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Related Experiment Video

Updated: Mar 1, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Improved sequence variant analysis strategy by automated false positive removal.

Wenzhou Li1, Jette Wypych1, Robert J Duff1

  • 1a Attribute Sciences, Amgen Inc. , Thousand Oaks , CA , USA.

Mabs
|June 8, 2017
PubMed
Summary
This summary is machine-generated.

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Sequence variant analysis (SVA) software identifies potential mutations in therapeutic proteins. A new Perl script significantly reduces false positives, saving weeks of manual verification time.

Area of Science:

  • Biotechnology
  • Proteomics
  • Bioinformatics

Background:

  • Sequence variant analysis (SVA) is crucial for therapeutic protein development, ensuring clone integrity and minimizing cell culture misincorporations.
  • Current methods for verifying sequence variants from mass spectrometry data are time-consuming, often requiring weeks of manual effort to filter false positives.

Purpose of the Study:

  • To develop and validate an automated method for reducing false positives in sequence variant analysis from mass spectrometry data.
  • To improve the efficiency of therapeutic protein characterization by minimizing manual data verification.

Main Methods:

  • Development of a Perl-based script to evaluate sequence variant identification hits.
  • Application of orthogonal criteria to filter false positives from PepFinder™ (MassAnalyzer) software output.
Keywords:
Algorithmautomationfalse positive removalsequence variant analysis

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  • Comparison of automated verification results with manual verification processes.

    • Main Results:

    • The Perl script reduced false positives in PepFinder™ output by approximately 4-fold.
    • Accuracy in detecting true positive identifications was maintained.
    • Manual verification time was reduced by over 70%.

    Conclusions:

    • Automated filtering of sequence variant analysis data using a Perl script significantly enhances efficiency and reduces false positives.
    • This approach streamlines the development of therapeutic proteins by accelerating clone verification.
    • The developed script offers a substantial time-saving alternative to manual data review.