Here, we focus our attention on annotation of data in Gemma, a resource for re-use and meta-analysis of gene expression profiling data (Hamer,K. et al., submitted for publication, http://www.chibi.ubc.ca/Gemma).ubc.ca/Gemma
The generic flow involves reading source data, performing automatic and/or manual annotation on the data and then saving the annotations (usually together with the source data).
These annotations are based on data supporting different transcript isoforms; in most cases, there are no data addressing whether both protein isoforms are biologically relevant.
The annotation on each data point shows the actual percentage difference for that point; values for correlation level of 0.9 there are shown also as the first line of Table 5.
In this paper, we performed a comprehensive evaluation of different annotations on RNA-Seq data analysis, including RefGene, UCSC, and Ensembl.
In the previous paper [ 21], we evaluated the impact of different annotations on RNA-seq data analysis, including RefGene [ 22], UCSC [ 23], and Ensembl [ 24].
An extended functional annotation was built based on data retrieved from the plant genome database Phytozome and from an inference of function using homology between Populus and the model plant Arabidopsis.
These annotations include data on chromatin structure, methylation, protein motifs, and binding.
CATMAID includes an overlay capability to place layers of annotations on top of EM data and controls to adjust transparency of layers.
This allows the possibility of having multiple layers of annotations on the same textual data, even including annotations on other entity types that may also overlap.
