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We computed the Hamming distance of each sequence (exons 2 and 3) with all other sequences, in terms of edit distance, and calculated the inter- and intra-group means.
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The closest distances of each query sequence to one or several reference sequences were all well below the clustering threshold, and the closest reference sequences belonged to TU as expected regarding the host- and morphology-based species identification, i.e. TU 5 (Peronospora minor), TU 49 (Pe. variabilis) and TU 73 (Pe. boni-henrici).
In order to estimate the rate at which sequences mutate due to the amplification steps of SELEX, we next determined the distance of each pool's sequences from the genome, in terms of gaps and mismatches in the best alignment to the genome.
The genetic distance of each of the study sequences to the Indian consensus C sequence (intra-population divergence) was calculated in MEGA 5 software [ 20].
After that, the BLOSUM62 distance of each transcript to the sequence of its closest reference taxon (i.e., the reference taxon found as best reciprocal hit) was calculated.
But there is one difference between profile distances and average distances: Average distances are a mean of maximum-likelihood distances of sequences between sequences of both groups, whereas the profile distance is a maximum-likelihood estimation between the "mean" of the sequences in both groups.
The test statistic "composition" was defined as the maximum of the χ2 distances separating each sequence composition from the composition of the whole data set [ 52].
For example, miRNA genes show two characteristic peaks of abundance, often located within a short distance of each other, with the miRNA sequence several times more abundant than the partially complementary miRNA* sequence.
We regressed r2 values adjusted by the physical distances with base-pair proportions of each sequence feature within SNP pairs, irrespective of categories of conserved and non-conserved regions.
The mean distance of a sequence from all CCR5/NSI sequences was considered as a measure of its conservation.
As the coreceptor prediction score we use the mean distance of a sequence to all the NSI/CCR5-tropic sequences in the training set.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com