Sentence examples for treated datasets from inspiring English sources

Could the surplus of the (A,A) covariation compared with that of (A,S) and (S,S) in the treated datasets (Specialty and Stanford-Treated) be an artifact of differences in the intrinsic mutation rates between silent and amino acid mutations (e.g. silent mutations are more likely to be transitions than transversions, thus evolving faster)?

AtWRKY6, AtWRKY18, AtWRKY45, and AtWRKY53 were differentially expressed in three jasmonate treated datasets.

Of the 39 jasmonate responsive AtWRKY genes, 22 were differentially expressed in at least two jasmonate treated datasets.

Differences between control and MeJA treated datasets were then adjusted using the Catharanthus reference gene EF1α (Cra3894) as an internal control [ 13], as this gene was used as the internal control for qRT-PCR expression measurements.

After discarding low quality, filtering 5' contaminant and trimming 3' adaptor reads, a total of 8,500,978, 9,357,545, 9,003,582 and 9,223,744 clean reads were obtained from mock, drought, salinity and alkalinity treated datasets, respectively (see Additional file 1).

C script to develop the dataset of distinct and fuzzy nucleosomes: Tab separated file containing the genomic positions and the log2 transformed signal intensity values from all the three replicates was used as input for both control and SA treated datasets to get the nucleosomal regions (distinct and fuzzy) at different stringency levels.

After we excluded non-reliable probes based on Δβ-values in control (mock treated) dataset, we normalized the DNA methylation data by comparing the mock-treated cells with the 5-aza-dC or IR-treated cells (Additional file 1: Figure S1).

The average signal intensity values of all the spike-in probes for two experiments with a reference and a treated dataset are shown in Figures 4 and 5.

A total number of ~1,000 and ~12,047 peaks were called by using W-ChIPeaks [ 14] in control and an E2 treated dataset, respectively.

These results also show that comparison of K a / K s values between treated and untreated datasets is effective at distinguishing primary and accessory drug-resistance mutations (only observed in the treated dataset) from viral fitness mutations (observed in the untreated dataset).

Based on experience treating datasets consisting of around 100 objects using the ED-XRF technique and following recommendations [26-28 [26-28caling was selected for column pre-treatment.