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Therefore, transcripts with at least 300 mapped reads in all time-points were retained in the dataset for subsequent analysis (7240 out of 10123 annotated genes).
Eighteen studies therefore comprised the study dataset for subsequent analysis.
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We therefore developed a series of computational tools that align deep sequencing datasets for subsequent analysis and interpretation.
After literature search we decided to include the following 13 datasets for subsequent analysis which were divided in a training-group for identification of the Epigenetic-Aging-Signature and a validation-group.
The entire genome-wide expression dataset was used for subsequent analysis [ 9, 50].
Genic regions containing no less than 5 genotyped SNPs in the dataset were chosen for subsequent analysis.
Unified genomic locations (see Method below) for each eGene and eQTL in hg18/b36 reference were used to recalculate eQTL-eGene distances and direction (5′/- or 3′/+), and this dataset was used for subsequent analysis.
The validation proved that most datasets were suitable for subsequent analysis.
Only genes with more than three reads per base pair for at least one time point of the Nascent-Seq dataset and two read per base pair for the RNA-Seq dataset were further considered for subsequent analysis (see above).
PartitionFinderProtein 1.0.1 [ 79], using the greedy option and Bayesian Information Criterion BICC), tested the best partitioning scheme of our dataset, which was chosen for subsequent analysis, as well as the concatenated alignment and the completely partitioned model.
Raw beta-values of adipose tissue and bone marrow derived datasets were quantile normalized together for subsequent analysis of epigenetic differences.
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