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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Related Experiment Video

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Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Detecting artifacts on SNP chips.

Maurizio Pellegrino, Mayte Suárez-Fariñas, Marcelo O Magnasco

    Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
    |October 20, 2007
    PubMed
    Summary
    This summary is machine-generated.

    A new tool, Harshlight, detects and masks blemishes in HDONA microarray chips. This image analysis method improves data quality for SNP microarray applications.

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    Area of Science:

    • Bioinformatics
    • Image Analysis
    • Genomics

    Background:

    • Microarray imaging is susceptible to artifacts like dust, debris, and uneven coatings.
    • These blemishes can negatively impact the accuracy of subsequent data analysis.
    • Existing methods may not adequately address the unique challenges of microarray defect detection.

    Purpose of the Study:

    • To develop and validate a novel tool, Harshlight, for identifying and masking blemishes in HDONA microarray chips.
    • To provide a robust solution for improving the quality of microarray data.
    • To demonstrate the tool's adaptability across different microarray technologies.

    Main Methods:

    • Harshlight employs a combination of statistical analysis and image processing techniques.
    • The tool is designed with user-tunable parameters for flexible application.
    • The methodology focuses on robust defect identification and masking.

    Main Results:

    • Harshlight successfully detects and masks various blemishes in HDONA microarray scans.
    • The tool's performance was validated on SNP (Single Nucleotide Polymorphism) microarrays.
    • Demonstrated wide applicability across different microarray chip technologies.

    Conclusions:

    • Harshlight offers an effective solution for mitigating image artifacts in microarray analysis.
    • The tool enhances the reliability of data derived from SNP microarrays.
    • Its adaptable nature makes it a valuable asset for researchers using diverse microarray platforms.