Literature DB >> 17332024

Microarray blob-defect removal improves array analysis.

Jun S Song1, Kaveh Maghsoudi, Wei Li, Edward Fox, John Quackenbush, X Shirley Liu.   

Abstract

MOTIVATION: New generation Affymetrix oligonucleotide microarrays often have blob-like image defects that will require investigators to either repeat their hybridization assays or analyze their data with the defects left in place. We investigated the effect of analyzing a spike-in experiment on Affymetrix ENCODE tiling arrays in the presence of simulated blobs covering between 1 and 9% of the array area. Using two different ChIP-chip tiling array analysis programs (Affymetrix tiling array software, TAS, and model-based analysis of tiling arrays, MAT), we found that even the smallest blob defects significantly decreased the sensitivity and increased the false discovery rate (FDR) of the spike-in target prediction.
RESULTS: We introduced a new software tool, the microarray blob remover (MBR), which allows rapid visualization, detection and removal of various blob defects from the .CEL files of different types of Affymetrix microarrays. It is shown that using MBR significantly improves the sensitivity and FDR of a tiling array analysis compared to leaving the affected probes in the analysis. AVAILABILITY: The MBR software and the sample array .CEL files used in this article are available at: http://liulab.dfci.harvard.edu/Software/MBR/MBR.htm

Entities:  

Mesh:

Year:  2007        PMID: 17332024     DOI: 10.1093/bioinformatics/btm043

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  10 in total

Review 1.  Cardiovascular genomics: a biomarker identification pipeline.

Authors:  John H Phan; Chang F Quo; May Dongmei Wang
Journal:  IEEE Trans Inf Technol Biomed       Date:  2012-05-16

2.  Spatial normalization improves the quality of genotype calling for Affymetrix SNP 6.0 arrays.

Authors:  High Seng Chai; Terry M Therneau; Kent R Bailey; Jean-Pierre A Kocher
Journal:  BMC Bioinformatics       Date:  2010-06-29       Impact factor: 3.169

3.  A review of statistical methods for preprocessing oligonucleotide microarrays.

Authors:  Zhijin Wu
Journal:  Stat Methods Med Res       Date:  2009-12       Impact factor: 3.021

4.  Detection and correction of probe-level artefacts on microarrays.

Authors:  Tobias Petri; Evi Berchtold; Ralf Zimmer; Caroline C Friedel
Journal:  BMC Bioinformatics       Date:  2012-05-30       Impact factor: 3.169

5.  BASH: a tool for managing BeadArray spatial artefacts.

Authors:  J M Cairns; M J Dunning; M E Ritchie; R Russell; A G Lynch
Journal:  Bioinformatics       Date:  2008-10-25       Impact factor: 6.937

6.  An imputation approach for oligonucleotide microarrays.

Authors:  Ming Li; Yalu Wen; Qing Lu; Wenjiang J Fu
Journal:  PLoS One       Date:  2013-03-07       Impact factor: 3.240

7.  An integrated software system for analyzing ChIP-chip and ChIP-seq data.

Authors:  Hongkai Ji; Hui Jiang; Wenxiu Ma; David S Johnson; Richard M Myers; Wing H Wong
Journal:  Nat Biotechnol       Date:  2008-11-02       Impact factor: 54.908

Review 8.  Getting started in tiling microarray analysis.

Authors:  X Shirley Liu
Journal:  PLoS Comput Biol       Date:  2007-10       Impact factor: 4.475

Review 9.  Microarray experiments and factors which affect their reliability.

Authors:  Roman Jaksik; Marta Iwanaszko; Joanna Rzeszowska-Wolny; Marek Kimmel
Journal:  Biol Direct       Date:  2015-09-03       Impact factor: 4.540

Review 10.  Comparing whole genomes using DNA microarrays.

Authors:  David Gresham; Maitreya J Dunham; David Botstein
Journal:  Nat Rev Genet       Date:  2008-04       Impact factor: 53.242

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.