To seq or not to seq that is the DGE question.

The most common question asked in Differential Gene Expression (DGE) experimental design meetings at the CI is; "should we do RNA-seq or microarray processing?". It all boils down to what questions you want to answer and how the data will integrate into the bigger experiment. I have described some of the most common questions that are asked or discussed and hopefully this information will be useful in getting you thinking about the direction you want to go. 

1. Why are people doing RNA-seq?
2. Isn't RNA-seq really expensive?
3. What about analysis, does it take longer to analyse RNA-seq data?
4. Do I need as many replicates? 
5. How long does it take?
6. How many samples can be processed at once?

1. Why are people doing RNA-seq? RNA-seq data allows you to have a greater dynamic range than microarray. RNA-seq is a digital reading (counting number of reads) and microarray is the analogue reading (fluorescents units) this can be useful if you are looking at the extremes of expression. You may if you wish in the future take your prepared library and do a different type of sequencing and analysis to look for splice junctions and other transcriptional changes. It is important to remember that wanting more than DGE needs a completely different experimental design. For microarray processing you are restrained to the design of the array and what species you want to explore, with RNA-seq there is no restraint as long as you have an adequate reference genome/transcriptome to align your data to. There are lots of technical reasons why you would choose one method over the other but I do not think that you can ignore the fact that RNA-seq is the new technology and people are choosing the method as it fashionable and may seem to be more attractive for publications.

2. Isn’t RNA-seq really expensive? Currently the biggest cost in sequencing is the library preparation. In the core we are currently investigating alternative suppliers to reduce this cost. None the less currently sequencing costs are approximately the same as microarray for DGE analysis within the CIGC.

3. What about analysis, does it take longer to analyse RNA-seq data? By its nature more data is created from RNA-seq sequencing so this in its self requires a significant amount of computing time to process the information. Put these aside similar stringent work flows and pipelines are in place to create a comprehensive gene list of the comparison for both processes.

4. Do I need as many replicates? Yes. The design of the experiment for DGE will remain similar for both RNA-seq and microarray which includes replication requirements. Therefore the number of replicates recommended for the experiment will be the same for either RNA-seq or microarray processing.

5. How long does it take? RNA-seq takes about the same amount of time to process samples in the lab as microarray samples. For both it takes just under a week to get to QC’ed cRNA (microarray) or normalised pooled libraries (RNA-seq). We process both protocols within the institute. However, as we no longer have a working microarray scanner on site, the guys at the Department of Pathology kindly perform the scanning step for us.

6. How many samples can be processed at once? Microarray project designs are constrained to multiples of 12 to get the most out of the consumables, due to the way they are manufactured. RNA-seq utilises 96 individual indexes so if processing less than 94 samples (we use 2 for positive controls) all sample can be pooled together. It gets a little more complicated for larger projects but this is also true for microarray processing.