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Published on: December 3, 2010
The T7-primer is a source of experimental bias and introduces variability between microarray platforms
Ron M Kerkhoven1, Daoud Sie, Marja Nieuwland
1Central Microarray Facility, The Netherlands Cancer Institute, Amsterdam, The Netherlands. r.kerkhoven@nki.nl
Plos One
|April 24, 2008
Summary
Eberwine-like amplification adds nucleotide stretches to RNA, causing high microarray signals. This T7 primer bias hinders cross-platform data comparison.
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- Eberwine-like amplification is a common method for amplifying RNA.
- This process involves adding nucleotide sequences to RNA transcripts.
- Potential biases introduced during amplification can affect downstream analyses.
Purpose of the Study:
- To investigate the impact of nucleotide stretches added during Eberwine-like amplification on microarray signals.
- To identify the cause of aberrantly high signals observed in specific probes.
- To assess the implications of T7 primer bias for cross-platform data comparability.
Main Methods:
- Analysis of over six thousand microarray datasets.
- Comparison of signals from probes targeting specific nucleotide motifs against unaffected probes.
- Evaluation of amplification products derived from different T7 primer sequences.
Main Results:
- Eberwine-like amplification introduces 6-10 bp nucleotide stretches at the 5' end of RNA transcripts.
- Probes complementary to these stretches show up to 100-fold higher signals compared to unaffected probes.
- This artifact is specific to amplified RNA and not observed with total RNA.
- Significant T7 primer bias exists across different datasets and platforms.
Conclusions:
- The nucleotide stretches added during Eberwine-like amplification create a significant bias in microarray analysis.
- This bias can lead to overestimation of gene expression, particularly for probes targeting these added sequences.
- The observed T7 primer bias poses a challenge for comparing microarray data across different laboratories and platforms, necessitating careful data normalization and validation strategies.

