1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
|
Metadata-Version: 2.1
Name: gtfparse
Version: 1.3.0
Summary: GTF Parsing
Home-page: https://github.com/openvax/gtfparse
Author: Alex Rubinsteyn
License: http://www.apache.org/licenses/LICENSE-2.0.html
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Description-Content-Type: text/markdown
License-File: LICENSE
[](https://travis-ci.org/openvax/gtfparse) [](https://coveralls.io/github/openvax/gtfparse?branch=master)
<a href="https://pypi.python.org/pypi/gtfparse/">
<img src="https://img.shields.io/pypi/v/gtfparse.svg?maxAge=1000" alt="PyPI" />
</a>
gtfparse
========
Parsing tools for GTF (gene transfer format) files.
# Example usage
## Parsing all rows of a GTF file into a Pandas DataFrame
```python
from gtfparse import read_gtf
# returns GTF with essential columns such as "feature", "seqname", "start", "end"
# alongside the names of any optional keys which appeared in the attribute column
df = read_gtf("gene_annotations.gtf")
# filter DataFrame to gene entries on chrY
df_genes = df[df["feature"] == "gene"]
df_genes_chrY = df_genes[df_genes["seqname"] == "Y"]
```
## Getting gene FPKM values from a StringTie GTF file
```python
from gtfparse import read_gtf
df = read_gtf(
"Transcripts.gtf",
column_converters={"FPKM": float})
gene_fpkms = {
gene_name: fpkm
for (gene_name, fpkm, feature)
in zip(df["seqname"], df["FPKM"], df["feature"])
if feature == "gene"
}
```
|
