Literature DB >> 20569210

Utilization of AFFX spike-in control probes to monitor sample identity throughout Affymetrix GeneChip Array processing.

Michael Walter1, Anja Honegger, Rahel Schweizer, Sven Poths, Michael Bonin.   

Abstract

Microarrays evolved from a highly specialized technique into a standard molecular biology method that is widely used for whole-genome gene expression profiling. One of the most important aspects of this method is sample identity, that is, whether the expression profile recorded from an array actually derives from the indicated sample. Several potential steps in the protocol exist where a mix-up of samples may occur. With the increasing size of microarray studies, it is important to ensure that each expression profile is assigned to the correct sample. Errors at this level almost certainly lead to erroneous results and can even cause a complete failure of the microarray study. We developed a system that utilizes probes already present on commercially available Affymetrix arrays to unambiguously correlate the recorded expression profile with the input sample RNA. A set of eight spike-in controls were generated, which can be added to sample RNA in different combinations to generate an "on-chip identifier" that passes through the entire array processing protocol and results in a sample-specific hybridization pattern. This pattern can then be used to monitor whether each array was hybridized with the correct sample. The spike-in controls did not have any negative effect on RNA integrity or any detectable influence on the expression values of the remaining probes on the array; therefore, these controls represent an inexpensive and easily adaptable system to guarantee high-quality results from microarray experiments.

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Year:  2010        PMID: 20569210     DOI: 10.2144/000113421

Source DB:  PubMed          Journal:  Biotechniques        ISSN: 0736-6205            Impact factor:   1.993


  5 in total

Review 1.  Quality assurance of RNA expression profiling in clinical laboratories.

Authors:  Weihua Tang; Zhiyuan Hu; Hind Muallem; Margaret L Gulley
Journal:  J Mol Diagn       Date:  2011-10-20       Impact factor: 5.568

2.  Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study.

Authors:  Karl W Broman; Mark P Keller; Aimee Teo Broman; Christina Kendziorski; Brian S Yandell; Śaunak Sen; Alan D Attie
Journal:  G3 (Bethesda)       Date:  2015-08-19       Impact factor: 3.154

3.  Clinical implementation of RNA signatures for pharmacogenomic decision-making.

Authors:  Weihua Tang; Zhiyuan Hu; Hind Muallem; Margaret L Gulley
Journal:  Pharmgenomics Pers Med       Date:  2011-09-08

4.  Calling sample mix-ups in cancer population studies.

Authors:  Andy G Lynch; Suet-Feung Chin; Mark J Dunning; Carlos Caldas; Simon Tavaré; Christina Curtis
Journal:  PLoS One       Date:  2012-08-09       Impact factor: 3.240

5.  Sample tracking in microbiome community profiling assays using synthetic 16S rRNA gene spike-in controls.

Authors:  Dieter M Tourlousse; Akiko Ohashi; Yuji Sekiguchi
Journal:  Sci Rep       Date:  2018-06-14       Impact factor: 4.379

  5 in total

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