Quantification of the read count and detection of differentially expressed genes. Mapping RNA-seq to a reference genome where the genomic information is available and 3. Quality control of the Next-Generation Sequencing (NGS) data (eg. The general workflow of RNA-seq analysis includes 1. In most RNA-seq experiments, the primary interest of the researcher is to find out differential gene expression between treatment and control groups. One challenge in comparing inter-lab results is that commercial transcriptome analyses packages have different options and parameters for transcript quantification, normalization, and differential expression analysis. The use of multiple biological replicates is critical for meaningful detection of differential gene expression. A successful RNA-seq study can be achieved by a good experimental design, choosing the proper type of library, the appropriate sequencing depth, and the number of replicates specific for the biological system of the study. There are several software tools available and still in development for analysis of RNA-seq and for detection of differential expression, but there is no optimal analysis pipeline that can be used for all types of RNA-seq samples. Since the discovery of RNA’s role as a key intermediate between the genome and the proteome, the quantification of gene expression based on the number of mRNA transcript reads is of great utility in gene expression studies. RNA sequencing (RNA-Seq) has a wide variety of applications in gene expression studies. 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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