Metadata-Version: 2.1
Name: tivar
Version: 0.1.2
Summary: Translation Initiation Variation
Home-page: https://github.com/zhpn1024/TIVar
Author: Peng Zhang
Author-email: zhpn1024@163.com
Project-URL: Bug Reports, https://github.com/zhpn1024/TIVar/issues
Project-URL: Source, https://github.com/zhpn1024/TIVar
Keywords: translation,initiation,RNA,variation
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.4, <4
Description-Content-Type: text/markdown
Requires-Dist: torch
Requires-Dist: numpy
Provides-Extra: dev
Requires-Dist: check-manifest ; extra == 'dev'
Provides-Extra: test
Requires-Dist: coverage ; extra == 'test'

# TIVar
Translation Initiation Variation

Predict translation initiation (TI) efficiency for potential start codons, based on the context sequence near the start codon. Given SNP/Indel variation, this tools can predict changes of TI efficiencies between ref and alt alleles.

# INSTALL

Python version >= 3.4.

**Requirements**

[NumPy](https://numpy.org/)

[PyTorch](https://pytorch.org/)

**Install from source**

`git clone https://github.com/zhpn1024/TIVar`

`python setup.py install`

or

`python setup.py install --user`


**Install from PyPI**

`pip install tivar`


# Usage

**predict**

This module can calculate TI efficiency scores from given sequences.

Fasta sequence file as input:

`tivar predict -S test1.fa -o out1.txt`

Provide sequence in the parameter:

`tivar predict -s aaaaaacaaaaaaaTGTACAATGGATGCATTGAAATTATATGTAATTGTATAAATGGTGCAACA -o out1.txt`

Provide transcript annotation and genome sequence:

`tivar predict -g hg38_gc31.gtf.gz -f hg38.fa -o out1.txt`

The output is like:

|SeqID|Pos|StartSeq|EffScore|
|-----|-----|-----|-----|
|Seq|13|aacaaaaaa-aTG-TACA|0.30354|
|Seq|20|aaaTGTACA-ATG-GATG|0.37131|



**diff**

This module predict TI changes caused by sequence variation.

`tivar diff -i test.vcf -g hg38_gc31.gtf.gz -f hg38.fa -o out2.txt`

The output is like:

|Gid|Tid|Var|GenoPos|Strand|Pos|RefSeq|AltSeq|EffeRef|EffeAlt|Diff|FC|Type|
|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|
|ENSG00000134262.13|ENST00000369569.6|chr1:113895309:A>AC|113895310|-|2056|ACCCTCCAG-ATG-GCTC|ACCCTCCAG-AGT-GGCT|0.32097|0.0|-0.321|0.0|TI_decreased|
|ENSG00000134262.13|ENST00000369569.6|chr1:113895309:A>AC|113895310|-|2056|ACCCTCCAG-ATG-GCTC|CCCTCCAGA-GTG-GCTC|0.32097|0.04335|-0.2776|0.1351|TI_decreased|

