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To do this, G2D measures the semantic distance between the annotations of the "known genes" and the annotations of genes in the problem region assigned by homology searches.
There are some differences between the annotations of shared homologs and D. antiqua unique unigenes, such as the GO term "synapse part" in the cellular component, "auxiliary transport protein," "metallochaperone," "nutrient reservoir," and "protein tag" in molecular functions, and "cell killing" and "viral reproduction" in biological process annotation.
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Overall a good correlation was observed between the annotation of the EUL domains for the different species and the rice domain in this cluster.
The correlation between the annotation of the contigs alone and the theoretical domain content of the sample were higher than for all read annotation (see Table 1).
We noticed some discrepancies between the annotation of Xie et al. [ 14] and the list of miRNAs in miRBase http://www.mirbase.org/[ 15- 17].
As shown in Figure 7(A), for transcript NM_001260832 of gene UTP15, the 12th exon has a 2 bp shift (70174203 vs. 70174205) between the annotation of RefSeq-rheMac3 and GASS.
An example of the stronger support provided by evidence lines of multiple types can be found in the annotations of homology between anatomical entities, provided by the Bgee team (see https://github.com/BgeeDB/anatomical-similarity-annotations/wiki/Similarity-annotations).
However, Cornell and co-workers [ 19] showed that there is little difference between the two modelling methods when the annotations of protein pairs found using each model were compared.
To measure the similarity between the GO annotations of two proteins, we calculated the maximum depth of the common ancestor of all pairs of GO terms assigned to both proteins (see Methods; Additional file 2; Fig. 2).
We also compared the consistency level between the functional annotations of each of the 108 modules and those of their corresponding predicted cis regulatory motifs for the module.
In the following, we present MedSim, a novel approach to disease gene prioritization that exploits the similarity between the functional annotations of diseases and candidate genes (Fig. 1).
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