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The optimal fuzzifier values were calculated for each set of transcripts and metabolites using the function mestimate.
For each set of transcripts and proteins, we identified over-represented biological functions, canonical pathways, and upstream regulators (Tables 1 and 2).
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This was done for the set of transcripts with and without motif.
Figure 3 shows the average numbers of four sets of transcripts and their intersections: the set of transcripts with a simulated differential expression, one set of transcripts identified as differentially expressed for each of both models, and the set of transcripts, which were identified as connected with an attributable (larger than zero in terms of FDR) blending error variance.
The wide variation in expression values for the nonannotated set of transcripts likely reflects that many of these transcripts are assembly errors rather than functionally transcribed genes.
Table 1 gives summary statistics for each kmer assembly before and after representative transcripts were chosen, and for the final merged set of transcripts.
Coverage plots: plots of coverage level versus base index, either for a single transcript or a set of transcripts.
As several assembled transcripts were obtained from each sample, we used Cuffmerge to assemble them into a comprehensive set of transcripts for further downstream differential expression analysis.
A transcript pattern identifies probe set)s across platforms that target a common set of transcripts for a specific gene.
Therefore, it is important to consider that this step may remove valid transcripts in order to generate a representative set of transcripts for annotation.
Choice of the underlying set of transcripts used for annotation can give the user more control over transcript use.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com