A little background: We have isolated neurofibromas from a NF1 mouse model from 4 different drug treatments. Tumors were allowed to grow for 7 months, at which point the mice to treated for 2 months with drugs that were hypothesized to shrink the tumors. From all 4 treatments some mice had tumors that shrunk (responsive) and some mice that had tumors that continued to grow (non-responsive). My goal is to look for genes that are overexpressed in non-responding tumors to ask if those genes play a potential role in drug resistance.
My question is: should I use affymetrix or RNA-seq to look for differential expression? I understand the pros and cons for each method but which one do you think would give me more reliable results? Regardless of the method, at least 6 responsive and non-responsive tumors from all 4 drug treatments, plus controls, will be used for this experiment.
It will depend on the distribution of gene expression levels in the sample you are measuring. If you don't know how many reads is sufficient to detect the genes of your interest, affymetrix would be a economical choice. If you do know how to estimate, RNA-Seq or TruSeq will be a better choice because these technologies are more reliable and will probably give you a more comprehensive gene profile.
I guess you should have known that sequencing based approaches are better because microarray technologies have three major drawbacks: (1) background level hybridisation, (2) probe affinity issues, and (3) a predefined set of genes.
If you are only interested in differential gene expression of known genes. Affy might be a better choice as it will be easier and cheaper to interpret. InSilico DB gives you access to public Affy gene expression and RNASeq data pre-processed and ready to analyze. You might find public experiments that are relevant to your question.
https://insilicodb.org
Hope this helps.
- Badges
- Science topic
- Similar topics
- Biological Science
- Genetics
- Gene Expression