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We used an iterative mapping approach to identify and define de novo splice sites with short reads and increase confidence in our transcript predictions.
Ten genes known to be imprinted in placenta had sufficient expression levels to attain a read depth that provided statistical power to detect imprinting, and yet all were consistent with non-imprinting in our transcript count data for neonatal brain.
No such correlation could be detected in our transcript profiles from many tissues of Setaria (Additional file 1: Figure S2).
While most of the JGI set was found in our transcript collection, we had many transcripts not covered at all by the JGI set (37%).
To delineate these metabolic pathways further, we mapped the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg) [ 28] database to the annotations in our transcript data.
Thus, it is not surprising that one of the most affected metabolic pathways in our transcript profiling experiments in this study was steroid biosynthesis.
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Similar is the case of the salicylic pathway where only the transcription factor TGA is found upregulated in our transcripts.
Indeed, in our transcript-based network analysis as well as in our proteome dataset, we found that metabolic signatures related to starvation and oxidative stress were consequences of aging.
The annotation results, however, did not pick up oxalate oxidase or oxaloacetate acetylhydrolase (enzyme involved in conversion of oxaloacetate to oxalate) in our transcripts.
Other tripeptides are found in the Higuchi Protobothrops and Gloydius transcripts and in our Ovophis transcript.
In our study, transcript levels did not correlate with the protein levels.
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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