleGene Expression ProfilingGenomeHumansMammary Glands, AnimalMiceNucleic Acid HybridizationOligonucleotide Array Sequence AnalysisQuantitative Trait LociRNA, MessengerSkinRNA, Messenger

Identifiers

PMID26223252
PMCPMC4518889
OpenAlexW1935804066

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.