As advances in next generation sequencing continue to provide increasing access to the genomics -revolution for research systems having few or no genomic resources, transcriptome sequencing will only increase in importance as a fast and direct means of accessing the genes themselves. However, constructing a comprehensive cDNA library for deep sequencing is very difficult, as highly abundant transcripts hamper de novo identification of low-expressed genes, and genes expressed only under very specific conditions will remain elusive. The reduction of variance in gene expression levels to within a tenfold range of differences by cDNA normalization provides an important means of allocating sequencing across a greater fraction of genes, directly translating into a more even coverage across genes. Here, we outline two different normalization methods, addressing many of the important issues we think need consideration when going from RNA isolation to the cDNA material required for sequencing. This will provide coding gene information across thousands of genes from any organism, providing rapid insights into topics such as gene family member identification and genetic variation that may be associated with a studied phenotype.