multiple-alignment

Name: multiple-alignment
Author: GPTomics

1.1kstars185forks

How to install multiple-alignment?

gptomics/bioskills/multiple-alignment

$npx -y skills add GPTomics/bioSkills --skill multiple-alignment

Installs into the current project.

Summary

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 an

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SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: MAFFT 7.520+, MUSCLE 5.1+, ClustalOmega 1.2.4+, T-Coffee 13+, PAL2NAL 14+, BioPython 1.83+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- CLI: `mafft --version`, `muscle -version`, `clustalo --version`
7	- Python: `pip show biopython` then `help(module.function)` to check signatures
8
9	If code throws errors, introspect the installed tool and adapt the example to match the actual CLI flags rather than retrying.
10
11	# Multiple Sequence Alignment
12
13	"Align multiple sequences" -> Compute an optimal alignment of three or more homologous sequences using progressive, iterative, or consistency-based methods.
14	- CLI: `mafft` (most versatile), `muscle` (highest accuracy), `clustalo` (scales well), `t_coffee` (consistency-based)
15	- Python: `subprocess.run()` wrapping CLI tools; BioPython `Bio.Align.Applications` was removed in BioPython 1.86 (verify with `pip show biopython`); use `subprocess` directly
16
17	## MSA Algorithm Taxonomy
18
19	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:
20
21	\| Family \| Representative tools \| Best at \| Fails when \|
22	\|--------\|---------------------\|---------\|-----------\|
23	\| Progressive \| ClustalW, MAFFT FFT-NS-2 \| Fast, large datasets, similar lengths \| Early-stage gap errors propagate; no recovery \|
24	\| Iterative refinement \| MAFFT L-INS-i, MUSCLE3, PRRN \| Recovers from progressive errors at <2000 seqs \| Slow on >2000; still guide-tree dependent \|
25	\| Consistency-based \| T-Coffee, ProbCons \| Highest accuracy <100 seqs; integrates evidence \| O(N^2 to N^4) scaling; heavy compute \|
26	\| HMM-based \| HMMER hmmalign, ClustalOmega (HHalign), UPP, WITCH \| Adding sequences to a curated profile; fragmentary input \| Needs an existing high-quality profile or backbone \|
27	\| Divide-and-conquer \| PASTA, MAGUS, MUSCLE5 super5 \| Heterogeneous large datasets (>10k seqs) \| Sub-alignment merges can introduce artefacts \|
28	\| Structure or pLM-informed \| Foldmason, PROMALS3D, vcMSA, 3D-Coffee \| Dark proteome, <15% identity, dataset has structures \| Requires structures or a working pLM \|
29
30	## Tool Selection
31
32	Pick by dataset size and divergence; the default recommendations follow the table.
33
34	\| Tool \| Best For \| Max Sequences \| Accuracy \| Speed \|
35	\|------\|----------\|---------------\|----------\|-------\|
36	\| MAFFT L-INS-i \| Highest accuracy, <200 seqs \| ~200 \| Highest \| Slow \|
37	\| MAFFT FFT-NS-2 \| Large datasets, good balance \| ~50,000 \| Good \| Fast \|
38	\| MAFFT E-INS-i \| Sequences with long unalignable internal regions \| ~200 \| High \| Slow \|
39	\| MUSCLE5 (PPP `-align`) \| Benchmarked highest accuracy on Balifam-10000 \| ~1000 \| Highest \| Medium \|
40	\| MUSCLE5 (`-super5`) \| Large datasets via mBed clustering \| ~100,000+ \| Good \| Medium \|
41	\| ClustalOmega \| Very large datasets, HMM-based profiles \| ~190,000 in published benchmark (Sievers et al 2011 Mol Syst Biol) \| Good \| Fast \|
42	\| T-Coffee (default) \| Small datasets needing maximum accuracy \| ~200 \| Highest \| Slowest \|
43
44	Default recommendation: MAFFT L-INS-i for <200 sequences; MAFFT FFT-NS-2 or MUSCLE5 super5 for thousands; MUSCLE5 ensemble (`-stratified`) when alignment confidence estimates are needed.
45
46	### Cross-Aligner Sensitivity Check
47
48	When downstream analysis depends on a specific column (a candidate selection site, a contact-prediction position), run BOTH MAFFT L-INS-i and MUSCLE5 `-align` and verify that column is stable across the two outputs. If unstable, flag as low-confidence regardless of GUIDANCE2/TCS scores. The MUSCLE5 ensemble (`-stratified`/`-diversified`) accomplishes the same check directly within one tool and is preferred when ensemble output is acceptable downstream.
49
50	### Beyond MAFFT and MUSCLE: Scale and Domain
51
52	Some workloads exceed what the four main tools handle gracefully. Use the scale-and-domain table below to escape default-tool blind spots.
53
54	\| Scenario \| Recommended tool \| Why \|
55	\|----------\|------------------\|-----\|
56	\| 100k - millions of sequences (UniRef cluster reps) \| FAMSA v2 (Deorowicz et al 2016 SREP, v2 2024) \| Million-scale MSA in hours with Pareto-optimal accuracy \|
57	\| Heterogeneous large dataset (variable length, divergence) \| PASTA (Mirarab et al 2015 J Comp Biol) or MAGUS (Smirnov & Warnow 2021 Bioinf) \| Divide-and-conquer plus iterative re-alignment \|
58	\| Fragmentary input (metagenomics, eDNA, ancient DNA) \| UPP (Nguyen et al 2015 Genome Biol) or WITCH (Shen et al 2022 J Comp Biol) \| HMM backbone tolerates partial sequences \|
59	\| Dark proteome / <15% identity \| vcM

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multiple-alignment

1.1kstars185forks

View on GitHub

How to install multiple-alignment?

gptomics/bioskills/multiple-alignment

$npx -y skills add GPTomics/bioSkills --skill multiple-alignment

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: MAFFT 7.520+, MUSCLE 5.1+, ClustalOmega 1.2.4+, T-Coffee 13+, PAL2NAL 14+, BioPython 1.83+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- CLI: `mafft --version`, `muscle -version`, `clustalo --version`
7	- Python: `pip show biopython` then `help(module.function)` to check signatures
8
9	If code throws errors, introspect the installed tool and adapt the example to match the actual CLI flags rather than retrying.
10
11	# Multiple Sequence Alignment
12
13	"Align multiple sequences" -> Compute an optimal alignment of three or more homologous sequences using progressive, iterative, or consistency-based methods.
14	- CLI: `mafft` (most versatile), `muscle` (highest accuracy), `clustalo` (scales well), `t_coffee` (consistency-based)
15	- Python: `subprocess.run()` wrapping CLI tools; BioPython `Bio.Align.Applications` was removed in BioPython 1.86 (verify with `pip show biopython`); use `subprocess` directly
16
17	## MSA Algorithm Taxonomy
18
19	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:
20
21	\| Family \| Representative tools \| Best at \| Fails when \|
22	\|--------\|---------------------\|---------\|-----------\|
23	\| Progressive \| ClustalW, MAFFT FFT-NS-2 \| Fast, large datasets, similar lengths \| Early-stage gap errors propagate; no recovery \|
24	\| Iterative refinement \| MAFFT L-INS-i, MUSCLE3, PRRN \| Recovers from progressive errors at <2000 seqs \| Slow on >2000; still guide-tree dependent \|
25	\| Consistency-based \| T-Coffee, ProbCons \| Highest accuracy <100 seqs; integrates evidence \| O(N^2 to N^4) scaling; heavy compute \|
26	\| HMM-based \| HMMER hmmalign, ClustalOmega (HHalign), UPP, WITCH \| Adding sequences to a curated profile; fragmentary input \| Needs an existing high-quality profile or backbone \|
27	\| Divide-and-conquer \| PASTA, MAGUS, MUSCLE5 super5 \| Heterogeneous large datasets (>10k seqs) \| Sub-alignment merges can introduce artefacts \|
28	\| Structure or pLM-informed \| Foldmason, PROMALS3D, vcMSA, 3D-Coffee \| Dark proteome, <15% identity, dataset has structures \| Requires structures or a working pLM \|
29
30	## Tool Selection
31
32	Pick by dataset size and divergence; the default recommendations follow the table.
33
34	\| Tool \| Best For \| Max Sequences \| Accuracy \| Speed \|
35	\|------\|----------\|---------------\|----------\|-------\|
36	\| MAFFT L-INS-i \| Highest accuracy, <200 seqs \| ~200 \| Highest \| Slow \|
37	\| MAFFT FFT-NS-2 \| Large datasets, good balance \| ~50,000 \| Good \| Fast \|
38	\| MAFFT E-INS-i \| Sequences with long unalignable internal regions \| ~200 \| High \| Slow \|
39	\| MUSCLE5 (PPP `-align`) \| Benchmarked highest accuracy on Balifam-10000 \| ~1000 \| Highest \| Medium \|
40	\| MUSCLE5 (`-super5`) \| Large datasets via mBed clustering \| ~100,000+ \| Good \| Medium \|
41	\| ClustalOmega \| Very large datasets, HMM-based profiles \| ~190,000 in published benchmark (Sievers et al 2011 Mol Syst Biol) \| Good \| Fast \|
42	\| T-Coffee (default) \| Small datasets needing maximum accuracy \| ~200 \| Highest \| Slowest \|
43
44	Default recommendation: MAFFT L-INS-i for <200 sequences; MAFFT FFT-NS-2 or MUSCLE5 super5 for thousands; MUSCLE5 ensemble (`-stratified`) when alignment confidence estimates are needed.
45
46	### Cross-Aligner Sensitivity Check
47
48	When downstream analysis depends on a specific column (a candidate selection site, a contact-prediction position), run BOTH MAFFT L-INS-i and MUSCLE5 `-align` and verify that column is stable across the two outputs. If unstable, flag as low-confidence regardless of GUIDANCE2/TCS scores. The MUSCLE5 ensemble (`-stratified`/`-diversified`) accomplishes the same check directly within one tool and is preferred when ensemble output is acceptable downstream.
49
50	### Beyond MAFFT and MUSCLE: Scale and Domain
51
52	Some workloads exceed what the four main tools handle gracefully. Use the scale-and-domain table below to escape default-tool blind spots.
53
54	\| Scenario \| Recommended tool \| Why \|
55	\|----------\|------------------\|-----\|
56	\| 100k - millions of sequences (UniRef cluster reps) \| FAMSA v2 (Deorowicz et al 2016 SREP, v2 2024) \| Million-scale MSA in hours with Pareto-optimal accuracy \|
57	\| Heterogeneous large dataset (variable length, divergence) \| PASTA (Mirarab et al 2015 J Comp Biol) or MAGUS (Smirnov & Warnow 2021 Bioinf) \| Divide-and-conquer plus iterative re-alignment \|
58	\| Fragmentary input (metagenomics, eDNA, ancient DNA) \| UPP (Nguyen et al 2015 Genome Biol) or WITCH (Shen et al 2022 J Comp Biol) \| HMM backbone tolerates partial sequences \|
59	\| Dark proteome / <15% identity \| vcM

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