Project description:Tree House Explorer (THEx) is a genome browser that integrates phylogenomic data and genomic annotations into a single interactive platform for combined analysis. THEx allows users to visualize genome-wide variation in evolutionary histories and genetic divergence on a chromosome-by-chromosome basis, with continuous sliding window comparisons to gene annotations (GFF/GTF), recombination rates, and other user-specified, highly customizable feature annotations. THEx provides a new platform for interactive phylogenomic data visualization to analyze and interpret the diverse evolutionary histories woven throughout genomes. Hosted on Conda, THEx integrates seamlessly into new or pre-existing workflows.
Project description:Plants and photovoltaics share the same purpose as harvesting sunlight. Therefore, botanical studies could lead to new breakthroughs in photovoltaics. However, the basic mechanism of photosynthesis is different to semiconductor-based photovoltaics and the gap between photosynthesis and solar cells must be bridged before we can apply the botanical principles to photovoltaics. In this study, we analysed the role of the fractal structures found in plants in light harvesting based on a simplified model, rotated the structures by 90° and applied them to fractal-structured photovoltaic Si solar cell arrays. Adoption of botanically inspired fractal structures can result in solar cell arrays with omnidirectional properties, and in this case, yielded a 25% enhancement in electric energy production. The fractal structure used in this study was two-dimensional and symmetric; investigating and optimizing three-dimensional asymmetric fractal structures would further enhance the performance of photovoltaics. Furthermore, this study represents only the first step towards the development of a new type of photovoltaics based on botanical principles, and points to further aspects of botanical knowledge that could be exploited, in addition to plant fractal structures. For example, leaf anatomy, phyllotaxis and chloroplastic mechanisms could be applied to the design of new types of photovoltaics.
Project description:Cytosine methylation is a conserved base modification, but explanations for its interspecific variation remain elusive. Only through taxonomic sampling of disparate groups can unifying explanations for interspecific variation be thoroughly tested. Here we leverage phylogenetic resolution of cytosine DNA methyltransferases (DNA MTases) and genome evolution to better understand widespread interspecific variation across 40 diverse fungal species. DNA MTase genotypes have diversified from the ancestral DNMT1+DNMT5 genotype through numerous loss events, and duplications, whereas, DIM-2 and RID-1 are more recently derived in fungi. Methylation is typically enriched at intergenic regions, which includes repeats and transposons. Unlike certain Insecta and Angiosperm species, Fungi lack canonical gene body methylation. Some fungi species possess large clusters of contiguous methylation encompassing many genes, repetitive DNA and transposons, and are not ancient in origin. Broadly, methylation is partially explained by DNA MTase genotype and repetitive DNA content. Basidiomycota on average have the highest level of methylation, and repeat content, compared to other phyla. However, exceptions exist across Fungi. Other traits, including DNA repair mechanisms, might contribute to interspecific methylation variation within Fungi. Our results show mechanism and genome evolution are unifying explanations for interspecific methylation variation across Fungi.
Project description:Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.