4 research outputs found
Transcriptome Complexities Across Eukaryotes
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.Comment: 33 pages main text; 6 main figures; 25 pages of supplement; 1
supplementary table; 24 Supp Figures; 58 pages tota
Estimating transcriptome complexities across eukaryotes
Abstract Background 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 evaluating changes in complexity using TranD. Results We ascertain if complexity is increasing or decreasing across transcriptomes and at what structural level, as complexity varies. In this study, we define three metrics – TpG, EpT, and EpG- to quantify the transcriptome's complexity that encapsulates 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 orthologs and novel genes in transcript model analysis. Effective Exon Number (EEN) issued 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 gene differences in complexity for orthologs and whole-transcriptome analyses are biased towards low-complexity genes with few exons and few alternative transcripts. Conclusions With our metric analyses, we are able to quantify changes in complexity across diverse lineages with greater precision and accuracy than previous cross-species comparisons under ortholog conditioning. These analyses represent a step 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