  Genomic complexity is a growing field of evolution, with case studies for
comparative evolutionary analyses in model and emerging non-model systems.
Understanding complexity and the functional components of the genome is an
untapped wealth of knowledge ripe for exploration. With the "remarkable lack of
correspondence" between genome size and complexity, there needs to be a way to
quantify complexity across organisms. In this study we use a set of complexity
metrics that allow for evaluation of changes in complexity using TranD. We
ascertain if complexity is increasing or decreasing across transcriptomes and
at what structural level, as complexity is varied. We define three metrics --
TpG, EpT, and EpG in this study to quantify the complexity of the transcriptome
that encapsulate the dynamics of alternative splicing. Here we compare
complexity metrics across 1) whole genome annotations, 2) a filtered subset of
orthologs, and 3) novel genes to elucidate the impacts of ortholog and novel
genes in transcriptome analysis. We also derive a metric from Hong et al.,
2006, Effective Exon Number (EEN), to compare the distribution of exon sizes
within transcripts against random expectations of uniform exon placement. EEN
accounts for differences in exon size, which is important because novel genes
differences in complexity for orthologs and whole transcriptome analyses are
biased towards low complexity genes with few exons and few alternative
transcripts. With our metric analyses, we are able to implement changes in
complexity across diverse lineages with greater precision and accuracy than
previous cross-species comparisons under ortholog conditioning. These analyses
represent a step forward toward whole transcriptome analysis in the emerging
field of non-model evolutionary genomics, with key insights for evolutionary
inference of complexity changes on deep timescales across the tree of life. We
suggest a means to quantify biases generated in ortholog calling and correct
complexity analysis for lineage-specific effects. With these metrics, we
directly assay the quantitative properties of newly formed lineage-specific
genes as they lower complexity in transcriptomes.
