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Abstract PhyloFisher is a software package written primarily in Python3 that can be used for the creation, analysis, and visualization of phylogenomic datasets that consist of protein sequences from eukaryotic organisms. Unlike many existing phylogenomic pipelines, PhyloFisher comes with a manually curated database of 240 protein‐coding genes, a subset of a previous phylogenetic dataset sampled from 304 eukaryotic taxa. The software package can also utilize a user‐created database of eukaryotic proteins, which may be more appropriate for shallow evolutionary questions. PhyloFisher is also equipped with a set of utilities to aid in running routine analyses, such as the prediction of alternative genetic codes, removal of genes and/or taxa based on occupancy/completeness of the dataset, testing for amino acid compositional heterogeneity among sequences, removal of heterotachious and/or fast‐evolving sites, removal of fast‐evolving taxa, supermatrix creation from randomly resampled genes, and supermatrix creation from nucleotide sequences. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Constructing a phylogenomic dataset Basic Protocol 2: Performing phylogenomic analyses Support Protocol 1: Installing PhyloFisher Support Protocol 2: Creating a custom phylogenomic database 

Abstract Members of eustigmatophyte algae, especially Nannochloropsis and Microchloropsis, have been tapped for biofuel production owing to their exceptionally high lipid content. Although extensive genomic, transcriptomic, and synthetic biology toolkits have been made available for Nannochloropsis and Microchloropsis, very little is known about other eustigmatophytes. Here we present three near-chromosomal and gapless genome assemblies of Monodopsis strains C73 and C141 (60 Mb) and Vischeria strain C74 (106 Mb), which are the sister groups to Nannochloropsis and Microchloropsis in the order Eustigmatales. These genomes contain unusually high percentages of simple repeats, ranging from 12% to 21% of the total assembly size. Unlike Nannochloropsis and Microchloropsis, long interspersed nuclear element repeats are abundant in Monodopsis and Vischeria and might constitute the centromeric regions. We found that both mevalonate and nonmevalonate pathways for terpenoid biosynthesis are present in Monodopsis and Vischeria, which is different from Nannochloropsis and Microchloropsis that have only the latter. Our analysis further revealed extensive spliced leader trans-splicing in Monodopsis and Vischeria at 36–61% of genes. Altogether, the high-quality genomes of Monodopsis and Vischeria not only serve as the much-needed outgroups to advance Nannochloropsis and Microchloropsis research, but also shed new light on the biology and evolution of eustigmatophyte algae. 

Phylogenomic analyses of hundreds of protein-coding genes aimed at resolving phylogenetic relationships is now a common practice. However, no software currently exists that includes tools for dataset construction and subsequent analysis with diverse validation strategies to assess robustness. Furthermore, there are no publicly available high-quality curated databases designed to assess deep (>100 million years) relationships in the tree of eukaryotes. To address these issues, we developed an easy-to-use software package, PhyloFisher ( https://github.com/TheBrownLab/PhyloFisher ), written in Python 3. PhyloFisher includes a manually curated database of 240 protein-coding genes from 304 eukaryotic taxa covering known eukaryotic diversity, a novel tool for ortholog selection, and utilities that will perform diverse analyses required by state-of-the-art phylogenomic investigations. Through phylogenetic reconstructions of the tree of eukaryotes and of the Saccharomycetaceae clade of budding yeasts, we demonstrate the utility of the PhyloFisher workflow and the provided starting database to address phylogenetic questions across a large range of evolutionary time points for diverse groups of organisms. We also demonstrate that undetected paralogy can remain in phylogenomic “single-copy orthogroup” datasets constructed using widely accepted methods such as all vs. all BLAST searches followed by Markov Cluster Algorithm (MCL) clustering and application of automated tree pruning algorithms. Finally, we show how the PhyloFisher workflow helps detect inadvertent paralog inclusions, allowing the user to make more informed decisions regarding orthology assignments, leading to a more accurate final dataset.