bio-alignment-multiple
Perform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee. Guides tool and algorithm selection based on dataset size, sequence divergence, and downstream application. Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies.
npx skills add BioTender-max/awesome-bio-agent-skills --skill multiple-alignment --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Guides performing multiple sequence alignment of three or more homologous sequences using a choice of tools (MAFFT, MUSCLE5, ClustalOmega, or T-Coffee) and to select algorithms based on dataset size, sequence divergence, and downstream application.
How it works
- Provides a detailed mapping of tools to dataset characteristics and algorithm flags (MAFFT, MUSCLE5, ClustalOmega, T-Coffee).
- Recommends default tools by sequence count and scenario (e.g., MAFFT L-INS-i for <200 sequences; MAFFT FFT-NS-2 or MUSCLE5 for thousands).
- Describes verification steps for tool versions and CLI flags, with commands to check versions and run specific MAFFT algorithms via CLI or Python subprocess wrappers.
- Explains MSA algorithm taxonomy and guidelines to switch tools when a dataset challenges a particular approach.
- Includes sections on MAFFT algorithm selection, explicit flag usage for common scenarios, and basic usage examples including how to add sequences to an existing alignment with MAFFT add/addfragments/addprofile/keeplength options.
- Covers running MUSCLE5 in two modes (-align for up to ~1000 sequences; -super5 for thousands to millions) and describes typical usage.
When to use it
Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies. Choose a tool based on dataset size, divergence, and need for alignment confidence or downstream processing.
What it can touch
- Tools: MAFFT, MUSCLE5, ClustalOmega, T-Coffee.
- Execution via CLI commands and Python subprocess examples for MAFFT.
- Specific flags and commands are quoted exactly as presented in the guidance (e.g., MAFFT flags like --localpair, --maxiterate 1000, --retree, --auto; MUSCLE5 -align and -super5).
Caveats
- The guidance emphasizes verifying installed versions and adapting to actual CLI signatures if they differ from examples (MAFFT, MUSCLE5, etc.).
- It recommends using explicit algorithm specification for publication-quality reproducibility rather than relying on --auto.
- No claims about outcomes; instructions focus on concrete steps, flags, and workflows.
## Version Compatibility Reference examples tested with: MAFFT 7.520+, MUSCLE 5.1+, ClustalOmega 1.2.4+, T-Coffee 13+, PAL2NAL 14+, BioPython 1.83+ Before using code patterns, verify installed versions match. If versions differ: - CLI: `mafft --version`, `muscle -version`, `clustalo --version` - Python: `pip show biopython` then `help(module.function)` to check signatures If code throws errors, introspect the installed tool and adapt the example to match the actual CLI flags rather than retrying. # Multiple Sequence Alignment **"Align multiple sequences"** → Compute an optimal alignment of three or more homologous sequences using progressive, iterative, or consistency-based methods. - CLI: `mafft` (most versatile), `muscle` (highest accuracy), `clustalo` (scales well), `t_coffee` (consistency-based) - Python: `subprocess.run()` wrapping CLI tools; BioPython `Bio.Align.Applications` was removed in BioPython 1.86 (verify with `pip show biopython`); use `subprocess` directly ## MSA Algorithm Taxonomy When a tool is failing on a dataset, switch to a tool from a different algorithmic family rather than tuning flags. The six families and their characteristic failure modes: | Family | Rep
- Version Compatibility
- MSA Algorithm Taxonomy
- Tool Selection
- Cross-Aligner Sensitivity Check
- Beyond MAFFT and MUSCLE: Scale and Domain
- Critical Concepts
- Mitigate Guide-Tree Dependency
- Joint MSA-Phylogeny Co-estimation (Small Datasets Only)
- Sequence Divergence Thresholds
- Running MAFFT
- Algorithm Selection
- What --auto Picks (and Why to Specify Explicitly)
- Basic Usage
- Adding Sequences to an Existing Alignment
Highest accuracy for <200 sequences (local pairwise iterative) mafft --localpair --maxiterate 1000 input.fasta > aligned.fasta Good balance for medium datasets mafft --retree 2 input.fasta > aligned.fasta Auto-select algorithm based on dataset size mafft --auto input.fasta > aligned.fasta Protein alignment with specific matrix (default BLOSUM62) mafft --amino --localpair --maxiterate 1000 input.fasta > aligned.fasta DNA alignment (auto-detected, but can be explicit) mafft --nuc --localpair --maxiterate 1000 input.fasta > aligned.fasta
What does the bio-alignment-multiple skill do?
Perform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee. Guides tool and algorithm selection based on dataset size, sequence divergence, and downstream application. Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill multiple-alignment --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.