Sentence examples for using the same transcripts from inspiring English sources
Considering all annotation categories for VEP and ANNOVAR annotations shows a substantial amount of disagreement in annotations from the two tools, even when using the same transcripts (Additional file 1: Figures S1 and S2).
Even when using the same transcript set, different annotation software packages can provide substantially different annotations.
Although it is counter-intuitive (since the annotations were based on the same set of transcripts), ANNOVAR and VEP do not always use the same transcript for the annotation of a variant.
There are only 16 variants that are annotated as stop-loss by VEP and as something else by ANNOVAR when the two tools use the same transcript for annotation (Additional file 1: Table S8).
When characterising differences and apparent errors in annotation, we looked at variants for which we know ANNOVAR and VEP did indeed use the same transcript as the basis for annotation.
We investigated these differences in annotation on variants where both tools used the same transcript for annotation, and annotations did not match, that is, a variant with a splicing annotation from ANNOVAR did not get an annotation of one of splice donor variant, splice acceptor variant or splice region variant, or the inverse.
Using the same maternal transcripts as the target sequences, the ZEBRAOV database was around 1.8 times more sensitive, for the detection of these transcripts, than the EST method previously used to describe the zebrafish ovary transcriptome.
A simple condition tree using the same flagged transcripts is shown in supplementary Figure 1B.
This would be possible if the piRNA and mRNA production pathways compete with each other by using the same primary transcripts as a source for either piRNAs or mRNAs.
The microarray testing phases are designed to evaluate the candidate external RNA controls on both one-color and two-color platforms using the same pools of transcripts.
Using the same low-abundance transcripts detected by both methods as the targeted sequences, we observed that the SAGE method is 26 times more sensitive than the EST method for the detection of low-abundance transcripts.
