gtfparse
Read GTF (gene transfer format) annotations into a pandas DataFrame, with one row per feature and attribute values such as gene and transcript IDs expanded into columns.
Install
python -m pip install gtfparse
Python 3.11 or later is required. For optional Polars output, install
gtfparse[polars].
Read an annotation and find genes
This small artificial GTF runs without a download. Replace the stream with your annotation filename when working with a real dataset.
from io import StringIO
from gtfparse import read_gtf
annotation = StringIO(
'1\texample\tgene\t101\t200\t.\t+\t.\tgene_id "g1"; gene_name "EXAMPLE";\n'
'1\texample\texon\t101\t150\t.\t+\t.\tgene_id "g1"; transcript_id "t1";\n'
)
features = read_gtf(annotation)
genes = features.loc[features["feature"] == "gene"]
print(genes["gene_name"].tolist())
for row in features.itertuples():
print(row.feature, row.seqname, row.start, row.end, row.gene_id)
['EXAMPLE']
gene 1 101 200 g1
exon 1 101 150 g1
feature identifies the row type. seqname is the chromosome or contig name;
it is not the gene ID. The ninth GTF field supplies attributes such as
gene_id, gene_name and transcript_id.
Documentation
- Filter features, select chromosomes, write GTF and read numeric attributes
- Progress callbacks
- Version 3 migration and optional Polars output
- Complete API reference
To build the documentation locally, run
python -m pip install -r requirements-docs.txt and ./docs.sh.
python scripts/check_docs_examples.py checks the printed example output.
5 maintainers
Metadata
5 maintainers
Release files for gtfparse 3.0.5
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