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Related Experiment Videos

A novel algorithm for computational identification of contaminated EST libraries.

Rotem Sorek1, Hershel M Safer

  • 1Compugen Ltd, 72 Pinchas Rosen Street, Tel Aviv 69512, Israel. rotem@compugen.co.il

Nucleic Acids Research
|February 1, 2003
PubMed
Summary

A new method identifies genomic DNA contamination in expressed sequence tag (EST) libraries, crucial for accurate human proteome and gene discovery. This work refines understanding of alternative splicing and new gene predictions.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • The Human Genome Project aimed to map all human genes, but genome sequences alone are insufficient for identifying all proteins.
  • Expressed sequence tags (ESTs) are vital for discovering new genes and alternative splicing events, but dbEST contains many artifacts.
  • Existing EST cleaning methods fail to detect certain types of contamination, leading to inaccurate gene and protein predictions.

Purpose of the Study:

  • To develop a novel method for identifying genomic DNA contamination and other artifacts in EST libraries.
  • To improve the accuracy of gene and alternative splicing predictions derived from EST data.
  • To provide a reliable dataset of contaminated ESTs for future bioinformatics applications.

Main Methods:

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  • Alignment of the complete set of human ESTs to the human genome.
  • Identification of highly contaminated EST libraries based on alignment patterns.
  • Analysis of contaminated ESTs to determine their nature (e.g., genomic DNA, pre-mRNA).

    • Main Results:

    • Identified 53 highly contaminated EST libraries.
    • Flagged 24,766 ESTs likely representing genomic DNA, pre-mRNA, or non-canonical splice events.
    • These contaminated sequences could have led to the erroneous prediction of 9,575 splice variants and 6,370 new genes.

    Conclusions:

    • Conclusions from EST analysis, particularly regarding alternative splicing and gene discovery, require re-evaluation.
    • The developed method and identified contaminated sequences are essential for accurate analysis of large EST datasets.
    • This work enhances the reliability of proteomic and genomic research relying on EST data.