scikit-bio

Name: scikit-bio
Author: LeonChaoX

698stars61forks

How to install scikit-bio?

leonchaox/qinyan-academic-skills/scikit-bio

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill scikit-bio

Installs into the current project.

Summary

Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

Files · 1

View on GitHub

SKILL.md

1	# scikit-bio
2
3	## Overview
4
5	scikit-bio is a comprehensive Python library for working with biological data. Apply this skill for bioinformatics analyses spanning sequence manipulation, alignment, phylogenetics, microbial ecology, and multivariate statistics.
6
7	## When to Use This Skill
8
9	This skill should be used when the user:
10	- Works with biological sequences (DNA, RNA, protein)
11	- Needs to read/write biological file formats (FASTA, FASTQ, GenBank, Newick, BIOM, etc.)
12	- Performs sequence alignments or searches for motifs
13	- Constructs or analyzes phylogenetic trees
14	- Calculates diversity metrics (alpha/beta diversity, UniFrac distances)
15	- Performs ordination analysis (PCoA, CCA, RDA)
16	- Runs statistical tests on biological/ecological data (PERMANOVA, ANOSIM, Mantel)
17	- Analyzes microbiome or community ecology data
18	- Works with protein embeddings from language models
19	- Needs to manipulate biological data tables
20
21	## Core Capabilities
22
23	### 1. Sequence Manipulation
24
25	Work with biological sequences using specialized classes for DNA, RNA, and protein data.
26
27	Key operations:
28	- Read/write sequences from FASTA, FASTQ, GenBank, EMBL formats
29	- Sequence slicing, concatenation, and searching
30	- Reverse complement, transcription (DNA→RNA), and translation (RNA→protein)
31	- Find motifs and patterns using regex
32	- Calculate distances (Hamming, k-mer based)
33	- Handle sequence quality scores and metadata
34
35	Common patterns:
36	```python
37	import skbio
38
39	# Read sequences from file
40	seq = skbio.DNA.read('input.fasta')
41
42	# Sequence operations
43	rc = seq.reverse_complement()
44	rna = seq.transcribe()
45	protein = rna.translate()
46
47	# Find motifs
48	motif_positions = seq.find_with_regex('ATG[ACGT]{3}')
49
50	# Check for properties
51	has_degens = seq.has_degenerates()
52	seq_no_gaps = seq.degap()
53	```
54
55	Important notes:
56	- Use `DNA`, `RNA`, `Protein` classes for grammared sequences with validation
57	- Use `Sequence` class for generic sequences without alphabet restrictions
58	- Quality scores automatically loaded from FASTQ files into positional metadata
59	- Metadata types: sequence-level (ID, description), positional (per-base), interval (regions/features)
60
61	### 2. Sequence Alignment
62
63	Perform pairwise and multiple sequence alignments using dynamic programming algorithms.
64
65	Key capabilities:
66	- Global alignment (Needleman-Wunsch with semi-global variant)
67	- Local alignment (Smith-Waterman)
68	- Configurable scoring schemes (match/mismatch, gap penalties, substitution matrices)
69	- CIGAR string conversion
70	- Multiple sequence alignment storage and manipulation with `TabularMSA`
71
72	Common patterns:
73	```python
74	from skbio.alignment import local_pairwise_align_ssw, TabularMSA
75
76	# Pairwise alignment
77	alignment = local_pairwise_align_ssw(seq1, seq2)
78
79	# Access aligned sequences
80	msa = alignment.aligned_sequences
81
82	# Read multiple alignment from file
83	msa = TabularMSA.read('alignment.fasta', constructor=skbio.DNA)
84
85	# Calculate consensus
86	consensus = msa.consensus()
87	```
88
89	Important notes:
90	- Use `local_pairwise_align_ssw` for local alignments (faster, SSW-based)
91	- Use `StripedSmithWaterman` for protein alignments
92	- Affine gap penalties recommended for biological sequences
93	- Can convert between scikit-bio, BioPython, and Biotite alignment formats
94
95	### 3. Phylogenetic Trees
96
97	Construct, manipulate, and analyze phylogenetic trees representing evolutionary relationships.
98
99	Key capabilities:
100	- Tree construction from distance matrices (UPGMA, WPGMA, Neighbor Joining, GME, BME)
101	- Tree manipulation (pruning, rerooting, traversal)
102	- Distance calculations (patristic, cophenetic, Robinson-Foulds)
103	- ASCII visualization
104	- Newick format I/O
105
106	Common patterns:
107	```python
108	from skbio import TreeNode
109	from skbio.tree import nj
110
111	# Read tree from file
112	tree = TreeNode.read('tree.nwk')
113
114	# Construct tree from distance matrix
115	tree = nj(distance_matrix)
116
117	# Tree operations
118	subtree = tree.shear(['taxon1', 'taxon2', 'taxon3'])
119	tips = [node for node in tree.tips()]
120	lca = tree.lowest_common_ancestor(['taxon1', 'taxon2'])
121
122	# Calculate distances
123	patristic_dist = tree.find('taxon1').distance(tree.find('taxon2'))
124	cophenetic_matrix = tree.cophenetic_matrix()
125
126	# Compare trees
127	rf_distance = tree.robinson_foulds(other_tree)
128	```
129
130	Important notes:
131	- Use `nj()` for neighbor joining (classic phylogenetic method)
132	- Use `upgma()` for UPGMA (assumes molecular clock)
133	- GME and BME are highly scalable for large trees
134	- Trees can be rooted or unrooted; some metrics require specific rooting
135
136	### 4. Diversity Analysis
137
138	Calculate alpha and beta diversity metrics for microbial ecology and community analysis.
139
140	Key capabilities:
141	- Alpha diversity: richness, Shannon entropy, Simpson index, Faith's PD, Pielou's evenness
142	- Beta diversity: Bray

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scikit-bio

698stars61forks

View on GitHub

How to install scikit-bio?

leonchaox/qinyan-academic-skills/scikit-bio

$npx -y skills add LeonChaoX/qinyan-academic-skills --skill scikit-bio

Installs into the current project.

Summary

Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

Files · 1

View on GitHub

SKILL.md

1	# scikit-bio
2
3	## Overview
4
5	scikit-bio is a comprehensive Python library for working with biological data. Apply this skill for bioinformatics analyses spanning sequence manipulation, alignment, phylogenetics, microbial ecology, and multivariate statistics.
6
7	## When to Use This Skill
8
9	This skill should be used when the user:
10	- Works with biological sequences (DNA, RNA, protein)
11	- Needs to read/write biological file formats (FASTA, FASTQ, GenBank, Newick, BIOM, etc.)
12	- Performs sequence alignments or searches for motifs
13	- Constructs or analyzes phylogenetic trees
14	- Calculates diversity metrics (alpha/beta diversity, UniFrac distances)
15	- Performs ordination analysis (PCoA, CCA, RDA)
16	- Runs statistical tests on biological/ecological data (PERMANOVA, ANOSIM, Mantel)
17	- Analyzes microbiome or community ecology data
18	- Works with protein embeddings from language models
19	- Needs to manipulate biological data tables
20
21	## Core Capabilities
22
23	### 1. Sequence Manipulation
24
25	Work with biological sequences using specialized classes for DNA, RNA, and protein data.
26
27	Key operations:
28	- Read/write sequences from FASTA, FASTQ, GenBank, EMBL formats
29	- Sequence slicing, concatenation, and searching
30	- Reverse complement, transcription (DNA→RNA), and translation (RNA→protein)
31	- Find motifs and patterns using regex
32	- Calculate distances (Hamming, k-mer based)
33	- Handle sequence quality scores and metadata
34
35	Common patterns:
36	```python
37	import skbio
38
39	# Read sequences from file
40	seq = skbio.DNA.read('input.fasta')
41
42	# Sequence operations
43	rc = seq.reverse_complement()
44	rna = seq.transcribe()
45	protein = rna.translate()
46
47	# Find motifs
48	motif_positions = seq.find_with_regex('ATG[ACGT]{3}')
49
50	# Check for properties
51	has_degens = seq.has_degenerates()
52	seq_no_gaps = seq.degap()
53	```
54
55	Important notes:
56	- Use `DNA`, `RNA`, `Protein` classes for grammared sequences with validation
57	- Use `Sequence` class for generic sequences without alphabet restrictions
58	- Quality scores automatically loaded from FASTQ files into positional metadata
59	- Metadata types: sequence-level (ID, description), positional (per-base), interval (regions/features)
60
61	### 2. Sequence Alignment
62
63	Perform pairwise and multiple sequence alignments using dynamic programming algorithms.
64
65	Key capabilities:
66	- Global alignment (Needleman-Wunsch with semi-global variant)
67	- Local alignment (Smith-Waterman)
68	- Configurable scoring schemes (match/mismatch, gap penalties, substitution matrices)
69	- CIGAR string conversion
70	- Multiple sequence alignment storage and manipulation with `TabularMSA`
71
72	Common patterns:
73	```python
74	from skbio.alignment import local_pairwise_align_ssw, TabularMSA
75
76	# Pairwise alignment
77	alignment = local_pairwise_align_ssw(seq1, seq2)
78
79	# Access aligned sequences
80	msa = alignment.aligned_sequences
81
82	# Read multiple alignment from file
83	msa = TabularMSA.read('alignment.fasta', constructor=skbio.DNA)
84
85	# Calculate consensus
86	consensus = msa.consensus()
87	```
88
89	Important notes:
90	- Use `local_pairwise_align_ssw` for local alignments (faster, SSW-based)
91	- Use `StripedSmithWaterman` for protein alignments
92	- Affine gap penalties recommended for biological sequences
93	- Can convert between scikit-bio, BioPython, and Biotite alignment formats
94
95	### 3. Phylogenetic Trees
96
97	Construct, manipulate, and analyze phylogenetic trees representing evolutionary relationships.
98
99	Key capabilities:
100	- Tree construction from distance matrices (UPGMA, WPGMA, Neighbor Joining, GME, BME)
101	- Tree manipulation (pruning, rerooting, traversal)
102	- Distance calculations (patristic, cophenetic, Robinson-Foulds)
103	- ASCII visualization
104	- Newick format I/O
105
106	Common patterns:
107	```python
108	from skbio import TreeNode
109	from skbio.tree import nj
110
111	# Read tree from file
112	tree = TreeNode.read('tree.nwk')
113
114	# Construct tree from distance matrix
115	tree = nj(distance_matrix)
116
117	# Tree operations
118	subtree = tree.shear(['taxon1', 'taxon2', 'taxon3'])
119	tips = [node for node in tree.tips()]
120	lca = tree.lowest_common_ancestor(['taxon1', 'taxon2'])
121
122	# Calculate distances
123	patristic_dist = tree.find('taxon1').distance(tree.find('taxon2'))
124	cophenetic_matrix = tree.cophenetic_matrix()
125
126	# Compare trees
127	rf_distance = tree.robinson_foulds(other_tree)
128	```
129
130	Important notes:
131	- Use `nj()` for neighbor joining (classic phylogenetic method)
132	- Use `upgma()` for UPGMA (assumes molecular clock)
133	- GME and BME are highly scalable for large trees
134	- Trees can be rooted or unrooted; some metrics require specific rooting
135
136	### 4. Diversity Analysis
137
138	Calculate alpha and beta diversity metrics for microbial ecology and community analysis.
139
140	Key capabilities:
141	- Alpha diversity: richness, Shannon entropy, Simpson index, Faith's PD, Pielou's evenness
142	- Beta diversity: Bray

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6 picks

Type