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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Related Experiment Video

Updated: May 10, 2026

A Method to Study the C924T Polymorphism of the Thromboxane A2 Receptor Gene
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Catching the genomic wave in oligonucleotide single-nucleotide polymorphism arrays by modeling sequence binding.

Yalu Wen1, Ming Li, Wenjiang J Fu

  • 1The Computational Genomics Lab, Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|June 15, 2013
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Summary

The genomic wave, a genome data artifact correlated with GC content, was studied to understand its mechanism. A new model successfully separated biological signals from this artifact, identifying GC content and fragment length as key sources.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • The genomic wave is a significant artifact in genome data, strongly linked to sequence GC content.
  • Existing statistical methods filter this artifact, but its underlying mechanism remains unstudied.
  • Understanding the genomic wave's sources is crucial for separating biological signals from artifacts in genomic studies.

Purpose of the Study:

  • To investigate the mechanism and sources of the genomic wave artifact in oligonucleotide single-nucleotide polymorphism (SNP) arrays.
  • To develop a method for separating biological signals from the genomic wave artifact.
  • To improve genome data quality for array design, modeling, and association studies.

Main Methods:

  • Developed a novel Probe Intensity Composite Representation (PICR) model to separate biological signals from array background.
  • The PICR model decomposes probe intensity into target concentrations, SNP-specific background, and measurement error.
  • Applied the PICR model to Affymetrix GeneChip 500K HapMap and Wellcome Trust Case-Control Study data.

Main Results:

  • The PICR model successfully captured the genomic wave within the SNP-specific background term.
  • Biological signals (allelic target concentrations) were successfully separated from the genomic wave artifact.
  • Identified GC content and fragment length (FL) as two primary sources of the genomic wave artifact.

Conclusions:

  • The PICR model effectively removes the genomic wave artifact from genome data.
  • The genomic wave is influenced not only by GC content but also by a nonlinear effect of fragment length.