We also observed a clear differentiation between the underground and the surface controls that could be explained by the variety of radiation sources present during cell growth under unshielded conditions. However, cells grown at below background showed significantly lower viability compared to those grown at both control levels after 5 days of incubation and lasted, intermittently, for up to 21 days. We did not observe a significant difference in cell number during the course of the experiment. To explore the existence of such response in eukaryotic cells, we grew Chinese hamster (Cricetulus griseus) V79 cells for 23 days using three different dose rates: 0.91 (below background), 35 (surface control) and 72 nGy h−1 (underground KCl-amended control). The study of depriving cells from background ionizing radiation for the past decades has provided valuable insights into its role in cellular homeostasis control. Therefore, we would propose DESeq2 (“DNAstar-D”) as an appropriate software tool for differential gene expression studies for treatments expected to give subtle transcriptome responses. When another model organism’s (nematode) response to these radiation differences was similarly analyzed, DNAstar-D also resulted in the most conservative expression patterns. When RT-qPCR validation comparisons to transcriptome results were carried out, they supported the more conservative DNAstar-D’s expression results. coli three of the four programs gave what we consider exaggerated fold-change results (15 – 178 fold), but one (DNAstar’s DESeq2) gave more realistic fold-changes (1.5–3.5). Regarding the extent of expression (fold-change), and considering the subtlety of the very low level radiation treatments, in E. elegans analysis showed exaggerated fold-changes in CLC and DNAstar’s edgeR while DNAstar-D was more conservative. In a parallel study comparing three qPCR commercial validation software programs, these programs also gave variable results as to which genes were significantly regulated. Regarding the extent of expression differences, three of the four programs gave high fold-change results (15–178 fold), but one (DNAstar’s DESeq2) resulted in more conservative fold-changes (1.5–3.5). In contrast, when the programs used different approaches in each of the three steps, between 31 and 40 DEGs were found in common. After imposing a 30-read minimum cutoff, one of the DNAStar options shared two of the three steps (mapping, normalization, and statistic) with Partek Flow (they both used median of ratios to normalize and the DESeq2 statistical package), and these two programs identified the highest number of DEGs in common with each other (53). coli, the four software programs identified one of the supplementary sources of radiation (KCl) to evoke about 5 times more transcribed genes than the minus-radiation treatment (69–114 differentially expressed genes, DEGs), and so the rest of the analyses used this KCl vs “Minus” comparison. In addition, RNA-seq data of Caenorhabditis elegans nematode from similar radiation treatments was analyzed by three of the software packages. The gene expression response to three supplemented sources of radiation designed to mimic natural background, 1952 – 5720 nGy in total dose (71–208 nGy/hr), are compared to this “radiation-deprived” treatment. coli grown shielded from natural radiation 655 m below ground in a pre-World War II steel vault. The RNA-seq data are from the effect of below-background radiation 5.5 nGy total dose (0.2nGy/hr) on E. In this comparative study we evaluate the performance of four software tools: DNAstar-D (DESeq2), DNAstar-E (edgeR), CLC Genomics and Partek Flow for identification of differentially expressed genes (DEGs) using a transcriptome of E.
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