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Nine animals per group were randomly divided into three equivalent sets, and a constant amount of RNA from animals in the same set was pooled into one single sample in order to eliminate individual differences within group.
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Using Samtools (Li et al. 2009), we merged read data from separate sequencing runs of the same sample into one single BAM file per individual.
The triplicate samples for each condition in each of the seven cell lines (42 samples) were imported into one single experiment.
The three samples were combined into one single tube and stored.
By combining all sub-groups into one single group the resulting slope for the entire sample will be steeper.
The triplicate samples for each condition, sensitive and resistant, in each of the seven cell lines (42 samples in total) were imported into one single experiment.
The resulting files are generated independently for each sample and can be linked together into one single file containing the data of an entire screening experiment.
In order to preserve a large enough sample for comparing individuals with both diabetes and VI, participants falling into categories 2 5 were combined into one single category designated as "any VI".
Data fusion combines the two datasets into one single database.
All the four genes were merged into one single alignment.
For each of the three densely sampled data sets, consensus supertrees were generated as follows: 1) the bootstrap trees obtained from the ML analysis of each considered single-gene family were pooled into one single data set.
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