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We explored various models of partitioning of genetic variation (Supplementary Table S1).
Our calculations show that coverage is 71.2% for HapMap CEU variation and 75.8% for HapMap CHB variation (Supplementary Table S1).
The top three principal components (dimensions) explained 58.6% of the genetic variation (Supplementary Fig. S3).
Here the QTL effect explained from 3.2%to52.9%9% of explained the phenotypic variation (Supplementary Table S3f).
We also saw a weak trend for decreasing housekeeper expression with sample age in our data; however, there was considerable sample variation (Supplementary Figures 7 and 8).
The resulting surfaces are described by a set of principal components that can collectively describe >99% of the shape variation (Supplementary Fig. 2).
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Further analysis of the Δ3 4 mutationtation revealed genetic features associated with genomic copy number variations (Supplementary Material, Fig. S1a).
SINEU-2_Crp was further classified into four subfamilies (SINEU-2A_Crp to SINEU-2D_Crp) based on sequence variations (supplementary fig. S1, Supplementary Material online).
Many reads of the small insert library were found in contigs with minor DNA sequence variations (supplementary material S4, Supplementary Material online).
In particular, within the low GMS regions, there are important variations (Supplementary Table S3) such as rs33992775 (GMS:0.00) related to hemoglobin, rs104893928 (GMS:0.00) related to survival of motor neurons, rs116840812 (GMS:0.00) related to ribosomal proteins and so on.
In accordance with the original study, miRNA prediction by Module 4 identified a number of conserved and putative novel miRNAs that displayed high variation scores (Supplementary Table S5 and Supplementary Table S7 Q1).
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