phylogenetics

Name: phylogenetics
Author: LeonChaoX

698stars61forks

How to install phylogenetics?

leonchaox/qinyan-academic-skills/phylogenetics

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill phylogenetics

Installs into the current project.

Summary

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studi

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SKILL.md

1	# Phylogenetics
2
3	## Overview
4
5	Phylogenetic analysis reconstructs the evolutionary history of biological sequences (genes, proteins, genomes) by inferring the branching pattern of descent. This skill covers the standard pipeline:
6
7	1. MAFFT — Multiple sequence alignment
8	2. IQ-TREE 2 — Maximum likelihood tree inference with model selection
9	3. FastTree — Fast approximate maximum likelihood (for large datasets)
10	4. ETE3 — Python library for tree manipulation and visualization
11
12	Installation:
13	```bash
14	# Conda (recommended for CLI tools)
15	conda install -c bioconda mafft iqtree fasttree
16	pip install ete3
17	```
18
19	## When to Use This Skill
20
21	Use phylogenetics when:
22
23	- Evolutionary relationships: Which organism/gene is most closely related to my sequence?
24	- Viral phylodynamics: Trace outbreak spread and estimate transmission dates
25	- Protein family analysis: Infer evolutionary relationships within a gene family
26	- Horizontal gene transfer detection: Identify genes with discordant species/gene trees
27	- Ancestral sequence reconstruction: Infer ancestral protein sequences
28	- Molecular clock analysis: Estimate divergence dates using temporal sampling
29	- GWAS companion: Place variants in evolutionary context (e.g., SARS-CoV-2 variants)
30	- Microbiology: Species phylogeny from 16S rRNA or core genome phylogeny
31
32	## Standard Workflow
33
34	### 1. Multiple Sequence Alignment with MAFFT
35
36	```python
37	import subprocess
38	import os
39
40	def run_mafft(input_fasta: str, output_fasta: str, method: str = "auto",
41	n_threads: int = 4) -> str:
42	"""
43	Align sequences with MAFFT.
44
45	Args:
46	input_fasta: Path to unaligned FASTA file
47	output_fasta: Path for aligned output
48	method: 'auto' (auto-select), 'einsi' (accurate), 'linsi' (accurate, slow),
49	'fftnsi' (medium), 'fftns' (fast), 'retree2' (fast)
50	n_threads: Number of CPU threads
51
52	Returns:
53	Path to aligned FASTA file
54	"""
55	methods = {
56	"auto": ["mafft", "--auto"],
57	"einsi": ["mafft", "--genafpair", "--maxiterate", "1000"],
58	"linsi": ["mafft", "--localpair", "--maxiterate", "1000"],
59	"fftnsi": ["mafft", "--fftnsi"],
60	"fftns": ["mafft", "--fftns"],
61	"retree2": ["mafft", "--retree", "2"],
62	}
63
64	cmd = methods.get(method, methods["auto"])
65	cmd += ["--thread", str(n_threads), "--inputorder", input_fasta]
66
67	with open(output_fasta, 'w') as out:
68	result = subprocess.run(cmd, stdout=out, stderr=subprocess.PIPE, text=True)
69
70	if result.returncode != 0:
71	raise RuntimeError(f"MAFFT failed:\n{result.stderr}")
72
73	# Count aligned sequences
74	with open(output_fasta) as f:
75	n_seqs = sum(1 for line in f if line.startswith('>'))
76	print(f"MAFFT: aligned {n_seqs} sequences → {output_fasta}")
77
78	return output_fasta
79
80	# MAFFT method selection guide:
81	# Few sequences (<200), accurate: linsi or einsi
82	# Many sequences (<1000), moderate: fftnsi
83	# Large datasets (>1000): fftns or auto
84	# Ultra-fast (>10000): mafft --retree 1
85	```
86
87	### 2. Trim Alignment (Optional but Recommended)
88
89	```python
90	def trim_alignment_trimal(aligned_fasta: str, output_fasta: str,
91	method: str = "automated1") -> str:
92	"""
93	Trim poorly aligned columns with TrimAl.
94
95	Methods:
96	- 'automated1': Automatic heuristic (recommended)
97	- 'gappyout': Remove gappy columns
98	- 'strict': Strict gap threshold
99	"""
100	cmd = ["trimal", f"-{method}", "-in", aligned_fasta, "-out", output_fasta, "-fasta"]
101	result = subprocess.run(cmd, capture_output=True, text=True)
102	if result.returncode != 0:
103	print(f"TrimAl warning: {result.stderr}")
104	# Fall back to using the untrimmed alignment
105	import shutil
106	shutil.copy(aligned_fasta, output_fasta)
107	return output_fasta
108	```
109
110	### 3. IQ-TREE 2 — Maximum Likelihood Tree
111
112	```python
113	def run_iqtree(aligned_fasta: str, output_prefix: str,
114	model: str = "TEST", bootstrap: int = 1000,
115	n_threads: int = 4, extra_args: list = None) -> dict:
116	"""
117	Build a maximum likelihood tree with IQ-TREE 2.
118
119	Args:
120	aligned_fasta: Aligned FASTA file
121	output_prefix: Prefix for output files
122	model: 'TEST' for automatic model selection, or specify (e.g., 'GTR+G' for DNA,
123	'LG+G4' for proteins, 'JTT+G' for proteins)
124	bootstrap: Number of ultrafast bootstrap replicates (1000 recommended)
125	n_threads: Number of threads ('AUTO' to auto-detect)
126	extra_args: Additional IQ-TREE arguments
127
128	Returns:
129	Dict with paths to

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phylogenetics

698stars61forks

View on GitHub

How to install phylogenetics?

leonchaox/qinyan-academic-skills/phylogenetics

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill phylogenetics

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Phylogenetics
2
3	## Overview
4
5	Phylogenetic analysis reconstructs the evolutionary history of biological sequences (genes, proteins, genomes) by inferring the branching pattern of descent. This skill covers the standard pipeline:
6
7	1. MAFFT — Multiple sequence alignment
8	2. IQ-TREE 2 — Maximum likelihood tree inference with model selection
9	3. FastTree — Fast approximate maximum likelihood (for large datasets)
10	4. ETE3 — Python library for tree manipulation and visualization
11
12	Installation:
13	```bash
14	# Conda (recommended for CLI tools)
15	conda install -c bioconda mafft iqtree fasttree
16	pip install ete3
17	```
18
19	## When to Use This Skill
20
21	Use phylogenetics when:
22
23	- Evolutionary relationships: Which organism/gene is most closely related to my sequence?
24	- Viral phylodynamics: Trace outbreak spread and estimate transmission dates
25	- Protein family analysis: Infer evolutionary relationships within a gene family
26	- Horizontal gene transfer detection: Identify genes with discordant species/gene trees
27	- Ancestral sequence reconstruction: Infer ancestral protein sequences
28	- Molecular clock analysis: Estimate divergence dates using temporal sampling
29	- GWAS companion: Place variants in evolutionary context (e.g., SARS-CoV-2 variants)
30	- Microbiology: Species phylogeny from 16S rRNA or core genome phylogeny
31
32	## Standard Workflow
33
34	### 1. Multiple Sequence Alignment with MAFFT
35
36	```python
37	import subprocess
38	import os
39
40	def run_mafft(input_fasta: str, output_fasta: str, method: str = "auto",
41	n_threads: int = 4) -> str:
42	"""
43	Align sequences with MAFFT.
44
45	Args:
46	input_fasta: Path to unaligned FASTA file
47	output_fasta: Path for aligned output
48	method: 'auto' (auto-select), 'einsi' (accurate), 'linsi' (accurate, slow),
49	'fftnsi' (medium), 'fftns' (fast), 'retree2' (fast)
50	n_threads: Number of CPU threads
51
52	Returns:
53	Path to aligned FASTA file
54	"""
55	methods = {
56	"auto": ["mafft", "--auto"],
57	"einsi": ["mafft", "--genafpair", "--maxiterate", "1000"],
58	"linsi": ["mafft", "--localpair", "--maxiterate", "1000"],
59	"fftnsi": ["mafft", "--fftnsi"],
60	"fftns": ["mafft", "--fftns"],
61	"retree2": ["mafft", "--retree", "2"],
62	}
63
64	cmd = methods.get(method, methods["auto"])
65	cmd += ["--thread", str(n_threads), "--inputorder", input_fasta]
66
67	with open(output_fasta, 'w') as out:
68	result = subprocess.run(cmd, stdout=out, stderr=subprocess.PIPE, text=True)
69
70	if result.returncode != 0:
71	raise RuntimeError(f"MAFFT failed:\n{result.stderr}")
72
73	# Count aligned sequences
74	with open(output_fasta) as f:
75	n_seqs = sum(1 for line in f if line.startswith('>'))
76	print(f"MAFFT: aligned {n_seqs} sequences → {output_fasta}")
77
78	return output_fasta
79
80	# MAFFT method selection guide:
81	# Few sequences (<200), accurate: linsi or einsi
82	# Many sequences (<1000), moderate: fftnsi
83	# Large datasets (>1000): fftns or auto
84	# Ultra-fast (>10000): mafft --retree 1
85	```
86
87	### 2. Trim Alignment (Optional but Recommended)
88
89	```python
90	def trim_alignment_trimal(aligned_fasta: str, output_fasta: str,
91	method: str = "automated1") -> str:
92	"""
93	Trim poorly aligned columns with TrimAl.
94
95	Methods:
96	- 'automated1': Automatic heuristic (recommended)
97	- 'gappyout': Remove gappy columns
98	- 'strict': Strict gap threshold
99	"""
100	cmd = ["trimal", f"-{method}", "-in", aligned_fasta, "-out", output_fasta, "-fasta"]
101	result = subprocess.run(cmd, capture_output=True, text=True)
102	if result.returncode != 0:
103	print(f"TrimAl warning: {result.stderr}")
104	# Fall back to using the untrimmed alignment
105	import shutil
106	shutil.copy(aligned_fasta, output_fasta)
107	return output_fasta
108	```
109
110	### 3. IQ-TREE 2 — Maximum Likelihood Tree
111
112	```python
113	def run_iqtree(aligned_fasta: str, output_prefix: str,
114	model: str = "TEST", bootstrap: int = 1000,
115	n_threads: int = 4, extra_args: list = None) -> dict:
116	"""
117	Build a maximum likelihood tree with IQ-TREE 2.
118
119	Args:
120	aligned_fasta: Aligned FASTA file
121	output_prefix: Prefix for output files
122	model: 'TEST' for automatic model selection, or specify (e.g., 'GTR+G' for DNA,
123	'LG+G4' for proteins, 'JTT+G' for proteins)
124	bootstrap: Number of ultrafast bootstrap replicates (1000 recommended)
125	n_threads: Number of threads ('AUTO' to auto-detect)
126	extra_args: Additional IQ-TREE arguments
127
128	Returns:
129	Dict with paths to

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