High Resolution Melt Analysis in Python
I am surprised that no free software is available to do such simple data analysis. So with some opensource spirit, I decide to write my own and share it with fellow scientists.
You can view this ipython notebook demo here:
Clustering not working.
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.
How sensitive is pyHRM?
I am able to reliably detect:
- nfkb1-/- genetyping: WT vs HET vs KO; using original regular PCR primer
- an amplicon in nfkb2 gene that have single point T->G mutation: WT vs HET vs KO
- an amplicon in nfkb2 gene that have 4 base pair "TCCA" loss mutation: WT vs HET vs KO
Do your PCR with touch down protocol, it greatly improves data quality, like magic!
Basics: HRM - High Resolution Melt Analysis
- Precision Melt from Bio-Rad: $3,455
- GenEx: EUR€595 - 2,495
- Life Technologies: $794, 1 license
- uAnalyze: Does not support Bio-Rad CFX platforms