fastaframes 1.4.0

A very simple fasta file parser.

FastaFrames

Convert between UniProt FASTA files and pandas DataFrames.

Installation

pip install fastaframes

Quick Start

Read a FASTA file into a DataFrame

from fastaframes import to_df

df = to_df("proteins.fasta")
print(df.head())

Write a DataFrame back to FASTA

from fastaframes import to_fasta

to_fasta(df, output_file="output.fasta")

Work with individual entries

from fastaframes import fasta_to_entries, entries_to_fasta

for entry in fasta_to_entries("proteins.fasta"):
    print(entry.unique_identifier, entry.protein_name)

# Filter and write back
entries = [e for e in fasta_to_entries("proteins.fasta") if e.organism_name == "Homo sapiens"]
entries_to_fasta(entries, output_file="human_only.fasta")

Multiple input formats

from io import StringIO
from fastaframes import to_df

# From a file path
df = to_df("proteins.fasta")

# From a string
df = to_df(">sp|P12345|EXAMPLE_HUMAN Example protein OS=Homo sapiens OX=9606\nMSEQUENCE\n")

# From a file object
with open("proteins.fasta") as f:
    df = to_df(f)

# From a StringIO
df = to_df(StringIO(">sp|P12345|EXAMPLE_HUMAN\nMSEQUENCE\n"))

Skip malformed entries

from fastaframes import to_df

df = to_df("messy_data.fasta", skip_error=True)

Wrap sequence lines

from fastaframes import to_fasta

# Wrap sequences at 60 characters (default is a single line per record)
to_fasta(df, output_file="wrapped.fasta", max_sequence_length=60)

Preserve non-standard header fields

Any KEY=value field beyond the standard PN/OS/OX/GN/PE/SV is kept in FastaEntry.additional_fields and re-emitted on serialization.

Error handling

All package errors derive from FastaFramesError. Malformed FASTA raises FastaFormatError (also a ValueError); unsupported inputs raise InvalidInputError (also a TypeError).

from fastaframes import to_df, FastaFormatError

try:
    df = to_df("example.fasta")
except FastaFormatError as err:
    print(err.header, err.reason)

Logging

FastaFrames logs under the fastaframes logger and stays silent until you configure logging:

import logging
logging.getLogger("fastaframes").setLevel(logging.DEBUG)

DataFrame Columns

Given this FASTA entry:

>sp|A0A087X1C5|CP2D7_HUMAN Putative cytochrome P450 2D7 OS=Homo sapiens OX=9606 GN=CYP2D7 PE=5 SV=1
MGLEALVPLAMIVAIFLLLVDLMHRHQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQ

to_df produces:

db	unique_identifier	entry_name	protein_name	organism_name	organism_identifier	gene_name	protein_existence	sequence_version	protein_sequence
sp	A0A087X1C5	CP2D7_HUMAN	Putative cytochrome P450 2D7	Homo sapiens	9606	CYP2D7	5	1	MGLEALVPLAMIVAIFLLLVDLMHRHQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQ

Column descriptions (following the UniProt FASTA header format):

Column	Description
db	Database source: sp (Swiss-Prot) or tr (TrEMBL)
unique_identifier	Primary UniProtKB accession number
entry_name	UniProtKB entry name
protein_name	Recommended protein name (RecName or first SubName)
organism_name	Scientific name of the source organism
organism_identifier	NCBI taxonomy identifier
gene_name	First gene name (if available)
protein_existence	Numerical evidence code for protein existence
sequence_version	Sequence version number
protein_sequence	Amino acid sequence

Development

pip install -e ".[dev]"

Common commands via just:

just check      # Run all checks (lint, typecheck, test)
just lint       # Lint with ruff
just fmt        # Format with ruff
just typecheck  # Type check with ty
just test       # Run tests
just test -v    # Run tests verbosely