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Our results show that EcoTILLING is a suitable and cost effective method for allele mining in B. vulgaris.
We could confirm that the polynomial based probe specific correction (PPC) was the most accurate method for allele frequency estimation.
When using a PCR-based approach, the most reliable method for allele identification so far has been sequencing with prior allele separation via cloning of PCR products.
This program has been found to be accurate in determining allelic phase even in extremely variable loci, such as the MHC [ 55] and, therefore, is considered to be a reliable method for allele identification.
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We did not investigate the applicability of the method for allele-specific CN estimates, but it's likely it could be used in such instances as well.
The method for allele-frequency estimation is efficient in terms of computational speed, allows for site-specific error rates, and yields estimates that are unbiased with minimal sampling variance (within the bounds dictated by the sampling scheme).
Traditionally, comparative ΔCt method for each allele frequency measure, which is based on the equation allele ratio = 2-ΔCt shown in Supplementary Material 7, 8, 9 [see Additional file 7, 8, 9], the averaged 2-ΔCt measurements of replicates were used to analyze linear regression to the mixed allele ratio.
The method for novel allele variant discovery is described in the Methods.
Here we present a method for HLA allele prediction from next-generation shotgun sequence datasets.
Next generation sequencing of pooled samples provides cost effective method for estimating allele frequencies at genome-wide scale [ 33].
We examined the association between cutoff value, encoding factor and method for every allele, after 10-fold cross-validation using logistic regression analysis.
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