Background

Phylogenetic analysis plays a critical role in understanding the evolutionary relationships among species, informing diverse fields such as evolutionary biology, epidemiology, and conservation genetics. The process of generating a phylogenetic tree typically involves key steps including sequence alignment, model selection, and tree inference, each of which is essential for deriving reliable evolutionary conclusions. However, traditional phylogenetic workflows often involve manual sequence alignment and model selection, introducing potential biases and inefficiencies.

To address these challenges, numerous computational tools have been developed. For example, GUIDANCE2 enhances sequence alignment by accounting for alignment uncertainty and evolutionary events such as insertions and deletions [1]. Model selection tools like Protest [2] and MrModeltest2 [3] automate the identification of optimal evolutionary models using statistical criteria such as AIC and BIC, thereby improving the reliability of downstream phylogenetic inferences. Besides, tools such as PAUP* [4] enable comprehensive phylogenetic analysis for nucleotide sequences, while MEGA X [5] facilitates sequence format conversion and preliminary analyses.

Beyond these tools, several non-Bayesian phylogenetic inference methods offer powerful alternatives with distinct advantages. The PHYLIP package [6] provides a comprehensive suite of programs implementing distance matrix, maximum parsimony, and maximum likelihood methods, making it a versatile choice for diverse phylogenetic analyses. Maximum likelihood-based programs like RAxML [7] and IQ-TREE [8] have revolutionized the field with their computational efficiency and accuracy, especially for large datasets. FastTree [9] employs heuristic approaches to construct approximately maximum-likelihood phylogenetic trees with remarkable speed while maintaining reasonable accuracy. PhyML [10] offers robust algorithms for maximum likelihood tree estimation with extensive substitution model options and branch support assessment. These non-Bayesian tools provide complementary strengths to Bayesian methods, often excelling in computational efficiency while still delivering statistically sound phylogenetic inferences.

Bayesian methods, particularly those implemented in MrBayes [11], provide a robust probabilistic framework for estimating phylogenetic trees and evolutionary parameters by incorporating uncertainty and prior knowledge. However, integrating these tools into a cohesive and reproducible workflow remains challenging due to differing format requirements between tools. For example, GUIDANCE2 accepts FASTA/PHYLIP inputs, MrBayes requires NEXUS format [12], and PAUP* demands non-interleaved NEXUS [4,13] for its analyses. These diverging specifications create hidden technical barriers. Our protocol addresses these challenges by presenting a seamless, step-by-step guide that integrates sequence alignment, model selection, and Bayesian inference using MrBayes. It automates critical steps, minimizes manual intervention, reduces potential errors, and ensures reproducibility. Custom Python scripts are included to streamline the parsing of model selection outputs, enhancing data handling efficiency. This structured protocol simplifies the phylogenetic analysis process, improves the accuracy and reliability of results, and is applicable to diverse datasets, including both protein and nucleotide sequences.

The NEXUS format is a common data format for phylogenetic analysis, facilitating greater cooperation in the analysis and visualization of data. PAUP* reads data in NEXUS file format, and all NEXUS files must begin with the declaration "#NEXUS". The Newick format [14] is another widely used format for representing phylogenetic trees, and it is supported by many phylogenetic analysis tools. The protocol leverages MEGA for initial format conversions and PAUP* for format refinement, ensuring seamless data handoffs between tools and preventing pipeline failures from format mismatches. This approach systematically addresses integration challenges and provides a versatile and reliable resource for researchers conducting rigorous evolutionary studies.