High Resolution Melting (HRM) for High-Throughput Genotyping-Limitations and Caveats in Practical Case Studies.
Marcin SłomkaMarta Sobalska-KwapisMonika WachulecGrzegorz BartoszDominik StrapagielPublished in: International journal of molecular sciences (2017)
High resolution melting (HRM) is a convenient method for gene scanning as well as genotyping of individual and multiple single nucleotide polymorphisms (SNPs). This rapid, simple, closed-tube, homogenous, and cost-efficient approach has the capacity for high specificity and sensitivity, while allowing easy transition to high-throughput scale. In this paper, we provide examples from our laboratory practice of some problematic issues which can affect the performance and data analysis of HRM results, especially with regard to reference curve-based targeted genotyping. We present those examples in order of the typical experimental workflow, and discuss the crucial significance of the respective experimental errors and limitations for the quality and analysis of results. The experimental details which have a decisive impact on correct execution of a HRM genotyping experiment include type and quality of DNA source material, reproducibility of isolation method and template DNA preparation, primer and amplicon design, automation-derived preparation and pipetting inconsistencies, as well as physical limitations in melting curve distinction for alternative variants and careful selection of samples for validation by sequencing. We provide a case-by-case analysis and discussion of actual problems we encountered and solutions that should be taken into account by researchers newly attempting HRM genotyping, especially in a high-throughput setup.
Keyphrases
- high throughput
- high resolution
- single cell
- genome wide
- mental health
- mass spectrometry
- copy number
- circulating tumor
- molecularly imprinted
- quality improvement
- primary care
- cell free
- single molecule
- healthcare
- tandem mass spectrometry
- electronic health record
- physical activity
- dna methylation
- patient safety
- emergency department
- big data
- machine learning
- deep learning
- drug delivery
- nucleic acid
- cancer therapy
- quantum dots