Submitted:
08 September 2026
Posted:
09 September 2026
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Abstract
Phylogenomics is a rapidly evolving field that increasingly relies on large-scale multiple sequence aligned (MSA) datasets to investigate the evolutionary processes shaping genes, genomes, and species. Such evolutionary genomics analysis workflows, usually call for the post alignment operation on the multiple aligned sequences to draw conclusion on the evolutionary and phylogenomic landscape of the species of interest. Although numerous tools are available for sequence alignment and automated trimming, comparatively fewer solutions provide flexible, large-scale curation of MSA. Such operations include targeted site filtering, region-specific editing, indel normalization, alignment merging, block replacement, and colour-coded visualization. Here, I present phyevkit, a high-throughput toolkit for the manipulation, curation, conversion, and visualization of multiple sequence alignments. Rather than replacing established alignment and trimming programs, phyevkit is designed to complement them by providing a unified environment for downstream alignment processing. The toolkit supports fine-grained alignment operations such as site- and block-level filtering, alignment merging, extraction of site-specific alignments, indel processing, format conversion, and HTML-based visualization. Implemented in Rust, phyevkit is designed to provide memory safety, efficient execution, and native concurrency for large-scale datasets. Its command-line architecture enables reproducible processing of population-scale, pangenomic, and genome-scale alignment datasets without requiring users to manage parallelization explicitly. phyevkit is freely available as open-source software from https://codeberg.org/gsablok/phyevkit.
Keywords:
bioinformatics
; genomics
; evolution
; phylogenomics
; software
Introduction
At the core of most phylogenomic workflows is the multiple sequence alignments (MSAs). The quality of an MSAs can directly influence downstream analyses, including phylogenetic reconstruction, molecular dating, ancestral-state reconstruction, and evolutionary rates. Misaligned regions, excessive gaps, and ambiguously homologous positions can introduce systematic biases into phylogenetic inference and estimates of evolutionary parameters. Several computational tools have been developed to address alignment processing at different stages of phylogenomic workflows. BMGE (Block Mapping and Gathering with Entropy), for example, uses entropy-based criteria together with BLOSUM and PAM substitution matrices to identify and retain phylogenetically informative regions while excluding sites associated with ambiguous homology (Criscuolo and Gribaldo, 2010). MACSE provides a complementary solution for protein-coding sequences by explicitly accounting for frameshifts and internal stop codons, thereby enabling the generation of biologically interpretable, codon-aware alignments in the presence of sequencing or assembly errors (Ranwez et al., 2011; Ranwez et al., 2018). Such approaches have become important components of phylogenomic workflows because they improve the quality and biological interpretability of alignments before they can be manually edited for the phylogenetic inference.
Following multiple sequence alignment, alignment trimming represents another important stage of phylogenomic data processing. Conventional trimming approaches frequently use user-defined thresholds based on gap content, conservation, or similarity to remove potentially unreliable columns. Although effective in many applications, such criteria can be somewhat arbitrary and may not explicitly account for the phylogenetic information retained within individual sites. ClipKIT, in contrast, introduced a site-retention framework that explicitly identifies and retains parsimony-informative sites rather than simply removing sites considered to be noisy or poorly conserved. Such approaches can improve phylogenomic inference under challenging conditions, including datasets containing highly divergent taxa, sparse sequence coverage, and heterogeneous evolutionary rates (Steenwyk et al., 2020; Steenwyk et al., 2025).
Despite the availability of sophisticated alignment and trimming tools, a practical gap remains between automated alignment processing and the subsequent fine-grained curation of MSAs. Once an alignment has been generated and trimmed, researchers frequently need to perform additional operations, such as filtering specific sites or taxa, modifying defined alignment regions, standardizing indel representations, merging alignments from independent loci, replacing problematic alignment blocks, extracting subsets of alignments, or inspecting conserved and variable regions through visual representations. These operations are often performed through combinations of independent command-line programs, custom scripts, general-purpose bioinformatics libraries, or graphical alignment editors. Such approaches can become cumbersome for genome-scale datasets and may reduce reproducibility, when multiple heterogeneous tools are required.
Here, I present phyevkit (Table 1) designed to facilitate the efficient manipulation, curation, inspection, and conversion of MSAs at scale. phyevkit is not intended to replace established alignment or trimming algorithms; rather, it provides a complementary processing layer between multiple sequence alignment and downstream phylogenomic analyses. The toolkit integrates alignment filtering, block-based processing, site extraction, alignment merging, motif searching, format conversion, and colour-coded visualization within a single command-line framework. Implemented in Rust version 1.91.0 and designed around concurrent execution, phyevkit is intended for high-throughput processing of gene-, marker-, population-, and genome-scale datasets (Supplementary Table). An important distinction is that phyevkit focuses not exclusively on global alignment trimming, but also on the practical curation steps that commonly follow alignment generation. In a typical phylogenomic analysis, a researcher may need to filter specific sites or taxa, edit particular alignment regions, replace or standardize indel characters, merge alignments originating from independent loci or sequencing datasets, substitute problematic blocks, or inspect regions of conservation and sequence variation. Although these operations are technically straightforward, performing them reproducibly across hundreds or thousands of alignments can require substantial scripting or manual intervention. phyevkit addresses these requirements by providing these operations within a unified command-line environment.
Overall, the design of phyevkit is intended to provide a drop-in post alignment-processing layer that complements established alignment-generation and trimming programs. Its primary contribution is therefore not a replacement for algorithms such as trimAl, ClipKIT, BMGE, or MACSE, but the consolidation of downstream alignment manipulation, inspection, filtering, extraction, visualization, and conversion within a single, parallelized command-line framework, which is operative on the post MSAs.
Materials and Methods
Software Implementation and Architecture
phyevkit is implemented in Rust (version 1.91.0) and uses the Tokio runtime and Rayon parallel threading to support asynchronous and concurrent execution and developed on DELL laptop using Ubuntu Linux with core i5 and 8GB RAM. A command-line interface (CLI) was implemented using the clap crate, providing a consistent interface to the individual modules and analytical functions of the toolkit.The computational architecture is designed to support parallel processing of large alignment datasets. Alignment-processing and search operations are implemented with multithreading wherever appropriate, enabling high-throughput analysis of population-scale and genome-scale sequence data without requiring users to explicitly manage computational concurrency. This design is intended to improve computational efficiency on conventional multi-core systems and to reduce the processing bottleneck associated with large-scale alignment curation. phyevkit integrates multiple post multiple sequence alignment-processing operations within a single executable, thereby reducing the need to combine independent command-line utilities and custom scripts. The resulting workflow can be incorporated into automated bioinformatics pipelines and across large collections of gene or marker alignments.phyevkit is released as open-source software through https://codeberg.org/gsablok/phyevkit
Results and Discussion
Large-scale phylogenomic analyses require a diverse set of computational operations, with multiple sequence alignment representing a central component of many workflows. As the scale and heterogeneity of phylogenomic datasets increase, researchers increasingly require tools not only for generating multiple sequence alignment but also for subsequent filtering, inspection, transformation, and visualization. phyevkit is not intended towards providing multiple sequence alignment algorithms but as a post multiple sequence alignment package that brings together all the options needed for structuring MSAs for reproducible phylogenomic workflows. phyevkit additionally supports conversion between commonly used alignment formats, including conversion of FASTA alignments to PHYLIP and NEXUS formats. It is worth to illustrate that trimAl provides automated alignment trimming based on gap and similarity criteria (Capella-Gutiérrez et al., 2009), whereas ClipKIT focuses on retaining phylogenetically informative sites (Steenwyk et al., 2020; Steenwyk et al., 2025). CIAlign provides customizable cleaning and visualization of alignment rows and columns (Tumescheit et al., 2022), while COATi provides statistical pairwise alignment of protein-coding sequences (García Mesa et al., 2024). TreeTuner, in turn, addresses redundancy and complexity in large phylogenetic datasets (Zhang et al., 2022). These tools therefore addresses distinct components of the broader alignment-processing workflow ranging from MSAs algorithm to post alignment MSAs.
Block-Based Filtering and Site-Level Extraction
Among the phyevkit subcommands (Figure 1), filter-block provides filtering based on collinearity blocks, thereby extending alignment processing beyond conventional column-wise conservation or gap-content criteria. This functionality is particularly relevant to comparative-genomic and pangenomic datasets, where large lineage-specific insertions, rearrangements, and other structural differences may complicate conventional column-based filtering. In such datasets, the biological interpretation of an alignment region may depend not only on the conservation of individual columns but also on the structural and collinear organization of the corresponding genomic regions.
This approach complements existing alignment-trimming strategies such as trimAl, which primarily evaluates alignment columns using gap and similarity criteria and ClipKIT emphasizes the retention of phylogenetically informative sites, block-based filtering can operate at a larger structural scale. It therefore provides an alternative mechanism for identifying and retaining biologically relevant alignment regions in datasets characterized by substantial genomic variation (Table 2). Jaccard overlap of retained columns: phyevkit’s filter-all is very similar to trimAl -gappyout (0.97) — makes sense, both are lightly conservative. filter-block overlaps moderately with trimAl’s stricter modes (~0.70 with strict/automated1). But these aren’t really the same algorithm family: trimAl trims based on gap/similarity thresholds per-column; phyevkit’s filter-all keeps variable (non-invariant) sites — a different biological criterion (parsimony-informative sites, not “well-aligned” sites) — and filter-block uses coarse fixed-size majority-vote blocks rather than per-column scoring. phyevkit has been developed on DELL XPS13 with corei5 and 8 GB RAM and took into account the computing needs of low to high end computers. A benchmark of the phyevkit computing on 8 GB RAM has been provided (Supplementary Table S1).
The site-alignment functionality addresses a different downstream requirement by enabling extraction of individual alignment sites for applications such as linkage disequilibrium (LD) analysis. This extends the scope of the toolkit beyond conventional phylogenetic alignment processing toward population-genomic applications. Whereas most alignment-trimming programs are primarily designed to optimize datasets for phylogenetic reconstruction, site-level extraction can facilitate subsequent analyses based on nucleotide variation among populations or individuals. Together, these functions broaden the utility of phyevkit from conventional gene-based alignment processing toward integrated gene-, marker-, comparative-genomic, and population-genomic workflows.
Alignment Visualization and Quality Control
Visualization represents another important component of alignment curation. Although dedicated alignment viewers such as Jalview (Waterhouse et al., 2009) and AliView (Larsson, 2014) provide comprehensive graphical environments for inspecting sequence alignments, incorporating visualization directly into a command-line workflow can be advantageous when large numbers of alignments must be inspected or when visualization is required as part of an automated pipeline. phyevkit provides colour-coded visualization through functions including nucleotidecolour, proteomecolour, alignment-html, alignment-view, and alignment-clipview. These functions allow users to inspect nucleotide and protein alignments and to generate HTML-based representations that can be readily viewed in standard web browsers. The sub-alignment-html functionality additionally permits users to extract and visualize selected regions of an alignment without requiring a separate clipping or visualization program (Figure 1). Such functionality is particularly useful for rapid quality control, identification of conserved or variable regions, inspection of alignment boundaries, and preparation of alignment figures for downstream analysis or publication. By integrating visualization with command-line processing, phyevkit can also facilitate reproducible generation of alignment views as part of scripted workflows.
Conclusions
Phylogenomic analyses increasingly depend on large collections of gene, marker, and genome-scale alignments, creating a growing need for efficient and reproducible alignment-curation workflows. Existing software provides powerful solutions for alignment generation, trimming, and specialized forms of sequence processing, but researchers often still rely on combinations of independent utilities and custom scripts for subsequent alignment manipulation and inspection. phyevkit addresses this practical gap by integrating alignment filtering, block-based processing, site extraction, merging, motif searching, format conversion, and colour-coded visualization within a single Rust-based command-line toolkit. Its concurrent architecture is intended to support high-throughput processing of large alignment collections while reducing the computational and scripting overhead associated with downstream curation.
Supplementary Materials
The following supporting information can be downloaded at the website of this paper posted on Preprints.org.
Acknowledgments
GS thanks biorender.com for helping making the figures for publication quality.
References
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Table 1.
Sub-commands of phyevkit along with the application and feature.
| Module / subcommand | Functionality | Input / output | Primary application | Distinguishing feature |
| filter-block | Filters alignments according to predefined collinearity blocks | Alignment + block information → filtered alignment | Comparative genomics, pangenomics, whole-genome alignments | Enables block-level filtering rather than relying exclusively on individual alignment-column scores |
| site-alignment | Extracts individual alignment sites or variant positions | MSA → site-level alignment | Population genomics and linkage-disequilibrium (LD) analyses | Extends alignment processing toward site-based population-genomic analyses |
| nucleotidecolour | Generates colour-coded nucleotide alignment representations | Nucleotide MSA → coloured alignment | Alignment inspection and quality control | Provides rapid visual assessment of nucleotide conservation and variation |
| proteomecolour | Generates colour-coded protein alignment representations | Protein MSA → coloured alignment | Protein alignment inspection and quality control | Facilitates visual assessment of amino-acid conservation and divergence |
| alignment-html | Converts an alignment into an interactive/readable HTML representation | MSA → HTML | Alignment inspection, quality control, data sharing | Allows alignment visualization directly in a web browser without a dedicated desktop viewer |
| sub-alignment-html | Extracts and renders a selected region of an alignment | MSA + selected region → HTML | Region-specific inspection and figure preparation | Enables visualization of alignment subsets without external clipping software |
| alignment-view | Displays alignment information for inspection | MSA → visual/textual representation | Alignment quality control | Provides direct alignment inspection within the phyevkit workflow |
| alignment-clipview | Generates a clipped representation of an alignment | MSA + selected region → clipped visualization | Targeted alignment inspection | Facilitates examination of specific alignment regions |
| Alignment filtering | Removes user-defined sites, regions, or sequence components | MSA → filtered MSA | Alignment curation before phylogenetic inference | Supports fine-grained curation beyond conventional global trimming |
| Alignment merging | Combines multiple alignments into a single dataset | Multiple MSAs → merged MSA | Multi-locus and phylogenomic analyses | Consolidates alignment datasets within a single CLI workflow |
| Motif search | Searches alignments for sequence motifs or patterns | MSA + motif/query → matching sites or regions | Comparative genomics and functional sequence analysis | Integrates sequence-pattern searches with alignment processing |
| Alignment statistics | Calculates descriptive statistics from alignments | MSA → summary statistics | Alignment QC and dataset characterization | Provides alignment-level information before downstream inference |
| FASTA → PHYLIP | Converts FASTA alignments to PHYLIP format | FASTA → PHYLIP | Phylogenetic workflows | Removes the need for a separate format-conversion utility |
| FASTA → NEXUS | Converts FASTA alignments to NEXUS format | FASTA → NEXUS | Phylogenetic and evolutionary analyses | Facilitates compatibility with downstream phylogenetic software |
| Multithreaded processing | Parallelizes alignment-processing operations | Large alignment collections → processed datasets | Genome- and population-scale analyses | Designed for high-throughput processing on multi-core systems |
| Rust-based CLI | Provides a compiled command-line framework | Command + input dataset → processed output | Reproducible bioinformatics workflows | Combines native execution, memory safety, and concurrent processing in a single executable |
Table 2.
Comparative analysis of trimal and phyevkit.
| Method | Columns kept (% of 471) |
| trimAl -gappyout | 466 (98.9%) |
| trimAl -strict/-strictplus/-automated1 | 365 (identical to each other) (77.5%) |
| trimAl -nogaps | 191 (40.6%) |
| phyevkit filter-all (keeps variable columns) | 463 (98.3%) |
| phyevkit filter-block 20 | 260 (55.2%) |
Benchmarked against trimal 011.AA.YKL197C.fasta, which is the protein alignment that trimal uses for benchamrking. To allow for the comparative benchmark, same datasets was used across trimal and phyevkit.
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