Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare

Mutation Resistant Target Prediction Algorithm in PCR Based Diagnostic Applications

Author(s): Osman Doluca and Murat Sayan *

Pp: 272-283 (12)

DOI: 10.2174/9781681088716121010017

* (Excluding Mailing and Handling)

Abstract

Highly mutable organisms often challenge primer design for diagnostic PCR kit manufacturers due to new mutations occurring in hybridization sites. Novel variants may require reconsideration of the existing PCR primers and even result in misdiagnosis. While conserved sequences are often the main target of primer design algorithms, they often do not consider possible new mutants. We represent a generalizable algorithm for filtration of the sequence to identify conserved sequences and the less likely regions to mutate. Primers selected from the filtered sequences are expected to target regions with lower mutation rates and consecutively act indifferent to more variants of a target pathogen, providing long-lasting primers and less frequent primer redesign.


Keywords: Molecular Evolution, Primer Picking Algorithms, Primer Selection, Sequence Conservation.

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