easyfasta 1.0.14

A lightweight Python library for efficient FASTA file parsing and DNA sequence manipulation.

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

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.

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.

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)

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

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)

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).

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.