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Optimal cDNA microarray design using expressed sequence tags for organisms with limited genomic information.

Yian A Chen1, David J McKillen, Shuyuan Wu

  • 1Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, Charleston, SC, USA. chenya@musc.edu

BMC Bioinformatics
|December 9, 2004
PubMed
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This study presents a new method for selecting cDNA microarray probes from expressed sequence tags (ESTs) to reduce redundancy and cross-hybridization. The approach optimizes microarray design for organisms with limited genomic data, aiding environmental and host-parasite interaction studies.

Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Expression microarrays are vital for studying environmental responses and host-parasite interactions.
  • Selecting cDNA microarray probes from expressed sequence tags (ESTs) is challenging due to redundancy and cross-hybridization, especially in organisms with limited genomic data.

Purpose of the Study:

  • To develop a general tool for cDNA microarray probe selection in organisms with limited genomic information.
  • To minimize sequence redundancy and potential cross-hybridization while ensuring functional representation of probes.

Main Methods:

  • Utilized sequence similarity (E-value) as a measure of dissimilarity for hierarchical cluster analyses to reduce redundancy.
  • Developed a sequence diversity index (SDI) within a sequence diversity plot (SDP) to determine the optimal number of probes.

Related Experiment Videos

  • Selected centroid ESTs as microarray probes, representing the most similar sequence within each cluster.

    • Main Results:

    • Demonstrated the algorithm's effectiveness using expressed sequence tags from Atlantic white shrimp (Litopenaeus setiferus).
    • Quantified the functional representativeness of selected probes using Gene Ontology (GO) annotations.
    • Showcased the reduction of sequence redundancy and potential for cross-hybridization.

    Conclusions:

    • Hierarchical clustering of ESTs provides an optimal cDNA microarray design for organisms with limited genomic information.
    • Average linkage is effective for biomarker discovery, while single linkage is suitable for identifying physiological mechanisms.
    • The developed procedure is applicable to single- and multiple-species microarrays across various organisms.