In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms.
Modeling of homologous hexameric UlaGL structures were performed with MODELLER-9v8 [ 50] and Rosetta v. 3.2 [ 51, 52] using customized python scripts and pair-wise sequence alignments extracted from the multiple sequence alignments used for phylogenetic analyses (see above).
The stringency of this search can be customized by adjusting various parameters affecting the alignment.
DRAGEN accelerated these steps by highly parallel alignments to a sorted reference genome and customized high-memory computer hardware with high IO throughput.
In addition to the annotations adopted in this study, customized annotations can also be included in the output e.g., the pairwise sequence alignment can have annotations for residues interacting with biomolecular partner(s).
Sequences showing a weak alignment (allowing a maximum of four bases in homopolymer) with a V3 V4 customized SILVA database (v119) were removed as well as overhangs at both end of each sequence.
Alignment of the 126 sequences of the P. chrysosporium P450ome was generated using the following customized parameters that varied from the default parameters- Matrix-BLOSUM (Henikoff), and Gap Open Penalty -1.
He totally customized it.
Each shade is customized.
AW provided customized illustrations.
