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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
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A white-box approach to microarray probe response characterization: the BaFL pipeline.

Kevin J Thompson1, Hrishikesh Deshmukh, Jeffrey L Solka

  • 1Computer Science Dept, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. kthom110@uncc.edu

BMC Bioinformatics
|December 31, 2009
PubMed
Summary

This study introduces a novel data cleansing protocol for microarrays, significantly improving gene expression measurement consistency and reliability across experiments by filtering out problematic probes. The method enhances statistical power for identifying significant biological differences.

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Performing Custom MicroRNA Microarray Experiments
07:04

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Published on: October 28, 2011

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Microarray accuracy relies on precise probe design for unique target matching and homogeneous duplex stability.
  • Non-ideal probes, including cross-hybridizing or structurally flawed ones, and platform effects compromise accurate target concentration measurement.
  • Current data cleansing pipelines often use general statistical tests, failing to specifically address constraints affecting probe performance.

Purpose of the Study:

  • To develop and validate a comprehensive 'white box' probe filtering and intensity transformation protocol for microarrays.
  • To manage and consistently apply biologically applied filter levels (BaFL) affecting probe performance.
  • To assess the impact of comprehensively excluding probes affected by known factors on inter-experiment target behavior consistency.

Main Methods:

  • Developed a 'white box' protocol incorporating probe-specific effects (SNPs, cross-hybridization, low heteroduplex affinity) and platform effects (scanner sensitivity, sample batches).
  • Tested the protocol on Affymetrix human GeneChip HG-U95Av2 data from two independent lung adenocarcinoma studies.
  • Included simple statistical tests for identifying unresolved biological factors contributing to sample variability.

Main Results:

  • The filtering protocol markedly improved the consistency and reliability of gene expression measurements.
  • Demonstrated improved inter-experiment target behavior consistency after comprehensive probe exclusion.
  • Showcased reproducible estimates of relative gene expression translatable across datasets.

Conclusions:

  • The data cleansing protocol enables reproducible and credible cross-experiment comparisons of gene expression profiles.
  • Provided evidence supporting the removal of specific probe classes and outlying samples.
  • The method enhances statistical power for discriminating significant differences between sample classes.