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Table 1 should be used to compute the numerical value for the alignment.
To analyze numbers and lengths of exons and introns, cut-off value for the alignment length was set as 90% of nrFLcDNA length.
The set of TR from the Philadelphia strain used in the results presented before in this paragraph was obtained by mreps with the highest value for the alignment score between the copies of an ATR.
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The Uniprot description for each Affymetrix® ID is accompanied by the E-value for the alignment; na = not applicable, no significant alignment.
However, before eliminating three of the four near identical (≥99%) environmental sequences isolated in 1999, the p-value for the alignment was 0.22, well above the threshold level.
We considered for addition to TCDB only cases where the Pfam sequence contained the same (or similar; ±1) number of TMSs as its closest match in TCDB and for which the E-value for the alignment between the Pfam sequence and the TCDB closest match was in the range 0.1 1 × 10− (Table S2).
Using just one E-value for the alignment enabled comparison across the methods, but it should be noted that the EVfold server offers optimization of the E-values based on sequence coverage and number of sequences found, which results in improved results in some cases.
We extracted identity values for the alignments of human cDNA and amino acid sequences, respectively, to their orthologs of mouse, rat, chimpanzee, dog, cow, and chicken.
Because the relatedness between the reference and the original sequence was different for each genome (the same species for the human and rat and different orders for the bird) and the degree of sequence conservation varies across the genome, only the relative values of the alignment scores for each contig could be used to separate the mixture of contigs into its component genomes.
These templates were selected based on the best BLAST E-values found for the amino acid sequences matching with the query sequence, using a cutoff value of 0.5 for the alignment score.
The probability of finding in random networks two nodes with the same or higher interaction overlap as a given alignment is estimated, and serves as a p-value for the corresponding alignment [See Additional file 1 for details.] We use an iterative algorithm as described in [ 7] to find the high-scoring graph alignments.
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