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Sequence biases in large scale gene expression profiling data.

Asim S Siddiqui1, Allen D Delaney, Angelique Schnerch

  • 1Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada.

Nucleic Acids Research
|July 15, 2006
PubMed
Summary
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This study introduces a statistical assay to measure G+C content bias in gene expression profiling. Long Serial Analysis of Gene Expression (LongSAGE) showed the least bias, while Signature Massively Parallel Signature Sequencing (MPSS) exhibited significant bias.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Gene expression profiling is crucial for understanding biological processes.
  • G+C content bias can significantly impact the accuracy of gene expression measurements.
  • Existing methods may not adequately address or quantify this bias.

Purpose of the Study:

  • To develop and apply a statistical assay for measuring G+C content sensitivity bias in gene expression experiments.
  • To compare the bias across five different gene expression profiling methods.
  • To assess the impact of G+C content bias on gene detection and expression level measurement.

Main Methods:

  • A simple, statistical assay was employed to quantify G+C content sensitivity bias.
  • Five gene expression profiling methods were analyzed: Affymetrix GeneChip, Long Serial Analysis of Gene Expression (LongSAGE), LongSAGELite, Classic Massively Parallel Signature Sequencing (MPSS), and Signature MPSS.

Related Experiment Videos

  • Systematic and random errors influencing G+C content sensitivity were investigated.

    • Main Results:

    • All analyzed methods exhibited varying degrees of G+C content sensitivity bias.
    • Long Serial Analysis of Gene Expression (LongSAGE) demonstrated the least bias.
    • Signature Massively Parallel Signature Sequencing (MPSS) showed a strong bias towards G+C rich tags, while Affymetrix data bias varied with processing methods (MAS 5.0, RMA, GC-RMA).
    • Bias in Affymetrix data predominantly affected genes with lower expression levels.
    • Despite larger library sampling, SAGE identified significantly more genes than MPSS.

    Conclusions:

    • G+C content bias is an inherent characteristic of gene expression profiling methods, influencing experimental outcomes.
    • The choice of gene expression profiling technology and data processing significantly affects the degree of G+C content bias.
    • Long Serial Analysis of Gene Expression (LongSAGE) emerges as a more robust method with minimal G+C content bias, crucial for accurate gene expression studies.