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Methods and programs abound that use the gene alignment in different ways to reconstruct the species tree.
Inclinable linear elements using hemispheric nuts and washers facilitated fracture alignment in different planes and PO changes when necessary [ 6, 18- 20].
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Furthermore, different possible alignments in different parts of the site are tentatively regarded as an indication of shifting orientations to localised grids, following the shift in alignment of monumental architecture, as the settlement landscape expanded over time.
These tools generate alignments in different formats, however, complicating downstream processing.
Using the Conservation score to bin the alignments in different groups here, we observed the same trend (supplementary file S2: table S6, Supplementary Material online).
To this end, we used a randomly selected dataset of 1000 protein structures to test the alignment quality in different gap penalties based on evaluation of the TMscore.
To make the process quicker, the annotator can sort the alignment data in different ways, e.g. by utilising a quality value (Q val) based on mutual information and rewards consistent translations with high frequency [ 22].
The parameters to optimize in the alignment may differ in different situations, because it is not easy to single out a set of parameters that best captures the similarity between two given structures [ 2].
If the same alignments, but in different directions, were taken for training, the SVM model would be exactly symmetric.
Despite this systematic error, test runs on BAliBASE can give a rough impression about the performance of multiple-alignment programs in different situations.
A useful analogy may be a multiple sequence alignment: a sequence alignment aligns equivalent residues in different sequences and highlights regions with similar (conserved) residues, whereas the genome alignment in Figure 5 aligns equivalent genes in different genomes and highlights regions with similar (conserved) gene order.
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