Figure 1. Flowchart of RNA-seq data analysis methodology using Burrows-Wheeler Aligner (BWA) and TopHat. Schematic representation of two RNA-seq data analysis workflows and resulting views of the data generated. A: BWA workflow: Gapped alignments are performed using the BWA algorithm against the mouse reference genome build mm9, and estimation of the expression of genes at the transcript isoform level is performed by importing aligned reads into the Partek Genomics Suite using annotations provided by the University of California Santa Cruz (UCSC) refflat.txt file. Transcripts expressed at low levels in all samples (<1 fragments per kilobase of exon model per million mapped reads [FPKM]) are filtered out. Differential expression analysis was performed by applying the ANOVA (ANOVA) method, and the resulting list was sorted and filtered into different transcript groups. Clustering of rod and cone enriched genes was performed using Cluster 3.0 software (see Methods). B: TopHat workflow: Splice junction mapping was performed using the TopHat algorithm in two phases. In the first phase, splice junctions were detected de novo from the reads from each sample and combined to obtain a master splice junctions list. In the second phase of TopHat alignment, reads from each sample were re-aligned by providing the master junctions list as input. The two-phase mapping approach significantly increased the number of alignments spanning the splice junctions. Estimation of gene expression and differential expression were computed using Cufflinks, Cuffcompare, and Cuffdiff. Sorting and filtering of transcript isoforms were performed as in the BWA workflow.