Data production workflow for microarrays. Microarrays require labeling of target material, hybridization to arrays, washing, and scanning to obtain measures of gene expression. RNA converted to cDNA from the sample will hybridize to the corresponding oligonucleotide targets, so that more highly expressed genes will be reflected in more abundant material hybridized and thus greater fluorescence intensity. In modern arrays, multiple probes are designed for a single gene in order to obtain fluorescence intensities that can be used as an index of gene expression.

Comparison of array and RNA-Seq data for measuring differential gene expression in the heads of male and female D. pseudoobscura. (a) Results for female heads; (b) results for male heads. We used custom designed Nimblegen arrays to an early release of the D. pseudoobscura annotation. This array consists of 50-mer probes selected without bias to gene position, and with an average of 10 probes per gene model. A full description of this array platform can be found in the GEO under platform number GPL4631. Robust Multi-array Averaging (RMA) [50] was used to normalize array experiments and normalization improves the correlation between arrays and sequencing results. A full description of the analysis and all sequencing data can be found in [51]. Colored circles are genes identified as differentially expressed between females and males by microarray analysis with four biological replicates. In this case, one of the four biological replicates was prepared for sequencing by fragmenting RNA using alkaline hydrolysis and constructing a cDNA library for sequencing. For these analyses, we generated about 6 million 36 base pair reads from the Illumina GA I platform and the number of reads per kilobase per million mapped reads (RPKM) was calculated by counting the number of unique mapping reads from the default Illumina mapper (ELAND but the same pattern holds for Bowtie) to the same coding sequence models that were used for constructing probes for the microarray. The correlation between fluorescence intensity as a surrogate for gene expression and the RPKM metric as obtained by mRNA-Seq is high (Pearson's r = 0.90-0.91; Spearman's rho = 0.90-0.91) and slightly higher for just the genes identified as differentially expressed by microarrays (Pearson's r = 0.89-0.92; Spearman's rho = 0.90-0.94). In the case of fold change (c) measurements (female/male), the congruence is reasonable for the entire data set (Pearson's r = 0.62; Spearman's rho = 0.54) but high in the case of the fold change measurements for the genes with sex-biased expression (Pearson's r = 0.92; Spearman's rho = 0.89).

Data production workflow for RNA-Seq. RNA-Seq requires building libraries of fragmented RNA that are then converted to cDNA by reverse transcription, followed by adaptor ligation and size selection. Sequencing libraries are prepared for clustering on an 8 lane flow cell and sequencing-by-synthesis is used to generate tens of millions of sequences per sample that can be mapped to a reference genome. The number of reads that map to a scaled region of genome space are the index of the expression level of the gene.