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https://github.com/prihoda/abnumber
Convenience Python APIs for antibody numbering using ANARCI
https://github.com/prihoda/abnumber
Last synced: about 9 hours ago
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Convenience Python APIs for antibody numbering using ANARCI
- Host: GitHub
- URL: https://github.com/prihoda/abnumber
- Owner: prihoda
- License: mit
- Created: 2020-10-12T14:05:55.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-09-16T10:33:10.000Z (2 months ago)
- Last Synced: 2024-09-16T12:10:43.193Z (2 months ago)
- Language: Python
- Size: 5.76 MB
- Stars: 80
- Watchers: 3
- Forks: 11
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AbNumber
[](https://github.com/prihoda/AbNumber/actions/workflows/build-test.yml)
[](https://anaconda.org/bioconda/abnumber)
[](http://abnumber.readthedocs.org/)
Convenience Python APIs for antibody numbering and alignment using [ANARCI](https://github.com/oxpig/ANARCI)
Try it out in your browser using Binder: [](https://mybinder.org/v2/gh/prihoda/AbNumber/HEAD?filepath=examples%2FAbNumber_getting_started.ipynb)
## Features:
- **Streamlined Python API** using `Chain` object
- **Identifying CDR regions**, e.g. using `chain.regions` or `chain.cdr3_seq`
- **Indexing and slicing**, e.g. using `chain['5']` or `chain['H2':'H5']`
- **Pairwise and multiple sequence alignment** in the given numbering using `chain.align(another)`
- **Alignment to nearest human germline** using `chain.align(chain.find_merged_human_germline())`
- **Humanization using CDR grafting** by `chain.graft_cdrs_onto_human_germline()`
See [AbNumber Documentation](https://abnumber.readthedocs.io/en/latest/) for the full reference.
## Installation
Install using Bioconda:
```bash
conda install -c bioconda abnumber
```
Note: **Windows is not supported** due to [HMMER](https://github.com/EddyRivasLab/hmmer) dependency. AbNumber is currently only available on UNIX & MacOS.
## Examples
```python
from abnumber import Chain
seq = 'QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPSRGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSSAKTTAPSVYPLA'
chain = Chain(seq, scheme='imgt')
chain
# QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPSRGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSS
# ^^^^^^^^ ^^^^^^^^ ^^^^^^^^^^^^
chain.cdr3_seq
# ARYYDDHYCLDY
chain.print(numbering=True)
# 0 1 2 3 4 5 6 7 8 9 10 11 12
# 12345678912345678901234567890567890123456789012345678923456789012456789012345678901234567890123456789023456789012345678
# QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPSRGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSS
# ^^^^^^^^ ^^^^^^^^ ^^^^^^^^^^^^
```
Chain can be iterated:
```python
for pos, aa in chain:
print(pos, aa)
# H1 Q
# H2 V
# H3 Q
# H4 L
# H5 Q
```
Chain can also be indexed and sliced using scheme numbering:
```python
chain['5']
# 'Q'
for pos, aa in chain['H2':'H5']:
print(pos, aa)
# H2 V
# H3 Q
# H4 L
# H5 Q
```
For all methods see [AbNumber Documentation](https://abnumber.readthedocs.io/en/latest/)
## Credits
See [ANARCI on GitHub](https://github.com/oxpig/ANARCI) and the ANARCI paper: [ANARCI: antigen receptor numbering and receptor classification](https://doi.org/10.1093/bioinformatics/btv552)