M. Stanley Fujimoto

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.

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

BackgroundGenome-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.ResultsBased 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.ConclusionsDespite 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.

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.

The OGCleaner: filtering false-positive homology clusters

Summary: 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.com Supplementary information: Supplementary data are available at Bioinformatics online.

Effects of error-correction of heterozygous next-generation sequencing data

BackgroundError correction is an important step in increasing the quality of next-generation sequencing data for downstream analysis and use. Polymorphic datasets are a challenge for many bioinformatic software packages that are designed for or assume homozygosity of an input dataset. This assumption ignores the true genomic composition of many organisms that are diploid or polyploid. In this survey, two different error correction packages, Quake and ECHO, are examined to see how they perform on next-generation sequence data from heterozygous genomes.ResultsQuake and ECHO perform well and were able to correct many errors found within the data. However, errors that occur at heterozygous positions had unique trends. Errors at these positions were sometimes corrected incorrectly, introducing errors into the dataset with the possibility of creating a chimeric read. Quake was much less likely to create chimeric reads. Quakes read trimming removed a large portion of the original data and often left reads with few heterozygous markers. ECHO resulted in more chimeric reads and introduced more errors than Quake but preserved heterozygous markers.Using real E. coli sequencing data and their assemblies after error correction, the assembly statistics improved. It was also found that segregating reads by haplotype can improve the quality of an assembly.ConclusionsThese findings suggest that Quake and ECHO both have strengths and weaknesses when applied to heterozygous data. With the increased interest in haplotype specific analysis, new tools that are designed to be haplotype-aware are necessary that do not have the weaknesses of Quake and ECHO.

Haplotype-centered mapping for improved alignments and genetic association studies

Next-Generation Sequencing experiments have been used to identify genotypes that are associated with many medical conditions. An important part of Next Generation read processing is the mapping of short reads to a reference genome. Although many algorithms have been created to perform this mapping, there are many reads that cannot be mapped because they are sequenced from low complexity regions of the genome (repeat regions) or from regions that are divergent from the reference genome. This research shows that when reads are first assembled into longer contigs that are then mapped to the reference genome, mapping efficiency and accuracy increases. When two contigs map to the same location, the contigs can provide haplotype information that can be used to perform association studies based on phased SNPs on a haplotype.

The PepSeq Pipeline: Software for Antimicrobial Motif Discovery in Randomly-Generated Peptide Libraries

Bacteria with resistance genes are becoming ever more common, and new methods of discovering antibiotics are being developed. One of these new methods involves researchers creating random peptides and testing their antimicrobial activity. Developing antibiotics from these peptides requires understanding which sequence motifs will be toxic to bacteria. To determine if the toxic peptides of a randomly-generated peptide library can be uniquely classified based solely on sequence motifs, we created the PepSeq Pipeline: a new software that utilizes a Random Forest algorithm to extract motifs from a peptide library. We found that this pipeline can accurately classify 56% of the toxic peptides in the peptide library using motifs extracted from the model. Testing on simulated data with less noise, we could classify up to 94% of the toxic peptides. The pipeline extracted significant toxic motifs in every library that was tested, but its ability to classify all toxic peptides depended on the number of motifs in the library. Once extracted, these motifs can be used both to understand the biology behind why certain peptides are toxic and to create novel antibiotics. The code and data used in this analysis can be found at https://github.com/tjense25/pep-seq-pipeline.