Literature DB >> 29652558

A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination.

Mridu Sinha1,2,3, Hannah Mack1,2,3, Todd P Coleman1,3, Stephanie I Fraley1,2,3.   

Abstract

DNA melting analysis provides a rapid method for genotyping a target amplicon directly after PCR amplification. To transform melt genotyping into a broad-based profiling approach for heterogeneous samples, we previously proposed the integration of universal PCR and melt analysis with digital PCR. Here, we advanced this concept by developing a high-resolution digital melt platform with precise thermal control to accomplish reliable, high-throughput heat ramping of microfluidic chip digital PCR reactions. Using synthetic DNA oligos with defined melting temperatures, we characterized sources of melting variability and minimized run-to-run variations. Within-run comparisons throughout a 20,000-reaction chip revealed that high-melting-temperature sequences were significantly less prone to melt variation. Further optimization using bacterial 16S amplicons revealed a strong dependence of the number of melting transitions on the heating rate during curve generation. These studies show that reliable high-resolution melt curve genotyping can be achieved in digital, picoliter-scale reactions and demonstrate that rate-dependent melt signatures may be useful for enhancing automated melt genotyping.

Entities:  

Keywords:  automated biology; engineering; high-throughput chemistry; informatics and software; point-of-care testing (POCT)

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Year:  2018        PMID: 29652558     DOI: 10.1177/2472630318769846

Source DB:  PubMed          Journal:  SLAS Technol        ISSN: 2472-6303            Impact factor:   3.047


  2 in total

1.  Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting.

Authors:  April Aralar; Yixu Yuan; Kevin Chen; Yunshu Geng; Daniel Ortiz Velez; Mridu Sinha; Shelley M Lawrence; Stephanie I Fraley
Journal:  J Clin Microbiol       Date:  2020-05-26       Impact factor: 5.948

2.  Data-driven noise modeling of digital DNA melting analysis enables prediction of sequence discriminating power.

Authors:  Lennart Langouche; April Aralar; Mridu Sinha; Shelley M Lawrence; Stephanie I Fraley; Todd P Coleman
Journal:  Bioinformatics       Date:  2020-12-23       Impact factor: 6.937

  2 in total

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