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  'maskBAD' – a package to detect and remove Affymetrix probes with binding affinity differences

Dannemann, M., Lachmann, M., & Lorenc, A. (2012). 'maskBAD' – a package to detect and remove Affymetrix probes with binding affinity differences. BMC Bioinformatics, 13: 56. doi:10.1186/1471-2105-13-56.

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Dannemann, Michael1, Author           
Lachmann, Michael1, Author           
Lorenc, Anna2, Author           
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1Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society, ou_1497672              
2Department Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445635              

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 Abstract: Background: Hybridization differences caused by target sequence differences can be a confounding factor in analyzing gene expression on microarrays, lead to false positives and reduce power to detect real expression differences. We prepared an R Bioconductor compatible package to detect, characterize and remove such probes in Affymetrix 3’IVT and exon-based arrays on the basis of correlation of signal intensities from probes within probe sets. Results: Using completely mouse genomes we determined type 1 (false negatives) and type 2 (false positives) errors with high accuracy and we show that our method routinely outperforms previous methods. When detecting 76.2% of known SNP/indels in mouse expression data, we obtain at most 5.5% false positives. At the same level of false positives, best previous method detected 72.6%. We also show that probes with differing binding affinity both hinder differential expression detection and introduce artifacts in cancer-healthy tissue comparison. Conclusions: Detection and removal of such probes should be a routine step in Affymetrix data preprocessing. We prepared a user friendly R package, compatible with Bioconductor, that allows the filtering and improving of data from Affymetrix microarrays experiments

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Language(s): eng - English
 Dates: 2011-12-042012-03-162012-04-16
 Publication Status: Published online
 Pages: 7 S.
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1186/1471-2105-13-56
Other: 2954/S 39299
 Degree: -

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Title: BMC Bioinformatics
Source Genre: Journal
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Publ. Info: BioMed Central
Pages: - Volume / Issue: 13 Sequence Number: 56 Start / End Page: - Identifier: ISSN: 1471-2105 (online)
CoNE: https://pure.mpg.de/cone/journals/resource/111000136905000