Deep Ancestral Introgression Shapes Evolutionary History of Dragonflies and Damselflies.

Introgression is an important biological process affecting at least 10% of the extant species in the animal kingdom. Introgression significantly impacts inference of phylogenetic species relationships where a strictly binary tree model cannot adequately explain reticulate net-like species relationships. Here, we use phylogenomic approaches to understand patterns of introgression along the evolutionary history of a unique, nonmodel insect system: dragonflies and damselflies (Odonata). We demonstrate that introgression is a pervasive evolutionary force across various taxonomic levels within Odonata. In particular, we show that the morphologically "intermediate" species of Anisozygoptera (one of the three primary suborders within Odonata besides Zygoptera and Anisoptera), which retain phenotypic characteristics of the other two suborders, experienced high levels of introgression likely coming from zygopteran genomes. Additionally, we find evidence for multiple cases of deep inter-superfamilial ancestral introgression. [Gene flow; Odonata; phylogenomics; reticulate evolution.].

Overcoming the loss of blue sensitivity through opsin duplication in the largest animal group, beetles.

Opsin proteins are fundamental components of animal vision whose structure largely determines the sensitivity of visual pigments to different wavelengths of light. Surprisingly little is known about opsin evolution in beetles, even though they are the most species rich animal group on Earth and exhibit considerable variation in visual system sensitivities. We reveal the patterns of opsin evolution across 62 beetle species and relatives. Our results show that the major insect opsin class (SW) that typically confers sensitivity to "blue" wavelengths was lost ~300 million years ago, before the origin of modern beetles. We propose that UV and LW opsin gene duplications have restored the potential for trichromacy (three separate channels for colour vision) in beetles up to 12 times and more specifically, duplications within the UV opsin class have likely led to the restoration of "blue" sensitivity up to 10 times. This finding reveals unexpected plasticity within the insect visual system and highlights its remarkable ability to evolve and adapt to the available light and visual cues present in the environment.

The OGCleaner: filtering false-positive homology clusters.

Detecting homologous sequences in organisms is an essential step in protein structure and function prediction, gene annotation and phylogenetic tree construction. Heuristic methods are often employed for quality control of putative homology clusters. These heuristics, however, usually only apply to pairwise sequence comparison and do not examine clusters as a whole. We present the Orthology Group Cleaner (the OGCleaner), a tool designed for filtering putative orthology groups as homology or non-homology clusters by considering all sequences in a cluster. The OGCleaner relies on high-quality orthologous groups identified in OrthoDB to train machine learning algorithms that are able to distinguish between true-positive and false-positive homology groups. This package aims to improve the quality of phylogenetic tree construction especially in instances of lower-quality transcriptome assemblies. AVAILABILITY AND IMPLEMENTATION: https://github.com/byucsl/ogcleaner CONTACT: sfujimoto@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

Opsins have evolved under the permanent heterozygote model: insights from phylotranscriptomics of Odonata.

Gene duplication plays a central role in adaptation to novel environments by providing new genetic material for functional divergence and evolution of biological complexity. Several evolutionary models have been proposed for gene duplication to explain how new gene copies are preserved by natural selection, but these models have rarely been tested using empirical data. Opsin proteins, when combined with a chromophore, form a photopigment that is responsible for the absorption of light, the first step in the phototransduction cascade. Adaptive gene duplications have occurred many times within the animal opsins' gene family, leading to novel wavelength sensitivities. Consequently, opsins are an attractive choice for the study of gene duplication evolutionary models. Odonata (dragonflies and damselflies) have the largest opsin repertoire of any insect currently known. Additionally, there is tremendous variation in opsin copy number between species, particularly in the long-wavelength-sensitive (LWS) class. Using comprehensive phylotranscriptomic and statistical approaches, we tested various evolutionary models of gene duplication. Our results suggest that both the blue-sensitive (BS) and LWS opsin classes were subjected to strong positive selection that greatly weakens after multiple duplication events, a pattern that is consistent with the permanent heterozygote model. Due to the immense interspecific variation and duplicability potential of opsin genes among odonates, they represent a unique model system to test hypotheses regarding opsin gene duplication and diversification at the molecular level.

A novel approach for multi-SNP GWAS and its application in Alzheimer's disease.

BACKGROUND: Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer's disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype. RESULTS: Based on the GWAS statistical model, we developed a multi-SNP GWAS analysis to identify pairs of variants whose common occurrence signaled the Alzheimer's disease phenotype. CONCLUSIONS: Despite not having sufficient data to demonstrate significance, our preliminary experimentation identified a high correlation between GRIA3 and HLA-DRB5 (an AD gene). GRIA3 has not been previously reported in association with AD, but is known to play a role in learning and memory.

Detecting false positive sequence homology: a machine learning approach.

BACKGROUND: Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. There are many existing heuristic tools, most commonly based on bidirectional BLAST searches that are used to identify homologous genes and combine them into two fundamentally distinct classes: orthologs and paralogs. Due to only using heuristic filtering based on significance score cutoffs and having no cluster post-processing tools available, these methods can often produce multiple clusters constituting unrelated (non-homologous) sequences. Therefore sequencing data extracted from incomplete genome/transcriptome assemblies originated from low coverage sequencing or produced by de novo processes without a reference genome are susceptible to high false positive rates of homology detection. RESULTS: In this paper we develop biologically informative features that can be extracted from multiple sequence alignments of putative homologous genes (orthologs and paralogs) and further utilized in context of guided experimentation to verify false positive outcomes. We demonstrate that our machine learning method trained on both known homology clusters obtained from OrthoDB and randomly generated sequence alignments (non-homologs), successfully determines apparent false positives inferred by heuristic algorithms especially among proteomes recovered from low-coverage RNA-seq data. Almost ~42 % and ~25 % of predicted putative homologies by InParanoid and HaMStR respectively were classified as false positives on experimental data set. CONCLUSIONS: Our process increases the quality of output from other clustering algorithms by providing a novel post-processing method that is both fast and efficient at removing low quality clusters of putative homologous genes recovered by heuristic-based approaches.

ScaffoldScaffolder: solving contig orientation via bidirected to directed graph reduction.

MOTIVATION: The contig orientation problem, which we formally define as the MAX-DIR problem, has at times been addressed cursorily and at times using various heuristics. In setting forth a linear-time reduction from the MAX-CUT problem to the MAX-DIR problem, we prove the latter is NP-complete. We compare the relative performance of a novel greedy approach with several other heuristic solutions. RESULTS: Our results suggest that our greedy heuristic algorithm not only works well but also outperforms the other algorithms due to the nature of scaffold graphs. Our results also demonstrate a novel method for identifying inverted repeats and inversion variants, both of which contradict the basic single-orientation assumption. Such inversions have previously been noted as being difficult to detect and are directly involved in the genetic mechanisms of several diseases. AVAILABILITY AND IMPLEMENTATION: http://bioresearch.byu.edu/scaffoldscaffolder. CONTACT: paulmbodily@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.