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We applied a robust technique developed in [9] to the 672 sequences which identified all robust mutations in the data using Principal Component Spectral Analysis [10], [18] [21] to identify the mutations that represent most of the variation in the data.
These false-positive splits might be due to some random mutations in the data.
A classifier divides the mutations in the data set into two groups: computational hot spot residues and computational non hot spot residues.
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This procedure is repeated 258 times until every mutation in the data set is tested.
Plasmids without the desired structure appeared to have the LEU2 gene in the pDblet cloning site but had some mutations in the regions (data not shown).
We corrected stability changes for all relevant (>230) mutations in the extracted data set.
Exclusion of mutations at residues contacting DNA did not eliminate the enrichment of missense mutations in the DBD (data not shown).
Detection of mutations in the PacBio data was performed using the 'Minor and Compound Variants' plug-in available in v2.0.1 of the PacBio SMRT Analysis Portal.
We observed a significant excess in the proportion of private mutations in the empirical data compared with models of demographic history without a recent epoch of population growth.
Across all 556 mutations in the OMIA data set up to 2012, 193 are missense and 78 are non-sense, indicating that missense mutations are 2.5 times as frequent as are non-sense mutations.
To identify somatic mutations in the WES data, a filter was first applied to exclude artifact mutations introduced by the processing of formalin-fixed, paraffin-embedded (FFPE) specimens, which are characterized by artificially induced C > T [ 32].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com