# 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|
Raw data
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"description": "# TIVar\nTranslation Initiation Variation\n\nPredict 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.\n\n# INSTALL\n\nPython version >= 3.4.\n\n**Requirements**\n\n[NumPy](https://numpy.org/)\n\n[PyTorch](https://pytorch.org/)\n\n**Install from source**\n\n`git clone https://github.com/zhpn1024/TIVar`\n\n`python setup.py install`\n\nor\n\n`python setup.py install --user`\n\n\n**Install from PyPI**\n\n`pip install tivar`\n\n\n# Usage\n\n**predict**\n\nThis module can calculate TI efficiency scores from given sequences.\n\nFasta sequence file as input:\n\n`tivar predict -S test1.fa -o out1.txt`\n\nProvide sequence in the parameter:\n\n`tivar predict -s aaaaaacaaaaaaaTGTACAATGGATGCATTGAAATTATATGTAATTGTATAAATGGTGCAACA -o out1.txt`\n\nProvide transcript annotation and genome sequence:\n\n`tivar predict -g hg38_gc31.gtf.gz -f hg38.fa -o out1.txt`\n\nThe output is like:\n\n|SeqID|Pos|StartSeq|EffScore|\n|-----|-----|-----|-----|\n|Seq|13|aacaaaaaa-aTG-TACA|0.30354|\n|Seq|20|aaaTGTACA-ATG-GATG|0.37131|\n\n\n\n**diff**\n\nThis module predict TI changes caused by sequence variation.\n\n`tivar diff -i test.vcf -g hg38_gc31.gtf.gz -f hg38.fa -o out2.txt`\n\nThe output is like:\n\n|Gid|Tid|Var|GenoPos|Strand|Pos|RefSeq|AltSeq|EffeRef|EffeAlt|Diff|FC|Type|\n|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|\n|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|\n|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|\n\n",
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