The phrase "annotation we utilized the" is not correct in written English as it is incomplete and lacks clarity.
It can be used in contexts discussing the use of annotations in a specific study or project, but it needs to be restructured for clarity.
Example: "In our research paper, the annotation we utilized the most was the one that highlighted key findings."
Alternatives: "the annotation we employed" or "the annotation we applied".
For this, we utilized the L1Xplorer annotation pipeline and created a set of customized modules (see Materials and Methods for details).
To identify full-length transcripts in our transcript collection, we utilized the functional annotation results with all the contigs in the final assembly being searched against zebrafish RefSeq and Uniprot database.
We utilized the extensive annotation database of where zebrafish genes are expressed anatomically (anatomical ontology provided by the zebrafish model organism database; ZFIN [ 43]) to bring further clarity to this issue.
To gain biological insights, we utilized the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 web tool [ 41, 42] to identify the Gene Ontology (GO) [ 43, 44] biological process and molecular function terms that exhibit significant enrichment in our gene list.
Next, we utilized the residue-resolution functional annotation of the previously generated E. coli GEM-PRO to identify whether the SMAP-predicted ligand binding sites overlapped with known functional sites, such as catalytic and substrate binding sites.
We utilized the mRNA and EST annotations from the UCSC Genome Brower Database to search for transcription evidence of intron gain in retrogenes [ 18, 19].
Second, we utilized the information of co-annotation of genes by a pair GO terms, a source of information ignored by most of contemporary methods, to further enhance the discriminative power of the graph-based metrics.
In this paper, we utilize the detailed annotation of the ENCODE regions and assemble the lists of conditional exons, constitutive exons and conditional-exon-free introns as training sets.
Additionally, we utilize the gene ontology annotation, in combination with measures of synonymous and non-synonymous differences in orthologous protein sequences, to better understand which of the cDNA sequences are likely to represent conserved mammalian biology and which are more likely to represent feline-specific biology.
We utilize the MIRIAM compliant annotations of biosimulation models to generate ontology-based representations of these models.
