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We determined potential somatic copy number variations by comparing each tumor focus with the matched benign tissue (Additional file 1, Figure S6).
The CT26 tumor genome: using the NGS reads, we assessed copy number and nucleotide variations by comparing CT26 to BALB/cJ DNA.
The first step is to detect variations by comparing the short-read DNA sequences of the cultivar to the reference genome of the target crop.
Although the number of synonymous variations was always significantly higher than non-synonymous variations, by comparing the number of non-silent variations between the acidic and TPR regions, it is possible to assign significantly lower levels of non-silent variations in the latter.
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We guess that you estimated the variation by comparing the values in different experiments.
A common way to interpret the scores and facilitate the comparison between different methods is to use score variation by comparing the mutant score with the reference score.
Copy number variants were detected using three different platforms: sequence read-depth analysis; CGH arrays by signal intensity variation by comparing two samples; and SNP arrays by clustered pool references, signal intensity, and allelic intensity [ 29, 60, 61].
We tested the assumption that combining individuals from the same population would not create any significant bias due to the expected higher inter- than intra-population variation by comparing CN among and between the seven populations.
We used the Mantel-Haenszel (M-H) method; this estimates the amount of between-study variation by comparing each study's result with a M-H fixed-effect meta-analysis result (Higgins 2011).
We performed SegSeq [ 54], a widely used method to identify copy number variation (CNVs) by comparing a tumor sample with a matched normal sample, to infer somatic CNVs in ESCC genomes based on WGS reads.
‡ Inter-assay variation was calculated by comparing variation in Ct value on experiments on 4 individual occasions.
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