An important tool for studying organismal biology is transcriptomics. With the introduction of new parallel sequencing technology, a new field of transcriptomics has emerged, allowing for the identification and quantification of each and every transcript present in a sample through ever-deeper sequencing. For all transcriptomics experiments, this might not be the optimal way to use parallel sequencing technology. I investigated shallow RNAseq's capacity to capture the ma- -jority of this information and used the Shannon entropy technique to estimate the amount of information present in a transcriptomics experiment. This investigation demonstrated that a subset of the most abundant 5,000 transcripts or less within any given sample can effectively capture nearly all of the network or genomic information provided in a variety of transcriptomics experiments. Thus, it seems that using parallel sequencing technology, large-scale factorial analysis with a high level of replication should be doable and economical.