Metadata-Version: 1.1
Name: varcode
Version: 0.4.19
Summary: Variant annotation in Python
Home-page: https://github.com/hammerlab/varcode
Author: Alex Rubinsteyn
Author-email: UNKNOWN
License: http://www.apache.org/licenses/LICENSE-2.0.html
Description: `|Build Status| <https://travis-ci.org/hammerlab/varcode>`_ `|Coverage
        Status| <https://coveralls.io/github/hammerlab/varcode?branch=master>`_
        `|DOI| <https://zenodo.org/badge/latestdoi/18834/hammerlab/varcode>`_
        
        Varcode
        =======
        
        Varcode is a library for working with genomic variant data in Python and
        predicting the impact of those variants on protein sequences.
        
        Installation
        ------------
        
        You can install varcode using
        `pip <https://pip.pypa.io/en/latest/quickstart.html>`_:
        
        ::
        
            pip install varcode
        
        Optionally, you can pre-populate metadata caches through
        `PyEnsembl <https://github.com/hammerlab/pyensembl>`_ as follows:
        
        ::
        
            # Downloads and installs the Ensembl releases (75 and 76)
            pyensembl install --release 75 76
        
        This will eliminate a potential delay of several minutes required to
        install the relevant data when using the ``varcode`` for the first time.
        
        Example
        -------
        
        ::
        
            import varcode
        
            # Load TCGA MAF containing variants from their
            variants = varcode.load_maf("tcga-ovarian-cancer-variants.maf")
        
            print(variants)
            ### <VariantCollection from 'tcga-ovarian-cancer-variants.maf' with 6428 elements>
            ###  -- Variant(contig=1, start=69538, ref=G, alt=A, genome=GRCh37)
            ###  -- Variant(contig=1, start=881892, ref=T, alt=G, genome=GRCh37)
            ###  -- Variant(contig=1, start=3389714, ref=G, alt=A, genome=GRCh37)
            ###  -- Variant(contig=1, start=3624325, ref=G, alt=T, genome=GRCh37)
            ###  ...
        
            # you can index into a VariantCollection and get back a Variant object
            variant = variants[0]
        
            # groupby_gene_name returns a dictionary whose keys are gene names
            # and whose values are themselves VariantCollections
            gene_groups = variants.groupby_gene_name()
        
            # get variants which affect the TP53 gene
            TP53_variants = gene_groups["TP53"]
        
            # predict protein coding effect of every TP53 variant on
            # each transcript of the TP53 gene
            TP53_effects = TP53_variants.effects()
        
            print(TP53_effects)
            ### <EffectCollection with 789 elements>
            ### -- PrematureStop(variant=chr17 g.7574003G>A, transcript_name=TP53-001, transcript_id=ENST00000269305, effect_description=p.R342*)
            ### -- ThreePrimeUTR(variant=chr17 g.7574003G>A, transcript_name=TP53-005, transcript_id=ENST00000420246)
            ### -- PrematureStop(variant=chr17 g.7574003G>A, transcript_name=TP53-002, transcript_id=ENST00000445888, effect_description=p.R342*)
            ### -- FrameShift(variant=chr17 g.7574030_7574030delG, transcript_name=TP53-001, transcript_id=ENST00000269305, effect_description=p.R333fs)
            ### ...
        
            premature_stop_effect = TP53_effects[0]
        
            print(str(premature_stop_effect.mutant_protein_sequence))
            ### 'MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMF'
        
            print(premature_stop_effect.aa_mutation_start_offset)
            ### 341
        
            print(premature_stop_effect.transcript)
            ### Transcript(id=ENST00000269305, name=TP53-001, gene_name=TP53, biotype=protein_coding, location=17:7571720-7590856)
        
            print(premature_stop_effect.gene.name)
            ### 'TP53'
        
        If you are looking for a quick start guide, you can check out `this
        iPython book <./examples/varcode-quick_start.ipynb>`_ that demonstrates
        simple use cases of Varcode
        
        Effect Types
        ------------
        
        Effect type \| Description -----------: \| :-----------
        *AlternateStartCodon* \| Replace annotated start codon with alternative
        start codon (*e.g.* ``ATG>CAG``). *ComplexSubstitution* \| Insertion and
        deletion of multiple amino acids. *Deletion* \| Coding mutation which
        causes deletion of amino acid(s). *ExonLoss* \| Deletion of entire exon,
        significantly disrupts protein. *ExonicSpliceSite* \| Mutation at the
        beginning or end of an exon, may affect splicing. *FivePrimeUTR* \|
        Variant affects 5' untranslated region before start codon.
        *FrameShiftTruncation* \| A frameshift which leads immediately to a stop
        codon (no novel amino acids created). *FrameShift* \| Out-of-frame
        insertion or deletion of nucleotides, causes novel protein sequence and
        often premature stop codon. *IncompleteTranscript* \| Can't determine
        effect since transcript annotation is incomplete (often missing either
        the start or stop codon). *Insertion* \| Coding mutation which causes
        insertion of amino acid(s). *Intergenic* \| Occurs outside of any
        annotated gene. *Intragenic* \|Within the annotated boundaries of a gene
        but not in a region that's transcribed into pre-mRNA.
        *IntronicSpliceSite* \| Mutation near the beginning or end of an intron
        but less likely to affect splicing than donor/acceptor mutations.
        *Intronic* \| Variant occurs between exons and is unlikely to affect
        splicing. *NoncodingTranscript* \| Transcript doesn't code for a
        protein. *PrematureStop* \| Insertion of stop codon, truncates protein.
        *Silent* \| Mutation in coding sequence which does not change the amino
        acid sequence of the translated protein. *SpliceAcceptor* \| Mutation in
        the last two nucleotides of an intron, likely to affect splicing.
        *SpliceDonor* \| Mutation in the first two nucleotides of an intron,
        likely to affect splicing. *StartLoss* \| Mutation causes loss of start
        codon, likely result is that an alternate start codon will be used
        down-stream (possibly in a different frame). *StopLoss* \| Loss of stop
        codon, causes extension of protein by translation of nucleotides from 3'
        UTR. *Substitution* \| Coding mutation which causes simple substitution
        of one amino acid for another. *ThreePrimeUTR* \| Variant affects 3'
        untranslated region after stop codon of mRNA.
        
        Coordinate System
        -----------------
        
        Varcode currently uses a "base counted, one start" genomic coordinate
        system, to match the Ensembl annotation database. We are planning to
        switch over to "space counted, zero start" (interbase) coordinates,
        since that system allows for more uniform logic (no special cases for
        insertions). To learn more about genomic coordinate systems, read this
        `blog
        post <http://alternateallele.blogspot.com/2012/03/genome-coordinate-conventions.html>`_.
        
        .. |Build
        Status| image:: https://travis-ci.org/hammerlab/varcode.svg?branch=master
        .. |Coverage
        Status| image:: https://coveralls.io/repos/hammerlab/varcode/badge.svg?branch=master&service=github
        .. |DOI| image:: https://zenodo.org/badge/18834/hammerlab/varcode.svg
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
