Metadata-Version: 2.1
Name: mmsplice
Version: 0.2.0
Summary: Predict splicing variant effect from VCF
Home-page: https://github.com/s6juncheng/mmsplice
Author: Jun Cheng
Author-email: chengju@in.tum.de
License: MIT license
Keywords: mmsplice
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Dist: setuptools (<=39.1.0)
Requires-Dist: sklearn
Requires-Dist: tensorflow
Requires-Dist: kipoi
Requires-Dist: pandas
Requires-Dist: concise
Requires-Dist: cyvcf2 (==0.9.0)
Requires-Dist: gffutils
Requires-Dist: pyfaidx
Requires-Dist: tqdm
Requires-Dist: click
Requires-Dist: flask

# mmsplice

[![pypi](https://img.shields.io/pypi/v/mmsplice.svg)](https://pypi.python.org/pypi/mmsplice)
[![travis](https://img.shields.io/travis/s6juncheng/mmsplice.svg)](https://travis-ci.org/s6juncheng/mmsplice)

Predict splicing variant effect from VCF

* Free software: MIT license


## Usage example
------

Check notebooks/example.ipynb

```python
# Import
from mmsplice.vcf_dataloader import SplicingVCFDataloader
from mmsplice import MMSplice, predict_all_table
from mmsplice.utils import max_varEff

# example files
gtf = 'tests/data/test.gtf'
vcf = 'tests/data/test.vcf.gz'
fasta = 'tests/data/hg19.nochr.chr17.fa'
gtfIntervalTree = '../tests/data/test.pkl' # pickle exon interval Tree

# dataloader to load variants from vcf
dl = SplicingVCFDataloader(gtf, 
                          fasta,
                          vcf,
                          out_file=gtfIntervalTree,
                          split_seq=False)

# Specify model
model = MMSplice(
    exon_cut_l=0,
    exon_cut_r=0,
    acceptor_intron_cut=6,
    donor_intron_cut=6,
    acceptor_intron_len=50,
    acceptor_exon_len=3,
    donor_exon_len=5,
    donor_intron_len=13)

 # Do prediction
 predictions = predict_all_table(model, dl, batch_size=1024, split_seq=False, assembly=False)

 # Summerize with maximum effect size
 predictionsMax = max_varEff(predictions)
```

=======
History
=======

0.1.0 (2018-07-17)
------------------

* First release on PyPI.


