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

ABSTRACT: The need to rapidly screen complex samples for a wide range of nucleic acid targets, like infectious diseases, remains unmet. Digital High-Resolution Melt (dHRM) is an emerging technology with potential to meet this need by accomplishing broad-based, rapid nucleic acid sequence identification. Here, we set out to develop a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. We then use these datasets to identify the most challenging melt curve classification tas

SUBMITTER: Langouche L 

PROVIDER: S-EPMC8016452 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature