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For each sample, we generated pileup files and used them for visual confirmation of novel variant calls.
In order to delineate which of the bacterial and host-associated study variables contributed to the species diversity per sample, we generated a multivariate PLS (Projection to latent structures by means of partial least squares) model using 166 X variables (133 bacterial species and 33 host-associated variables) and one Y variable (number of different species per sample) (Figure 2A, B, C).
For each pair of WGA and non-WGA sample, we generated de novo variation calls simultaneously.
To achieve comprehensive coverage for each sample, we generated ~25 30 million single end reads.
For each sample we generated a file with the sequences (.fasta) and the corresponding file with the quality scores (.qual).qual
Using the total number of errors/variants counted per sample, we generated a normalized error score for each enzyme or library-preparation method.
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First, we draw the symbol sample, and then for each symbol sample, we generate a phase sample: (24).
For comparison between samples we generated normalized clonality values (NC values) where the most clonal integration within each sample was normalized to a value of 1 (Fig. 2c).
To further evaluate the performance of the approach on a larger set of samples, we generated in silico patterns by randomly combining from 2 to 6 single strain SSR patterns from the reference strain collection (1000 patterns for each combination).
In other words, for the presented results in Tables 3 and 4 under 'Rnd (600 samples)', we generated 600 random patches 16 times.
For one of the samples, we generated additional sequence using the cloning-independent pyrosequencing method [25] on the Roche GS20.
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CEO of Professional Science Editing for Scientists @ prosciediting.com