Optimization of Combinatory Nicking Endonucleases for Accurate Identification of Nucleic Acids in Low Abundance

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

1 Scopus Citations
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Original languageEnglish
Pages (from-to)411-417
Journal / PublicationJournal of laboratory automation
Issue number4
StatePublished - 25 Aug 2015


Nucleic acid biomarkers embody inherent importance for differentiating disease-causing organisms or environmental pathogens. Identifying unknown nucleic acids in low abundance remains extremely challenging. Previously, we reported a method to identify complementary DNA (cDNA) molecules based on sequence-specific topographical labels measured by atomic force microscopy (AFM). However, the accuracy is limited because only one type of nicking endonuclease was used as the labeling agent. Here we investigate how accuracy is improved using multiple types of nicking endonucleases in combinations. The numerical experiments created cDNA molecules incorporating measurement error or labeling defects, which were later compared with the 29,563 human messenger RNA (mRNA) transcript database with ideal labels. After comparison, the unknown cDNA molecule was identified as the transcript with the highest matching score. Thus, the accuracy was determined by the rate of true positives. We found that the accuracy is positively proportional to the label number. Compared with cases using single nicking endonuclease, which has an average accuracy of 51.2% ± 34.4%, the average accuracy was improved to 97.1% ± 5.6% using an optimized combination of NtBsmAI + NtBstNBI + NtAlwI. This improved accuracy is applicable to more than 85% of human mRNA transcripts. Together, our study suggests an optimization strategy for identifying nucleic acids in low abundance using the AFM-based method, with implications for diseases diagnosis, pathogen identification, and forensics at the single molecule level.

Research Area(s)

  • AFM, bioinformatics, nucleic acids, optimization