Replicates, Read Numbers, and Other Important Experimental Design Considerations for Microbial RNA-seq Identified Using Bacillus thuringiensis Datasets.
RNA-seq is being used increasingly for gene expression studies and it is revolutionizing the fields of genomics and transcriptomics. However, the field of RNA-seq analysis is still evolving. Therefore, we specifically designed this study to contain large numbers of reads and four biological replicates per condition so we could alter these parameters and assess their impact on differential expression results. Bacillus thuringiensis strains ATCC10792 and CT43 were grown in two Luria broth medium lots on four dates and transcriptomics data were generated using one lane of sequence output from an Illumina HiSeq2000 instrument for each of the 32 samples, which were then analyzed using DESeq2. Genome coverages across samples ranged from 87 to 465X with medium lots and culture dates identified as major variation sources. Significantly differentially expressed genes (5% FDR, two-fold change) were detected for cultures grown using different medium lots and between different dates. The highly differentially expressed iron acquisition and metabolism genes, were a likely consequence of differing amounts of iron in the two media lots. Indeed, in this study RNA-seq was a tool for predictive biology since we hypothesized and confirmed the two LB medium lots had different iron contents (~two-fold difference). This study shows that the noise in data can be controlled and minimized with appropriate experimental design and by having the appropriate number of replicates and reads for the system being studied. We outline parameters for an efficient and cost effective microbial transcriptomics study.
Graduate School of Genome Science and Technology, University of TennesseeKnoxville, TN, USA; BioEnergy Science Center, Oak Ridge National LaboratoryOak Ridge, TN, USA.
Punita
P
Manga
BioEnergy Science Center, Oak Ridge National LaboratoryOak Ridge, TN, USA; Biosciences Division, Oak Ridge National LaboratoryOak Ridge, TN, USA.
Dawn M
DM
Klingeman
Biosciences Division, Oak Ridge National Laboratory Oak Ridge, TN, USA.
Tse-Yuan S
TY
Lu
Environmental Sciences Division, Oak Ridge National Laboratory Oak Ridge, TN, USA.
Tonia L
TL
Mehlhorn
Graduate School of Genome Science and Technology, University of TennesseeKnoxville, TN, USA; Biosciences Division, Oak Ridge National LaboratoryOak Ridge, TN, USA.
Dale A
DA
Pelletier
Graduate School of Genome Science and Technology, University of TennesseeKnoxville, TN, USA; Biosciences Division, Oak Ridge National LaboratoryOak Ridge, TN, USA.
Loren J
LJ
Hauser
BioEnergy Science Center, Oak Ridge National LaboratoryOak Ridge, TN, USA; Biosciences Division, Oak Ridge National LaboratoryOak Ridge, TN, USA.
Charlotte M
CM
Wilson
Graduate School of Genome Science and Technology, University of TennesseeKnoxville, TN, USA; BioEnergy Science Center, Oak Ridge National LaboratoryOak Ridge, TN, USA; Biosciences Division, Oak Ridge National LaboratoryOak Ridge, TN, USA.
Steven D
SD
Brown
DESeq2; Illumina; coverage; negative binomial; normalization; replicates