| Name | easyfasta JSON |
| Version |
1.0.13
JSON |
| download |
| home_page | None |
| Summary | A lightweight Python library for efficient FASTA file parsing and DNA sequence manipulation. |
| upload_time | 2025-08-08 01:00:58 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
bioinformatics
fasta
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Easy Fasta
A lightweight functional Python library for efficient FASTA file parsing and DNA sequence manipulation. No OOP bloat, only data.
## Features
- **Memory-efficient parsing**: Stream through large FASTA files without loading everything into memory
- **Random access**: Jump directly to specific sequences with position tracking
- **Sequence extraction**: Filter sequences by identifiers
- **DNA manipulation**: Complete IUPAC-compliant complement and reverse complement operations
- **Formatting**: Convert sequences to multi-line FASTA format
- **Does not validate input**: user are responsible to provide correctly formatted file.
## Installation
python 3.8+
```
> pip install easyfasta
```
or simply copy the module to your project
## Quick Start
```python
from easyfasta import *
# Parse FASTA file sequence by sequence (memory efficient)
with open('sequences.fasta') as f:
for header, sequence in fasta_iter(f):
print(f">{header}")
print(sequence[:50]) # First 50 bases
# Load entire FASTA into dictionary
sequences = load_fasta('sequences.fasta')
print(sequences['sequence_id'])
# Extract specific sequences
target_ids = ['seq1', 'seq2', 'seq3']
found = get_sequence_id('sequences.fasta', target_ids)
for header, seq in found:
print(f"Found: {header}")
# Extract specific sequences using indexes
index = build_index('sequences.fasta')
# using pickle you can save and load the index
#import pickle
#pickle.dump(index, "save_index_file.pkl")
#index = pickle.load("save_index_file.pkl")
target_ids = ['seq1', 'seq2', 'seq3']
found = get_sequence_index('sequences.fasta', target_ids, index, ignore_unfound=True)
for header, seq in found:
print(f"Found: {header}")
# DNA manipulation
dna = "ATCGGTAA"
print(complement(dna)) # TAGCCATT
print(reverse_complement(dna)) # TTACCGAT
```
## API Reference
### Parsing Functions
#### `fasta_iter(open_file: TextIO) -> Generator[tuple[str, str], None, None]`
Memory-efficient iterator over FASTA sequences.
```python
with open('large_file.fasta') as f:
for header, sequence in fasta_iter(f):
# Process one sequence at a time
process_sequence(header, sequence)
```
#### `load_fasta(fasta_path: str|Path) -> dict[str, str]`
Load entire FASTA file into a dictionary mapping sequence IDs to sequences.
```python
sequences = load_fasta('sequences.fasta')
my_sequence = sequences['sequence_id']
```
#### `get_sequence_id(fasta_file: str|Path, identifiers: Iterable[str], identifier_only: bool = True) -> list[tuple[str, str]]`
Extract sequences matching specific identifiers.
- `identifier_only`: If True, match only the first part of headers (before whitespace)
```python
wanted = ['seq1', 'seq2']
results = get_sequence_id('sequences.fasta', wanted)
```
#### `build_index(fasta_file: str|Path) -> dict[str, int]`
Build a fasta index as a dictionary
```python
index = build_index(fasta_file)
```
#### `get_sequence_index(fasta_file: str|Path, identifiers:Iterable[str], index_dict:dict[str, int], ignore_unfound: bool = True) -> list[tuple[str, str]]`
use index to retrieve sequence (faster)
```python
index = build_index(fasta_file)
wanted = ['seq1', 'seq2']
get_sequence_index(fasta_file, wanted, index)
```
### Sequence Manipulation
#### `complement(seq: str) -> str`
Return the complement of a DNA sequence (A↔T, C↔G, supports all IUPAC codes).
#### `reverse(seq: str) -> str`
Return the reverse of a sequence.
#### `reverse_complement(seq: str) -> str`
Return the reverse complement of a DNA sequence.
#### `wrap_sequence(sequence: str, chunk_size: int = 80) -> str`
Format sequence with line breaks every `chunk_size` characters (standard multiline FASTA format).
```python
formatted = wrap_sequence("ATCGATCGATCG" * 10, 60)
print(formatted) # 60 characters per line
# write to a file
with open(out_file, 'w') as fo:
fo.write(">{}\n{}\n".format('seq_id', wrap_sequence("ATCGATCGATCG" * 10, 80)))
```
## Design Philosophy
This library prioritizes:
- **Memory efficiency**: Built for large genomic files that don't fit in RAM
- **Simplicity**: Clean, predictable API with minimal dependencies. Not OOP bloat, only data.
- **Performance**: Stream-based processing with O(1) memory usage for parsing
- **Standards compliance**: Full IUPAC nucleotide code support
## Use Cases
- Processing large fasta file (metagenome)
- Common DNA sequence manipulation
- Common operations on fasta including parsing, indexing and sequence retrieval.
- Bioinformatics workflows requiring memory efficiency
## Requirements
- Python 3.8+
- No external dependencies
## License
MIT
## Contributing
Feel free to ask for new features. I published it as lightweight because those are the feature I use the most and wanted to start with a solid fondation.
I used this library for years, and it has been extensively tested. As such I will only adress issue that come with a minimal reproducible problem.
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"description": "# Easy Fasta\n\nA lightweight functional Python library for efficient FASTA file parsing and DNA sequence manipulation. No OOP bloat, only data.\n\n## Features\n\n- **Memory-efficient parsing**: Stream through large FASTA files without loading everything into memory\n- **Random access**: Jump directly to specific sequences with position tracking\n- **Sequence extraction**: Filter sequences by identifiers\n- **DNA manipulation**: Complete IUPAC-compliant complement and reverse complement operations\n- **Formatting**: Convert sequences to multi-line FASTA format\n- **Does not validate input**: user are responsible to provide correctly formatted file.\n## Installation\npython 3.8+\n```\n> pip install easyfasta\n```\nor simply copy the module to your project\n\n## Quick Start\n\n```python\nfrom easyfasta import *\n# Parse FASTA file sequence by sequence (memory efficient)\nwith open('sequences.fasta') as f:\n for header, sequence in fasta_iter(f):\n print(f\">{header}\")\n print(sequence[:50]) # First 50 bases\n\n# Load entire FASTA into dictionary\nsequences = load_fasta('sequences.fasta')\nprint(sequences['sequence_id'])\n\n# Extract specific sequences\ntarget_ids = ['seq1', 'seq2', 'seq3']\nfound = get_sequence_id('sequences.fasta', target_ids)\nfor header, seq in found:\n print(f\"Found: {header}\")\n\n# Extract specific sequences using indexes\nindex = build_index('sequences.fasta')\n# using pickle you can save and load the index\n#import pickle\n#pickle.dump(index, \"save_index_file.pkl\")\n#index = pickle.load(\"save_index_file.pkl\")\ntarget_ids = ['seq1', 'seq2', 'seq3']\nfound = get_sequence_index('sequences.fasta', target_ids, index, ignore_unfound=True)\nfor header, seq in found:\n print(f\"Found: {header}\")\n\n# DNA manipulation\ndna = \"ATCGGTAA\"\nprint(complement(dna)) # TAGCCATT\nprint(reverse_complement(dna)) # TTACCGAT\n```\n\n## API Reference\n\n### Parsing Functions\n\n#### `fasta_iter(open_file: TextIO) -> Generator[tuple[str, str], None, None]`\n\nMemory-efficient iterator over FASTA sequences.\n\n```python\nwith open('large_file.fasta') as f:\n for header, sequence in fasta_iter(f):\n # Process one sequence at a time\n process_sequence(header, sequence)\n```\n\n#### `load_fasta(fasta_path: str|Path) -> dict[str, str]`\n\nLoad entire FASTA file into a dictionary mapping sequence IDs to sequences.\n\n```python\nsequences = load_fasta('sequences.fasta')\nmy_sequence = sequences['sequence_id']\n```\n\n#### `get_sequence_id(fasta_file: str|Path, identifiers: Iterable[str], identifier_only: bool = True) -> list[tuple[str, str]]`\n\nExtract sequences matching specific identifiers.\n\n- `identifier_only`: If True, match only the first part of headers (before whitespace)\n\n```python\nwanted = ['seq1', 'seq2']\nresults = get_sequence_id('sequences.fasta', wanted)\n```\n\n#### `build_index(fasta_file: str|Path) -> dict[str, int]`\n\nBuild a fasta index as a dictionary\n\n\n```python\nindex = build_index(fasta_file)\n```\n\n#### `get_sequence_index(fasta_file: str|Path, identifiers:Iterable[str], index_dict:dict[str, int], ignore_unfound: bool = True) -> list[tuple[str, str]]`\n\nuse index to retrieve sequence (faster)\n\n\n```python\nindex = build_index(fasta_file)\nwanted = ['seq1', 'seq2']\nget_sequence_index(fasta_file, wanted, index)\n```\n\n### Sequence Manipulation\n\n#### `complement(seq: str) -> str`\nReturn the complement of a DNA sequence (A\u2194T, C\u2194G, supports all IUPAC codes).\n\n#### `reverse(seq: str) -> str`\nReturn the reverse of a sequence.\n\n#### `reverse_complement(seq: str) -> str`\nReturn the reverse complement of a DNA sequence.\n\n#### `wrap_sequence(sequence: str, chunk_size: int = 80) -> str`\nFormat sequence with line breaks every `chunk_size` characters (standard multiline FASTA format).\n\n```python\nformatted = wrap_sequence(\"ATCGATCGATCG\" * 10, 60)\nprint(formatted) # 60 characters per line\n# write to a file\nwith open(out_file, 'w') as fo:\n fo.write(\">{}\\n{}\\n\".format('seq_id', wrap_sequence(\"ATCGATCGATCG\" * 10, 80)))\n```\n\n## Design Philosophy\n\nThis library prioritizes:\n\n- **Memory efficiency**: Built for large genomic files that don't fit in RAM\n- **Simplicity**: Clean, predictable API with minimal dependencies. Not OOP bloat, only data.\n- **Performance**: Stream-based processing with O(1) memory usage for parsing\n- **Standards compliance**: Full IUPAC nucleotide code support\n\n## Use Cases\n\n- Processing large fasta file (metagenome)\n- Common DNA sequence manipulation\n- Common operations on fasta including parsing, indexing and sequence retrieval.\n- Bioinformatics workflows requiring memory efficiency\n\n## Requirements\n\n- Python 3.8+\n- No external dependencies\n\n## License\nMIT\n\n## Contributing\nFeel free to ask for new features. I published it as lightweight because those are the feature I use the most and wanted to start with a solid fondation.\n\nI used this library for years, and it has been extensively tested. As such I will only adress issue that come with a minimal reproducible problem.\n",
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