The phrase "established alignment" is correct and can be used in written English.
It is typically used to describe a situation where two or more things have been arranged or positioned in a way that is well-defined, agreed upon, or proven to be effective. Example: The new project manager was able to quickly understand and work within the established alignment of the team, resulting in a successful completion of the project.
This time kmacs outperformed the established alignment-free approaches for all values of k that we tested.
This, in turn, allows using established alignment-based methods to analyze even large-volume next generation sequencing (NGS) datasets.
In most cases, phylogenetic trees calculated with kmacs were more accurate than trees produced with established alignment-free methods that are based on exact word matches.
We use these distance measures to construct phylogenetic trees for real-world and simulated DNA and protein sequence families, and we compare our method to established alignment-free methods using contiguous -word frequencies, as well as to a traditional alignment-based approach.
For longer patterns, however, the Euclidean distance performed better; for ℓ > 20, multiple patterns with the Euclidean distance produced perfect tree topologies, i.e. the RF distance was 0. The established alignment-free approaches K r and ACS performed worse than spaced words with single or multiple patterns for all ℓ > 10.
MaB generated data, established alignments and commented upon the manuscript.
IBE generated data, established alignments, participated in analyzing the data, and wrote parts of the manuscript.
In response, various optimization models have been proposed to establish alignments that meet various geometry requirements.
In 1932, the New York Automobile Club drafted a plan establishing alignments for several routes through the city.
A total of 315 gene-based markers mapped to the Lp1 family were used for establishing alignments with Os, Bd and Sb.
The results of these pairwise comparisons are used to train an SVR model that predicts PFM-similarities which are quantified using the well established multiple alignment based PFM similarity score MoSta [36].
