Metadata-Version: 2.1
Name: mmsplice
Version: 2.3.0
Summary: Predict splicing variant effect from VCF
Home-page: https://github.com/gagneurlab/mmsplice
Author: Jun Cheng, Muhammed Hasan Çelik
Author-email: chengju@in.tum.de, muhammedhasancelik@gmail.com
License: MIT license
Description: # MMSplice & MTSplice
        [![CircleCI](https://circleci.com/gh/gagneurlab/MMSplice_MTSplice.svg?style=svg)](https://circleci.com/gh/gagneurlab/MMSplice_MTSplice)
        [![pypi](https://img.shields.io/pypi/v/mmsplice.svg)](https://pypi.python.org/pypi/mmsplice)
        
        Predict (tissue-specific) splicing variant effect from VCF. MTSplice is integrated into MMSplice with the same API.
        
        Paper: Cheng et al. https://doi.org/10.1101/438986, https://www.biorxiv.org/content/10.1101/2020.06.07.138453v1
        
        ![MMSplice](https://raw.githubusercontent.com/kipoi/models/master/MMSplice/Model.png)
        ![MTSplice](https://raw.githubusercontent.com/s6juncheng/figshare/master/MTSplice.JPG)
        
        ## Installation
        -----------------
        
        External dependencies:
        ```bash
        pip install cyvcf2 cython
        ```
        
        Conda installation is recommended:
        ```bash
        conda install cyvcf2 cython -y
        ```
        
        ```bash
        pip install mmsplice
        ```
        
        ## Run MMSplice Online
        
        You can run mmsplice with following google colab notebooks online:
        
        - [run on vcf file](https://colab.research.google.com/drive/1Kw5rHMXaxXXsmE3WecxbXyGQJma80Eq6)
        
        ### Preparation
        -----------------
        
        #### 1. Prepare annotation (gtf) file
        Standard human gene annotation file in GTF format can be downloaded from ensembl or gencode.
        `MMSplice` can work directly with those files, however, some filtering is higly recommended.
        
        - Filter for protein coding genes.
        
        #### 2. Prepare variant (VCF) file
        A correctly formatted VCF file with work with `MMSplice`, however the following steps will make it less prone to false positives:
        
        - Quality filtering. Low quality variants leads to unreliable predictions.
        - Avoid presenting multiple variants in one line by splitting them into multiple lines. Example code to do it:
          ```bash
          bcftools norm -m-both -o out.vcf in.vcf.gz
          ```
        - Left-normalization. For instance, GGCA-->GG is not left-normalized while GCA-->G is. Details for unified representation of genetic variants see [Tan et al.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4481842/)
          ```bash
          bcftools norm -f reference.fasta -o out.vcf in.vcf
          ```
        
        #### 3. Prepare reference genome (fasta) file
        Human reference fasta file can be downloaded from ensembl/gencode. Make sure the chromosome name matches with GTF annotation file you use.
        
        
        ### Example code
        -------------------
        
        Check [notebooks/example.ipynb](https://github.com/gagneurlab/MMSplice/blob/master/notebooks/example.ipynb)
        
        To score variants (including indels), we suggest to use primarily the `deltaLogitPSI` predictions, which is the default output. The differential splicing efficiency (dse) model was trained from MMSplice modules and exonic variants from MaPSy, thus only the predictions for exonic variants are calibrated.
        
        **MTSplice** To predict tissue-specific variant effect with MTSplice, specify `tissue_specific=True` in `SplicingVCFDataloader`.
        
        ```python
        # Import
        from mmsplice.vcf_dataloader import SplicingVCFDataloader
        from mmsplice import MMSplice, predict_save, 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'
        csv = 'pred.csv'
        ```
        Dataloader to load variants from vcf
        ```python
        dl = SplicingVCFDataloader(gtf, fasta, vcf, tissue_specific=False)
        ```
        
        To predict tissue-specific effect, in the dataloader use `tissue_specific=True` in the dataloader instead
        ```python
        dl = SplicingVCFDataloader(gtf, fasta, vcf, tissue_specific=True)
        ```
        
        Run prediction with default MMSplice parameters
        ```python
        # Specify model
        model = MMSplice()
        
        # Or predict and return as df
        predictions = predict_all_table(model, dl, pathogenicity=True, splicing_efficiency=True)
        ```
        
        To predict variant effect on <img src="https://render.githubusercontent.com/render/math?math=\Delta \Psi"> scale instead of <img src="https://render.githubusercontent.com/render/math?math=\Delta \text{logit}(\Psi)">. This option only works with tissue specific predictions `dl = SplicingVCFDataloader(..., tissue_specific=True)`:
        ```python
        # Or predict and return as df
        predictions = predict_all_table(model, dl, natural_scale=True)
        ```
        
        One variant might map to multiple exons. In the end we summarize the effect of as the maximum across all exons.
        ```python
        # Summerize with maximum effect size
        predictionsMax = max_varEff(predictions)
        ```
        
        ### Output
        
        Output of MMSplice is an tabular data which contains following described columns:
        
        * `ID`: id string of the variant
        * `delta_logit_psi`: The main score is predicted by MMSplice, which shows the effect of the variant on the inclusion level (PSI percent spliced in) of the exon. The score is on a logit scale.  If the score is positive, it shows that variant leads higher inclusion rate for the exon. If the score is negative, it shows that variant leads higher exclusion rate for the exon. If delta_logit_psi is bigger than 2 or smaller than -2, the effect of variant can be considered strong.
        * `exons`: Genetics location of exon whose inclusion rate is effected by variant
        * `exon_id`: Genetic id of exon whose inclusion rate is effected by variant
        * `gene_id`: Genetic id of the gene which the exon belongs to.
        * `gene_name`:  Name of the gene which the exon belongs to.
        * `transcript_id`: Genetic id of the transcript which the exon belongs to.
        * `ref_acceptorIntron`: acceptor intron score of the reference sequence
        * `ref_acceptor`: acceptor score of the reference sequence
        * `ref_exon`: exon score of the reference sequence
        * `ref_donor`: donor score of the reference sequence
        * `ref_donorIntron`: donor intron score of the reference sequence
        * `alt_acceptorIntron`: acceptor intron score of variant sequence
        * `alt_acceptor`: acceptor score of the sequence with variant
        * `alt_exon`: exon score of the sequence with variant
        * `alt_donor`: donor score of the sequence with variant
        * `alt_donorIntron`: donor intron score of the sequence with variant
        * `pathogenicity`: Potential pathogenic effect of the variant.
        * `efficiency`: The effect of the variant on the splicing efficiency of the exon.
        
        
        ## VEP Plugin
        
        The VEP plugin wraps the prediction function from `mmsplice` python package. Please check documentation of vep plugin [under VEP_plugin/README.md](VEP_plugin/README.md).
        
        
        =======
        History
        =======
        
        1.0.0 (2019-07-23)
        ------------------
        * Dependicies fixed #16
        * Valide gtf, fasta, vcf chrom annotation #15
        * Ship mmsplice with prebuild exon set. #12
        * Faster variant overlapping with pyranges #11
        * Batch prediction with masking update in exon module
        
        0.1.0 (2018-07-17)
        ------------------
        
        * First release on PyPI.
        
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 :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
