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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Counting unique molecular identifiers in sequencing using a multi-type branching process with immigration.

Serik Sagitov1, Anders Ståhlberg2

  • 1Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Sweden.

Journal of Theoretical Biology
|November 21, 2022
PubMed
Summary

This study models PCR barcoding errors using a branching process. A key finding shows UMI cluster sizes follow a predictable 2^-m pattern, aiding rare variant detection in sequencing.

Keywords:
Growing immigrationPCR amplification ratePCR branching processSequencingTree-bookkeepingUnique Molecular Identifier

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Detecting rare DNA variants like tumor DNA is difficult due to sequencing errors.
  • Unique Molecular Identifiers (UMIs) in next-generation sequencing library construction help correct PCR-induced errors.

Purpose of the Study:

  • To model the impact of PCR cycles and amplification rates on UMI cluster formation.
  • To describe the distribution of UMI cluster sizes in DNA sequencing library preparation.

Main Methods:

  • A branching process with growing immigration was developed to model PCR barcoding over 't' cycles.
  • The model considers five different amplification rates for various molecule types.
  • Analysis focused on the number of UMI clusters (Ct) and the number of clusters of size 'm' (Ct(m)).

Main Results:

  • A consistent asymptotic pattern was identified for moderately large 't'.
  • The expected ratio of UMI clusters of size 'm' to total clusters approximates 2^-m (E(Ct(m))/E(Ct)≈2^-m).
  • This pattern holds true irrespective of the specific amplification rates used.

Conclusions:

  • The developed model provides insights into UMI cluster size distributions during PCR barcoding.
  • Understanding these patterns helps optimize sequencing protocols and improve the accuracy of rare variant detection.
  • This research aids researchers in interpreting sequencing data more effectively.