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

Genome assembly and population genomic analysis provide insights into the evolution of modern sweet corn.

Sweet corn is one of the most important vegetables in the United States and Canada. Here, we present a de novo assembly of a sweet corn inbred line Ia453 with the mutated shrunken2-reference allele (Ia453-sh2). This mutation accumulates more sugar and is present in most commercial hybrids developed for the processing and fresh markets. The ten pseudochromosomes cover 92% of the total assembly and 99% of the estimated genome size, with a scaffold N50 of 222.2 Mb. This reference genome completely assembles the large structural variation that created the mutant sh2-R allele. Furthermore, comparative genomics analysis with six field corn genomes highlights differences in single-nucleotide polymorphisms, structural variations, and transposon composition. Phylogenetic analysis of 5,381 diverse maize and teosinte accessions reveals genetic relationships between sweet corn and other types of maize. Our results show evidence for a common origin in northern Mexico for modern sweet corn in the U.S. Finally, population genomic analysis identifies regions of the genome under selection and candidate genes associated with sweet corn traits, such as early flowering, endosperm composition, plant and tassel architecture, and kernel row number. Our study provides a high-quality reference-genome sequence to facilitate comparative genomics, functional studies, and genomic-assisted breeding for sweet corn

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

Non-Uniform and Non-Random Binding of Nucleoprotein to Influenza A and B Viral RNA

The genomes of influenza A and B viruses have eight, single-stranded RNA segments that exist in the form of a viral ribonucleoprotein complex in association with nucleoprotein (NP) and an RNA-dependent RNA polymerase complex. We previously used high-throughput RNA sequencing coupled with crosslinking immunoprecipitation (HITS-CLIP) to examine where NP binds to the viral RNA (vRNA) and demonstrated for two H1N1 strains that NP binds vRNA in a non-uniform, non-random manner. In this study, we expand on those initial observations and describe the NP-vRNA binding profile for a seasonal H3N2 and influenza B virus. We show that, similar to H1N1 strains, NP binds vRNA in a non-uniform and non-random manner. Each viral gene segment has a unique NP binding profile with areas that are enriched for NP association as well as free of NP-binding. Interestingly, NP-vRNA binding profiles have some conservation between influenza A viruses, H1N1 and H3N2, but no correlation was observed between influenza A and B viruses. Our study demonstrates the conserved nature of non-uniform NP binding within influenza viruses. Mapping of the NP-bound vRNA segments provides information on the flexible NP regions that may be involved in facilitating assembly

Variation in leaf transcriptome responses to elevated ozone corresponds with physiological sensitivity to ozone across maize inbred lines

All FASTA files used for BLAST analyses and all BLAST results for https://doi.org/10.1093/genetics/iyac080 are included. Descriptions of each file can be found in README_maize_genetics_2022_zenodo.csv and further information about the content of the files can be found on github.Peer reviewe