High Resolution Melt Analysis in Python
Demo is on duty.
The PyHRM.py can be run in Jupyter Notebook.
contributed by https://github.com/liuyigh/PyHRM
When you get noisy data, the k-means is not going to magically salvage it. Try these:
- Do your PCR with touch down protocol, it greatly improves data quality, like magic!
- Make sure you get rid off empty wells, failed wells (look at your melting curve peaks), obvious outliers
- Make sure you choose the best temp range ± 5 degree C around melting temp usually works the best.
- For subtle differences, your eyes can be better at pattern recognition than k-means. Use the provided code to plot it with
plot.ly
. You can look at individual lines on plot.ly to make your own judgement. - Reduce heat block variation by running only 1 target gene in symatrically arranged wells.
We are used SYBR Green reagents on our samples Clinical demo data is collected by Medical Genetics Center in Yakutsk with patients of different hereditary pathologies
PCR protocal is developing. There are evidence that incresing melting temperature around 5-10 C on PCR will make better performance on data (https://pubmed.ncbi.nlm.nih.gov/29717053/)
- Precision Melt from Bio-Rad: $ not supplied on the territory of Russia
- GenEx: EUR€ not supplied on the territory of Russia
- Life Technologies: $ not supplied on the territory of Russia
- uAnalyze: Does not support Bio-Rad CFX platforms, but if change csv file to tabular delimiter it should work fine
- There is another HRM script on R to be investigated https://pavloh.shinyapps.io/hrmR/ also on GitHub https://github.com/pavlohrab/hrmR