Estimating transcriptome complexities across eukaryotes.

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.

Comparative physical genome mapping of malaria vectors Anopheles sinensis and Anopheles gambiae.

BACKGROUND: Anopheles sinensis is a dominant natural vector of Plasmodium vivax in China, Taiwan, Japan, and Korea. Recent genome sequencing of An. sinensis provides important insights into the genomic basis of vectorial capacity. However, the lack of a physical genome map with chromosome assignment and orientation of sequencing scaffolds hinders comparative analyses with other genomes to infer evolutionary changes relevant to the vector capacity. RESULTS: Here, a physical genome map for An. sinensis was constructed by assigning 52 scaffolds onto the chromosomes using fluorescence in situ hybridization (FISH). This chromosome-based genome assembly composes approximately 36% of the total An. sinensis genome. Comparisons of 3955 orthologous genes between An. sinensis and Anopheles gambiae identified 361 conserved synteny blocks and 267 inversions fixed between these two lineages. The rate of gene order reshuffling on the X chromosome is approximately 3.2 times higher than that on the autosomes. CONCLUSIONS: The physical map will facilitate detailed genomic analysis of An. sinensis and contribute to understanding of the patterns and mechanisms of large-scale genome rearrangements in anopheline mosquitoes.

New alignment-based sequence extraction software (ALiBaSeq) and its utility for deep level phylogenetics.

Despite many bioinformatic solutions for analyzing sequencing data, few options exist for targeted sequence retrieval from whole genomic sequencing (WGS) data with the ultimate goal of generating a phylogeny. Available tools especially struggle at deep phylogenetic levels and necessitate amino-acid space searches, which may increase rates of false positive results. Many tools are also difficult to install and may lack adequate user resources. Here, we describe a program that uses freely available similarity search tools to find homologs in assembled WGS data with unparalleled freedom to modify parameters. We evaluate its performance compared to other commonly used bioinformatics tools on two divergent insect species (>200 My) for which annotated genomes exist, and on one large set each of highly conserved and more variable loci. Our software is capable of retrieving orthologs from well-curated or unannotated, low or high depth shotgun, and target capture assemblies as well or better than other software as assessed by recovering the most genes with maximal coverage and with a low rate of false positives throughout all datasets. When assessing this combination of criteria, ALiBaSeq is frequently the best evaluated tool for gathering the most comprehensive and accurate phylogenetic alignments on all types of data tested. The software (implemented in Python), tutorials, and manual are freely available at https://github.com/AlexKnyshov/alibaseq.