With regard to transcript stability, data from a recent study aiming at measuring mRNA decay rates in A. thaliana cell suspension cultures [ 37] were used to look for half-lifes of gene transcripts identified through proteomics.
Ideally, an efficient experimental design will be informed by an understanding of when increasing sequencing depth begins to provide rapidly diminishing returns with regard to transcript detection and DE testing.
This probably reflects the known limitations of the MPSS technology [ 39], particularly with regards to transcripts that were not detected (zero counts) in one sample, as well as genes lacking appropriate restriction enzyme sites required for this technology.
However, interplaying gene networks linked to phenotypes may not necessarily follow linear relationships with regard to transcript levels.
Despite previous studies indicating an upregulation of KIAA1199 in neoplasic CRC tissues, this has not been confirmed in a large set of samples, neither with regard to transcript or protein levels, nor with regard to clinical outcome.
In particular, it is unknown how complete neo-Y protein-coding genes are, especially with regards to the transcripts that contain premature stop codons or frameshift mutations.
In addition, we followed a different strategy of assigning flybase polypeptide IDs to neo-Y transcripts, and the two assemblies may differ with regards to which exact transcript is assigned to which FBpp ID (in the genome assembly, co-linearity and scaffold information was taken into account, whereas the transcripts were assigned based on a reciprocal best hit with D. pseudoobscura genes only).
However, with regards to the two transcripts characterised in this work as strongly up-regulated, namely cyclin D1 and c-myc, the former was down-regulated in our screen (×1.5 fold), and the latter gave values that were not considered statistically significant.
GO annotation [ 46], using Blast2GO (http://www.blast2go.com/b2ghome) [ 47, 48], was performed to compare the four transcriptomes with regards to the frequency of transcript-associated GO terms.
However, in order to fully exploit mesothelin as a target for clinical applications, it is important to understand mesothelin expression with regard to the transcript variants that have been reported to date.
