系统生物学

In current genomic research, molecular dating is challenged by both imperfect substitution modeling and analysis efficiency, as genome-scale datasets often exhibit substantial rate heterogeneity and complex patterns of sequence evolution, which can make divergence-time estimation sensitive to modeling assumptions and computational settings. Meanwhile, commonly used molecular dating workflows remain operationally demanding; preparing correctly formatted inputs, implementing model settings, configuring fossil calibrations, and performing basic diagnostics and visualization frequently require multiple tools and extensive manual steps, resulting in high hands-on time and avoidable operational errors. To facilitate the practical implementation of molecular dating analyses and lower the operational barrier for users, this protocol describes a GUI-based workflow in PhyloSuite v2 for molecular dating analysis. Using a dataset of fish nuclear genomes as an example, the tutorial covers multi-format data import, visual configuration of fossil calibrations, automatic selection and implementation of substitution models, automation of complex analytical procedures, and assessment of Markov chain Monte Carlo (MCMC) convergence, along with data visualization. Through this protocol, users can quickly master the full workflow—from input preparation and molecular dating to MCMC sample statistical assessment and timetree visualization—thus significantly enhancing the efficiency of molecular dating analysis and result verification.

Bayesian phylogenetic analysis is essential for elucidating evolutionary relationships among organisms. Traditional methods often rely on fixed models and manual parameter settings, which can limit accuracy and efficiency. This protocol presents an integrated workflow that leverages GUIDANCE2 for rigorous sequence alignment, ProtTest and MrModeltest for robust model selection, and MrBayes for phylogenetic tree estimation through Bayesian inference. By automating key steps and providing detailed command-line instructions, this protocol enhances the reliability and reproducibility of phylogenetic studies.

Zebrafish genetic mutants have emerged as a valuable model system for studying various aspects of disease and developmental biology. Mutant zebrafish embryos are generally identified based on phenotypic defects at later developmental stages, making it difficult to investigate underlying molecular mechanisms at earlier stages. This protocol presents a PCR-based genotyping method that enables the identification of wild-type, heterozygous, and homozygous zebrafish genetic mutants at any developmental stage, even when they are phenotypically indistinguishable. The approach involves the amplification of specific genomic regions using carefully designed primers, followed by gel electrophoresis. This genotyping method facilitates the investigation of the molecular mechanisms driving phenotypic defects that are observed at later timepoints. This protocol allows researchers to perform analyses such as immunofluorescence, RT-PCR, RNA sequencing, and other molecular experiments on early developmental stages of mutants. The availability of this protocol expands the utility of zebrafish genetic mutants for elucidating the molecular underpinnings of various biological processes throughout development.

The limited standards for the rigorous and objective use of mitochondrial genomes (mitogenomes) can lead to uncertainties regarding the phylogenetic relationships of taxa under varying evolutionary constraints. The mitogenome exhibits heterogeneity in base composition, and evolutionary rates may vary across different regions, which can cause empirical data to violate assumptions of the applied evolutionary models. Consequently, the unique evolutionary signatures of the dataset must be carefully evaluated before selecting an appropriate approach for phylogenomic inference. Here, we present the bioinformatic pipeline and code used to expand the mitogenome phylogeny of the order Carcharhiniformes (groundsharks), with a focus on houndsharks (Chondrichthyes: Triakidae). We present a rigorous approach for addressing difficult-to-resolve phylogenies, incorporating multi-species coalescent modelling (MSCM) to address gene/species tree discordance. The protocol describes carefully designed approaches for preparing alignments, partitioning datasets, assigning models of evolution, inferring phylogenies based on traditional site-homogenous concatenation approaches as well as under multispecies coalescent and site heterogenous models, and generating statistical data for comparison of different topological outcomes. The datasets required to run our analyses are available on GitHub and Dryad repositories.

Mitochondrial genomes (mitogenomes) display relatively rapid mutation rates, low sequence recombination, high copy numbers, and maternal inheritance patterns, rendering them valuable blueprints for mapping lineages, uncovering historical migration patterns, understanding intraspecific population dynamics, and investigating how environmental pressures shape traits underpinned by genetic variation. Here, we present the bioinformatic pipeline and code used to assemble and annotate the complete mitogenomes of five houndsharks (Chondrichthyes: Triakidae) and compare them to the mitogenomes of other closely related species. We demonstrate the value of a combined assembly approach for detecting deviations in mitogenome structure and describe how to select an assembly approach that best suits the sequencing data. The datasets required to run our analyses are available on the GitHub and Dryad repositories.

In recent years, the increase in genome sequencing across diverse plant species has provided a significant advantage for phylogenomics studies, allowing the analysis of one of the most diverse gene families in plants: nucleotide-binding leucine-rich repeat receptors (NLRs). However, due to the sequence diversity of the NLR gene family, identifying key molecular features and functionally conserved sequence patterns is challenging through multiple sequence alignment. Here, we present a step-by-step protocol for a computational pipeline designed to identify evolutionarily conserved motifs in plant NLR proteins. In this protocol, we use a large-scale NLR dataset, including 1,862 NLR genes annotated from monocot and dicot species, to predict conserved sequence motifs, such as the MADA and EDVID motifs, within the coiled-coil (CC)-NLR subfamily. Our pipeline can be applied to identify molecular signatures that have remained conserved in the gene family over evolutionary time across plant species.

Estimating the time of most recent common ancestor (tMRCA) is important to trace the origin of pathogenic viruses. This analysis is based on the genetic diversity accumulated in a certain time period. There have been thousands of mutant sites occurring in the genomes of SARS-CoV-2 since the COVID-19 pandemic started; six highly linked mutation sites occurred early before the start of the pandemic and can be used to classify the genomes into three main haplotypes. Tracing the origin of those three haplotypes may help to understand the origin of SARS-CoV-2. In this article, we present a complete protocol for the classification of SARS-CoV-2 genomes and calculating tMRCA using Bayesian phylodynamic method. This protocol may also be used in the analysis of other viral genomes.

Key features

• Filtering and alignment of a massive number of viral genomes using custom scripts and ViralMSA.

• Classification of genomes based on highly linked sites using custom scripts.

• Phylodynamic analysis of viral genomes using Bayesian evolutionary analysis sampling trees (BEAST).

• Visualization of posterior distribution of tMRCA using Tracer.v1.7.2.

• Optimized for the SARS-CoV-2.

Graphical overview

Graphical workflow of time of most recent common ancestor (tMRCA) estimation process

The recent surge in plant genomic and transcriptomic data has laid a foundation for reconstructing evolutionary scenarios and inferring potential functions of key genes related to plants’ development and stress responses. The classical scheme for identifying homologous genes is sequence similarity–based searching, under the crucial assumption that homologous sequences are more similar to each other than they are to any other non-homologous sequences. Advances in plant phylogenomics and computational algorithms have enabled us to systemically identify homologs/orthologs and reconstruct their evolutionary histories among distantly related lineages. Here, we present a comprehensive pipeline for homologous sequences identification, phylogenetic relationship inference, and potential functional profiling of genes in plants.

Key features

• Identification of orthologs using large-scale genomic and transcriptomic data.

• This protocol is generalized for analyzing the evolution of plant genes.

COVID-19, the disease caused by the novel SARS-CoV-2 coronavirus, originated as an isolated outbreak in the Hubei province of China but soon created a global pandemic and is now a major threat to healthcare systems worldwide. Following the rapid human-to-human transmission of the infection, institutes around the world have made efforts to generate genome sequence data for the virus. With thousands of genome sequences for SARS-CoV-2 now available in the public domain, it is possible to analyze the sequences and gain a deeper understanding of the disease, its origin, and its epidemiology. Phylogenetic analysis is a potentially powerful tool for tracking the transmission pattern of the virus with a view to aiding identification of potential interventions. Toward this goal, we have created a comprehensive protocol for the analysis and phylogenetic clustering of SARS-CoV-2 genomes using Nextstrain, a powerful open-source tool for the real-time interactive visualization of genome sequencing data. Approaches to focus the phylogenetic clustering analysis on a particular region of interest are detailed in this protocol.