msa-parsing

Name: msa-parsing
Author: GPTomics

1.1kstars185forks

How to install msa-parsing?

gptomics/bioskills/msa-parsing

$npx -y skills add GPTomics/bioSkills --skill msa-parsing

Installs into the current project.

Summary

Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.

Files · 1

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SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: BioPython 1.83+, numpy 1.26+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7
8	If code throws ImportError, AttributeError, or TypeError, introspect the installed
9	package and adapt the example to match the actual API rather than retrying.
10
11	# MSA Parsing and Analysis
12
13	Parse multiple sequence alignments to extract information, analyze content, and prepare for downstream analysis.
14
15	## Required Import
16
17	Goal: Load modules for parsing, analyzing, and manipulating multiple sequence alignments.
18
19	Approach: Import AlignIO for reading, Counter for column analysis, and alignment classes for constructing modified alignments.
20
21	```python
22	from Bio import AlignIO
23	from Bio.Align import MultipleSeqAlignment
24	from Bio.SeqRecord import SeqRecord
25	from Bio.Seq import Seq
26	from collections import Counter
27	import numpy as np
28	import pandas as pd
29	```
30
31	Optional for streaming and Easel-based weighting:
32	```python
33	import pyhmmer
34	```
35
36	## Loading Alignments
37
38	Goal: Read an MSA file and inspect its dimensions.
39
40	Approach: Use `AlignIO.read()` specifying the file and format.
41
42	```python
43	from Bio import AlignIO
44
45	alignment = AlignIO.read('alignment.fasta', 'fasta')
46	print(f'{len(alignment)} sequences, {alignment.get_alignment_length()} columns')
47	```
48
49	## Extracting Sequence Information
50
51	### Get All Sequence IDs
52	```python
53	seq_ids = [record.id for record in alignment]
54	```
55
56	### Get Sequences as Strings
57	```python
58	sequences = [str(record.seq) for record in alignment]
59	```
60
61	### Get Sequence by ID
62	```python
63	def get_sequence_by_id(alignment, seq_id):
64	for record in alignment:
65	if record.id == seq_id:
66	return record
67	return None
68
69	target = get_sequence_by_id(alignment, 'species_A')
70	```
71
72	### Access Descriptions and Annotations
73	```python
74	for record in alignment:
75	print(f'ID: {record.id}')
76	print(f'Description: {record.description}')
77	print(f'Annotations: {record.annotations}')
78	```
79
80	## Column-wise Analysis
81
82	Goal: Analyze alignment content column by column to assess composition, conservation, and variability.
83
84	Approach: Use column indexing (`alignment[:, idx]`) and Counter to examine character frequencies at each position.
85
86	### Get Single Column
87	```python
88	column_5 = alignment[:, 5] # Returns string of characters at position 5
89	print(column_5) # e.g., 'AAAGA'
90	```
91
92	API note: `Bio.AlignIO` returns `MultipleSeqAlignment` objects whose `[:, idx]` returns a plain `str`; `[:, start:end]` returns another `MultipleSeqAlignment`. The newer `Bio.Align.Alignment` (from `Align.read` / `Align.parse`) uses numpy-backed slicing -- verify with `type(alignment[:, 0])` before assuming string methods work. For numpy-array access to the full alignment, use `np.array(alignment)`.
93
94	### Iterate and Count Columns
95	```python
96	for col_idx in range(alignment.get_alignment_length()):
97	column = alignment[:, col_idx]
98	counts = Counter(column)
99	```
100
101	### Find Conserved Positions
102	```python
103	def find_conserved_positions(alignment, threshold=1.0):
104	conserved = []
105	for col_idx in range(alignment.get_alignment_length()):
106	column = alignment[:, col_idx]
107	counts = Counter(column)
108	most_common_char, most_common_count = counts.most_common(1)[0]
109	if most_common_char != '-':
110	conservation = most_common_count / len(alignment)
111	if conservation >= threshold:
112	conserved.append((col_idx, most_common_char))
113	return conserved
114
115	fully_conserved = find_conserved_positions(alignment, threshold=1.0)
116	mostly_conserved = find_conserved_positions(alignment, threshold=0.8)
117	```
118
119	## Gap Analysis
120
121	Goal: Quantify gap distribution across sequences and columns to identify problematic regions or sequences.
122
123	Approach: Count gap characters per sequence and per column, then identify positions exceeding a gap fraction threshold.
124
125	### Count Gaps Per Sequence
126	```python
127	gap_counts = [(record.id, str(record.seq).count('-')) for record in alignment]
128	for seq_id, gaps in gap_counts:
129	print(f'{seq_id}: {gaps} gaps')
130	```
131
132	### Count Gaps Per Column
133	```python
134	def gaps_per_column(alignment):
135	return [alignment[:, i].count('-') for i in range(alignment.get_alignment_length())]
136
137	gap_profile = gaps_per_column(alignment)
138	```
139
140	### Find Gappy Columns
141	```python
142	def find_gappy_columns(alignment, threshold=0.5):
143	gappy = []
144	num_seqs = len(alignment)
145	for col_idx in range(alignment.get_alignment_length()):
146	column = alignment[:, col_idx]
147	gap_fraction = column.c

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msa-parsing

1.1kstars185forks

View on GitHub

How to install msa-parsing?

gptomics/bioskills/msa-parsing

$npx -y skills add GPTomics/bioSkills --skill msa-parsing

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	## Version Compatibility
2
3	Reference examples tested with: BioPython 1.83+, numpy 1.26+
4
5	Before using code patterns, verify installed versions match. If versions differ:
6	- Python: `pip show <package>` then `help(module.function)` to check signatures
7
8	If code throws ImportError, AttributeError, or TypeError, introspect the installed
9	package and adapt the example to match the actual API rather than retrying.
10
11	# MSA Parsing and Analysis
12
13	Parse multiple sequence alignments to extract information, analyze content, and prepare for downstream analysis.
14
15	## Required Import
16
17	Goal: Load modules for parsing, analyzing, and manipulating multiple sequence alignments.
18
19	Approach: Import AlignIO for reading, Counter for column analysis, and alignment classes for constructing modified alignments.
20
21	```python
22	from Bio import AlignIO
23	from Bio.Align import MultipleSeqAlignment
24	from Bio.SeqRecord import SeqRecord
25	from Bio.Seq import Seq
26	from collections import Counter
27	import numpy as np
28	import pandas as pd
29	```
30
31	Optional for streaming and Easel-based weighting:
32	```python
33	import pyhmmer
34	```
35
36	## Loading Alignments
37
38	Goal: Read an MSA file and inspect its dimensions.
39
40	Approach: Use `AlignIO.read()` specifying the file and format.
41
42	```python
43	from Bio import AlignIO
44
45	alignment = AlignIO.read('alignment.fasta', 'fasta')
46	print(f'{len(alignment)} sequences, {alignment.get_alignment_length()} columns')
47	```
48
49	## Extracting Sequence Information
50
51	### Get All Sequence IDs
52	```python
53	seq_ids = [record.id for record in alignment]
54	```
55
56	### Get Sequences as Strings
57	```python
58	sequences = [str(record.seq) for record in alignment]
59	```
60
61	### Get Sequence by ID
62	```python
63	def get_sequence_by_id(alignment, seq_id):
64	for record in alignment:
65	if record.id == seq_id:
66	return record
67	return None
68
69	target = get_sequence_by_id(alignment, 'species_A')
70	```
71
72	### Access Descriptions and Annotations
73	```python
74	for record in alignment:
75	print(f'ID: {record.id}')
76	print(f'Description: {record.description}')
77	print(f'Annotations: {record.annotations}')
78	```
79
80	## Column-wise Analysis
81
82	Goal: Analyze alignment content column by column to assess composition, conservation, and variability.
83
84	Approach: Use column indexing (`alignment[:, idx]`) and Counter to examine character frequencies at each position.
85
86	### Get Single Column
87	```python
88	column_5 = alignment[:, 5] # Returns string of characters at position 5
89	print(column_5) # e.g., 'AAAGA'
90	```
91
92	API note: `Bio.AlignIO` returns `MultipleSeqAlignment` objects whose `[:, idx]` returns a plain `str`; `[:, start:end]` returns another `MultipleSeqAlignment`. The newer `Bio.Align.Alignment` (from `Align.read` / `Align.parse`) uses numpy-backed slicing -- verify with `type(alignment[:, 0])` before assuming string methods work. For numpy-array access to the full alignment, use `np.array(alignment)`.
93
94	### Iterate and Count Columns
95	```python
96	for col_idx in range(alignment.get_alignment_length()):
97	column = alignment[:, col_idx]
98	counts = Counter(column)
99	```
100
101	### Find Conserved Positions
102	```python
103	def find_conserved_positions(alignment, threshold=1.0):
104	conserved = []
105	for col_idx in range(alignment.get_alignment_length()):
106	column = alignment[:, col_idx]
107	counts = Counter(column)
108	most_common_char, most_common_count = counts.most_common(1)[0]
109	if most_common_char != '-':
110	conservation = most_common_count / len(alignment)
111	if conservation >= threshold:
112	conserved.append((col_idx, most_common_char))
113	return conserved
114
115	fully_conserved = find_conserved_positions(alignment, threshold=1.0)
116	mostly_conserved = find_conserved_positions(alignment, threshold=0.8)
117	```
118
119	## Gap Analysis
120
121	Goal: Quantify gap distribution across sequences and columns to identify problematic regions or sequences.
122
123	Approach: Count gap characters per sequence and per column, then identify positions exceeding a gap fraction threshold.
124
125	### Count Gaps Per Sequence
126	```python
127	gap_counts = [(record.id, str(record.seq).count('-')) for record in alignment]
128	for seq_id, gaps in gap_counts:
129	print(f'{seq_id}: {gaps} gaps')
130	```
131
132	### Count Gaps Per Column
133	```python
134	def gaps_per_column(alignment):
135	return [alignment[:, i].count('-') for i in range(alignment.get_alignment_length())]
136
137	gap_profile = gaps_per_column(alignment)
138	```
139
140	### Find Gappy Columns
141	```python
142	def find_gappy_columns(alignment, threshold=0.5):
143	gappy = []
144	num_seqs = len(alignment)
145	for col_idx in range(alignment.get_alignment_length()):
146	column = alignment[:, col_idx]
147	gap_fraction = column.c

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