contributed by a reader
As with all novel technologies that are becoming mainstream in research labs, a scientist coming into any new field might find themselves inundated with all of the available options for what they need to do. This is certainly the case with next generation sequencing technologies. The field is rapidly increasing with new tools for different approaches to sequencing being made available every day.
In the case of analyzing the expression profile for a particular sample, exome sequencing and RNA sequencing are two very useful applications. However, each provides different information which is not readily apparent, and making the decision as to which tool to use isn’t either. Here we outline the type of information that each tool provides, as well as some of the disadvantages of using one versus the other (1,2).
RNA Whole Transcriptome sequencing – Focused on gene expression profile
- Relevant gene expression is detected as a result of the RNA present.
- Able to see other biologically relevant information from RNA, aside from what is actually translated such as snRNA.
- Detects alternative splicing events.
- Probes the dynamics of gene expression, ie changes in regulation.
- Useful in detection of gene fusion products.
- Able to focus on biological pathway of interest using targeting enrichement, such as Ampliseq.
- Detection of genes with relatively low expression levels is difficult. (This may depend on the platform used; use of deep sequencing can detect low expressors)
- Single nucleotide variants can be detected, however you must ensure that the depth of the coverage is significant. Platforms such as SOLiD can do this, however it is much more costly and time consuming.
- Need a priori knowledge of the sample in order to enrich for under expressed genes.
- Ribo-depletion is required in addition to enrichment to get better coverage for specific rare genes, but will significantly reduce input amount
- The amount of data used in analysis can be quite large
Exome Sequencing – Focuses on all exons of the genomic DNA.
- Able to see copy number variants when comparing across multiple samples.
- Single nucleotide variants can be detected.
- Amount of data is less massive than looking at entire genome.
- Can detect chromosomal rearrangement events since the RNA is never expressed.
- Low expression level genes are easier to detect because you are targeting DNA.
- Still costly compared to RNA-seq.
- Doesn’t capture exons from mitochondria.
- Entire exome is not captured as a result of exclusion during the design of capture probes. Depending on the kit, the thermodynamics of the probe being designed determines whether it will be included.
In addition, a new tool to enrich for exomes in an RNA-seq application has been demonstrated to capture low transcripts; however the ability to accurately quantify is reduced (3).
Which application to use for gene expression profiles largely depend on the type of information to be obtained, in addition as to the level of sensitivity necessary. As the technology continues to be fine tuned, the decision as to which application is best will be further distinguishable.