Mus musculus domesticus Transcriptome or Gene expression

The use of fold-change (FC) to prioritize differentially expressed genes for subsequent characterization is a common technique in the analysis of RNAseq data but may exclude highly expressed genes, for which significant changes in abundance fail to exceed a ratiometric standard. Using next generation RNA sequencing, we demonstrated a novel method for prioritization of differentially expressed genes based on changes in transcript abundance (?T) using publically available RNA sequencing data as well as the following new pairwise comparisons: (1) DIV 0 neural precursor cells vs DIV 1 developmental stage (DS) I/II neurons, (2) DIV 1 neurons vs DIV 14 (DS III/IV) neurons (3) treatment of DIV 15 neurons with botulinum neurotoxin serotype A (BoNT/A) for 3 d vs DIV 18 neurons (4) or a low-dose treatment with 1 µM L-glutamate (Lglu) for 5 min in DIV 15 neurons, measured at 24 h vs DIV 16 neurons. The ?T method offers an additional method to analyze expression profiling experiments that is conceptually simple, easy to apply, and can be incorporated into the output of existing differential prediction algorithms with minimal effort. We propose that implementation of large ?T analysis will enable a broader overview of the mechanisms by which differential gene expression impacts cellular behavior.