| Name | seqpro JSON |
| Version |
0.1.16
JSON |
| download |
| home_page | None |
| Summary | None |
| upload_time | 2025-01-09 19:54:07 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
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| requirements |
No requirements were recorded.
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No Travis.
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# SeqPro (Sequence processing toolkit)
```python
import seqpro as sp
```
SeqPro is a Python package for processing DNA/RNA sequences, with limited support for protein sequences. SeqPro is fully functional on its own but is heavily utilized by other packages including [SeqData](https://github.com/ML4GLand/SeqData), [MotifData](https://github.com/ML4GLand/MotifData), [SeqExplainer](https://github.com/ML4GLand/SeqExplainer), and [EUGENe](https://github.com/ML4GLand/EUGENe).
All functions in SeqPro take as input a string, a list of strings, a NumPy array of strings, a NumPy array of single character bytes (S1) or a NumPy array of one-hot encoded strings. There is also emerging integration with XArray through the `seqpro.xr` submodule to integrate nicely with [SeqData](https://github.com/ML4GLand/SeqData).
Computational bottelnecks or code that is impossible to vectorize with NumPy alone are accelerated with Numba e.g. padding sequences, one-hot encoding, converting from one-hot encoding to nucleotides, etc.
# Installation
```bash
pip install seqpro
```
## API
```python
N = 2
L = 3
# Generating random sequences
seqs = sp.random_seqs(shape=(N, L), alphabet=sp.DNA, seed=1234)
# Padding
sp.pad_seqs(seqs, pad="right", pad_value="N", length=5, length_axis=-1)
# One-hot encoding and decoding
ohe = sp.ohe(seqs, alphabet=sp.DNA)
sp.decode_ohe(ohe, ohe_axis=-1, alphabet=sp.DNA, unknown_char="N")
# Tokenization
token_map = {"A": 7, "C": 8, "G": 9, "T": 10, "N": 11}
tokens = sp.tokenize(seqs, token_map=token_map, unknown_token=11)
sp.decode_tokens(tokens, token_map=token_map)
# Reverse complement
sp.reverse_complement(seqs, alphabet=sp.DNA)
# k-let preserving shuffling
sp.k_shuffle(seqs, k=2, length_axis=1, seed=1234)
# Calculating GC or nucleotide content
sp.gc_content(seqs, alphabet=sp.DNA)
sp.nucleotide_content(seqs, alphabet=sp.DNA)
# Randomly jittering sequences
sp.jitter(seqs, max_jitter=128, length_axis=1, seed=1234)
# Collapse coverage to a given bin width
sp.bin_coverage(coverage, bin_width=128, length_axis=1, normalize=False)
```
# More to come!
All contributions, including bug reports, documentation improvements, and enhancement suggestions are welcome. Everyone within the community is expected to abide by our [code of conduct](https://github.com/ML4GLand/EUGENe/blob/main/CODE_OF_CONDUCT.md)
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