qPCR corroboration of an RNA-Seq experiment published in BMC Genomics: Characterizing short read sequencing for gene discovery and RNA-Seq analysis in Crassostrea gigas (Gavery & Roberts 2012)
This fileset includes data on qPCR corroboration of an RNA-Seq experiment published in BMC Genomics: Characterizing short read sequencing for gene discovery and RNA-Seq analysis in Crassostrea gigas (Gavery & Roberts 2012). In this study we wanted to investigate how qPCR technology using individ...
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Format: | Dataset |
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figshare
2013
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Online Access: | https://dx.doi.org/10.6084/m9.figshare.683879.v1 https://figshare.com/articles/dataset/qPCR_corroboration_of_an_RNA_Seq_experiment_published_in_BMC_Genomics_Characterizing_short_read_sequencing_for_gene_discovery_and_RNA_Seq_analysis_in_Crassostrea_gigas_Gavery_Roberts_2012_/683879/1 |
Summary: | This fileset includes data on qPCR corroboration of an RNA-Seq experiment published in BMC Genomics: Characterizing short read sequencing for gene discovery and RNA-Seq analysis in Crassostrea gigas (Gavery & Roberts 2012). In this study we wanted to investigate how qPCR technology using individuals corroborated with RNA-seq analysis. Overall, two general trends were observed. First, directionality of expression was congruent for a majority of the assayed genes. For those targets that were not in agreement, the difference in expression between the samples was within 2 fold. This observation is consistent with previous studies examining the correlation between RNA-seq and qPCR (e.g. Marioni et al., 2008, Beane et al., 2011). Second, the fold difference between samples was generally larger by RNA-seq analysis. For example, for all 4 genes determined to be significantly different by both analyses (DPGN, GSPA, GP17A and HMG2) the fold difference was larger for the RNA-seq analysis than for qPCR. Previous studies have also indicated that RNA-seq analysis reports larger fold differences than qPCR or microarray analysis (Hoen et al., 2008). The genes identified as not significantly different (CALL, GNRR2 and TIMP3) using RNA-seq had the lowest number of mapped reads. Aside from these general trends, there were differences observed between between these orthologous methods. There could be multiple explanations for these discrepancies, which are described. |
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