Sentence examples for both datasets were then from inspiring English sources

Genes that lacked orthologs in both datasets were then removed from each of the two networks.

The consensus CENH3-binding regions identified in both datasets were then considered as the functional centromeres.

Predicted ancestral sequences for both bacterial and archaeal/eukaryal datasets were then aligned using M-coffee.

The raw data for both response and signalling datasets were then normalized using a hill function filter and scaled to the range [0,1] as described previously [ 24] (See Additional File 3, S1 for an assessment of the proposed method's sensitivity to variables of the normalization procedure).

Transcriptomic datasets were then generated to assess the differential expression of both genes and miRNA between 7 and 21 DPP.

Phylogenetic trees for each datasets were then edited and annotated using Evolview V.255.

These three datasets were then combined using R statistical software to create one large population dataset spanning the years 3700 BC to 2000 AD.

The CT datasets were then resampled to match the resolution of the MR images.

By the jackknife cross-validation, the prediction accuracies for the two datasets were then calculated.

The transformed datasets were then hierarchically clustered using Cluster 2.11 and the results displayed using Treeview 1.60 [69].

The high resolution environmental datasets were then used to predict potential habitat suitability for five species of framework-forming scleractinians.