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These results indicate that our method is efficient in identifying highly confident regulatory interactions from raw dataset.
The clean reads from raw dataset were matched to the known miRNA in miRBase 16.0 (http://microrna.sanger.ac.uk) to identify conserved miRNAs and annotated stem-loop sequences.
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The features abstracted from the raw dataset are then used for scoring sleep stages.
The features with saturation rate ≠0 and its SNR <3 were excluded from the raw dataset.
Differentially expressed genes were identified from the raw dataset using Significance Analysis of Microarray software (SAM, [ 22]) as described in Pons et al. [ 17].
Such alignments allow us to quickly filter-out marginal Rmaps from the raw dataset and provide an initial assessment of the completeness of a given sequence build [ 27].
We applied a false discovery rate (FDR) of 5% (q value ≤ 0.05) as cut-off to select the SDEG from the raw dataset.
Nss1/ Idd5-ho 11, was found to be an outlier in both the array plot and the correspondence analysis in J-Express, and was excluded from the raw dataset, before filtration and normalization.
A random set was included for comparison, where we assembled the same number of sequences as obtained in the preprocessing step with SEED, but by randomly selecting the reads from the raw dataset.
In addition to the CCDF for all the collected data (solid line), we removed contacts with durations exceeding 7 hours (0.03% of total reported contacts) from the raw dataset (dashed line) because we assumed contact of this duration was due to sensors abandoned near each other.
We used the spike-ins from the Whitehead Institute raw dataset to evaluate the sensitivity of peak identification using the normalization and peak finding analyses that we applied.
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