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CNV estimate is based on coverage and variability patterns summarized from multiple reference samples.
Using these multiple reference samples, we identified chromosomal segments with CNVs.
Although these rates were based on CNV detection using multiple reference samples, we also calculated rates using the conventional single reference method.
We then performed the same procedures as in the normal (non-permutated) data to determine CNV segments based on multiple reference samples.
From the SW-ARRAY results of all pair-wise samples, we used the previous algorithm (4) to determine CNV segments on the basis of multiple reference samples.
For each SW-ARRAY segment determined by the pair-wise comparison, we identified multiple reference samples that were thought to have two copies (4).
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These analyses provide detailed views of ovarian cancer genomic changes and highlight the benefits of using multiple reference sample types for the evaluation of CNA-specific expression changes.
A curve relating f to E s * was determined from multiple measurements on reference samples.
Considering these issues, we proposed a novel method called PatternCNV, which summarizes overall consistent patterns of both depths and variability of exonic region coverage across samples, where 'patterns' of coverage and variability are summarized using multiple 'normal' or reference samples.
To test the performance in different conditions, we sampled multiple reference datasets with different average lengths from the Target Scan database.
Whole exome and whole genome sequencing was performed on multiple technical replicates of five reference samples using the Illumina HiSeq 2000/2500.
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multiple reference genes
multiple reference ones
multiple reference translations
multiple reference stations
multiple reference systems
multiple reference sequences
multiple reference points
multiple reference transcripts
multiple reference groups
multiple reference strains
multiple reference books
multiple reference frames
multiple reference lines
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