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 In Analysis, RNA-Seq, Sequencing

(This post is a continuation of our RNA-seq DX series leading up to our RNA-seq DX Webinar on July 26th. Part 1, Part 2)

We’ve already discussed the first problem of RNA-seq: How many reads do I need? The question is important because it helps researchers save money by not over or under sequencing. But another benefit is that it helps us set a baseline to compare results across experiments. Our second problem with current day RNA-seq is aimed directly at confidently comparing experimental results.

RNA-seq has the promise of full observation and characterization of the transcriptome without prior knowledge of the framework. In other words, the detection system does not rely on prior knowledge of the sequences as in micro-arrays and qPCR. With that said, previous technologies had a significant amount of controls and standards to normalize results such that samples could be accurately and confidently compared. Until recently, this wasn’t the case for RNA-seq. Just about every experiment will produce results and candidates, but making comparisons between experiments is difficult due to this lack of standards.

While there have been bioinformatic approaches to normalize and allow cross comparison of the data, recent publications have shown that results are highly dependent on the bioinformatics normalization chosen. Extremely concerning and a weak leg for those candidates to rest on. Like prior robust tests, what is needed are molecular based controls with which to confirm the success of the characterization and to normalize to a known molecular component such that we can confidently compare expression levels.

As previously posted, Cofactor has integrated molecular spike-ins into the process to overcome all of these hurdles associated with the lack of internal controls and inability to confidently compare across samples. We are the only commercial provider to offer this service. These spike-ins, developed by the ERCC, will help create the reproducible and comparable results that were expected from past technologies and required for RNA-seq to continue to move forward.

Check back soon for the next installment in our RNA-seq DX series. And be sure to join us for our RNA-seq DX webinar on July 26th.

Register to attend the webinar here.

July 26th 12-1 CST.

 

Sources:
Dillies MA, et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 2012.

 

Lovén, et al. Revisiting Global Gene Expression Analysis. Cell 151 2012.

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