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For global alignment, we use a graph-based approach making use of the same error metric and iteratively refine the complete vehicle trajectory.
As a performance measure of the alignment we use the average of the absolute alignment errors in seconds at the beginning and at the end of each lyric line.
Test data for alignment: We use the following three datasets to test the alignment accuracy of our method.
To bring models into further alignment, we use a Uniform (−100, 100) distribution over virus locations and serum locations.
When evaluating the amount of sequence data that should be used within NCF when generating an alignment, we use protein sequence similarity data.
To compute the pairwise alignment, we use dynamic time warping (DTW) due to its applicability to data with non-linear time scale distortions (Itakura, 1975; Kruskal and Liberman, 1999; Sakoe and Chiba, 1978).
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For sequence alignment we used RSEM, which aligns reads to known isoforms and then calculates gene expression as the sum of isoform expression for a given gene, assigning ambiguous reads to multiple isoforms using a maximum likelihood statistical model [ 25].
To align a group of sequences to a reference alignment, we used the "--add" option of MAFFT [ 94].
We used ClustalW with default parameters and gapopen = 0.0 to align the sequences shown in the Figure 2. Note that for the alignment, we used only the β-strand-rich region (β-barrel domain), positions 90 220.
Indeed, whichever program, query alignment and reference alignment we used, 13 HEAT repeats ( = 14%) and six ARM repeats ( = 10%) amongst the repeats from known structures remained undetected.
To detect positive Darwinian selection in the protein-coding multiple alignment we used the statistical methods available in the PAML package [37] developed by Ziheng Yang.
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