Figure 1.

Overview of the RNA-seq analysis pipeline for detecting differential expression. The steps in the pipeline are in red boxes; the methodological components of the pipeline are shown in blue boxes and bold text; software examples and methods for each step (a non-exhaustive list) are shown by regular text in blue boxes. References for the tools and methods shown are listed in Table 1. First, reads are mapped to the reference genome or transcriptome (using junction libraries to map reads that cross exon boundaries); mapped reads are assembled into expression summaries (tables of counts, showing how may reads are in coding region, exon, gene or junction); the data are normalized; statistical testing of differential expression (DE) is performed, producing and a list of genes with associated P-values and fold changes. Systems biology approaches can then be used to gain biological insights from these lists.

Oshlack et al. Genome Biology 2010 11:220   doi:10.1186/gb-2010-11-12-220
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