Metadata-Version: 2.1
Name: denovonear
Version: 0.8.4
Summary: Package to examine de novo clustering
Home-page: https://github.com/jeremymcrae/denovonear
Author: Jeremy McRae
Author-email: jeremy.mcrae@gmail.com
License: MIT
Description: ![travis](https://travis-ci.org/jeremymcrae/denovonear.svg?branch=master)
        
        ### Denovonear
        
        This code assesses whether de novo single-nucleotide variants are closer
        together within the coding sequence of a gene than expected by chance. We use
        local-sequence based mutation rates to account for differential mutability of
        regions. The default rates are per-trinucleotide based see [Nature Genetics
        46:944–950](http://www.nature.com/ng/journal/v46/n9/full/ng.3050.html), but
        you can use your own rates, or even longer sequence contexts, such as 5-mers or
        7-mers.
        
        ### Install
        ```sh
        pip install denovonear
        ```
        
        
        ### Usage
        Analyse *de novo* mutations within python:
        
        ```py
        from denovonear.cluster_test import cluster_de_novos
        
        symbol = 'PPP2R5D'
        de_novos = {'missense': [42975003, 42975003, 42975003, 42975013], 'nonsense': []}
        p_values = cluster_de_novos(symbol, de_novos, iterations=1000000)
        ```
        
        Pull out site-specific rates by creating Transcript objects, then get the
        rates by consequence at each site
        
        ```py
        from denovonear.ensembl_requester import EnsemblRequest
        from denovonear.load_mutation_rates import load_mutation_rates
        from denovonear.load_gene import construct_gene_object
        from denovonear.site_specific_rates import SiteRates
        
        # convenience object to extract transcript coordinates and sequence from Ensembl
        ensembl = EnsemblRequest(cache_folder='cache', genome_build='grch37')
        transcript = construct_gene_object(ensembl, 'ENST00000346085')
        mut_rates = load_mutation_rates()
        
        rates = SiteRates(transcript, mut_rates)
        
        # rates are stored by consequence, but you can iterate through to find all
        # possible sites in and around the CDS:
        for cq in ['missense', 'nonsense', 'splice_lof', 'synonymous']:
            for site in rates[cq]:
                site['pos'] = transcript.get_position_on_chrom(site['pos'], site['offset'])
        
        # or if you just want the summed rate
        rates['missense'].get_summed_rate()
        ```
        
        You can also analyse de novo clustering via the denovonear command:
        
        ```sh
        denovonear cluster \
           --in data/example_de_novos.txt \
           --out output.txt
        ```
        
        That command uses a minimal example de novo input file, included in the git
        repository. The input is a tab-separated file with a line for each de novo
        event. The columns are HGNC symbol, chromosome, position, VEP consequence for
        the variant, and whether the de novo is a SNP or indel (the analysis excludes
        indels).
        
        Other options are:
        
        * `--rates PATH_TO_RATES`
        * `--cache-folder PATH_TO_CACHE_DIR`
        * `--genome-build "grch37" or "grch38" (default=grch37)`
        
        The optional rates file is a table separated file with three columns: 'from',
        'to', and 'mu_snp'. The 'from' column contains DNA sequence (where the length
        is an odd number) with the base to change at the central nucleotide. The 'to'
        column contains the sequence with the central base modified. The 'mu_snp' column
        contains the probability of the change (as per site per generation).
        
        The cache folder defaults to making a folder named "cache" within the working
        directory. The genome build indicates which genome build the coordinates of the
        de novo variants are based on, and defaults to GRCh37.
        
        ### Identify transcripts containing de novo events
        
        You can identify transcripts containing de novos events with the
        `identify_transcripts.py` script. This either identifies all transcripts for a
        gene with one or more de novo events, or identifies the minimal set of
        transcripts to contain all de novos (where transcripts are prioritised on the
        basis of number of de novo events, and length of coding sequence). Transcripts
        can be identified with:
        
        .. code:: bash
        
            denovonear transcripts \
                --de-novos data/example_de_novos.txt \
                --out output.txt \
                --all-transcripts
        
        Other options are:
        
        * `--minimise-transcripts` in place of `--all-transcripts`, to find the minimal
          set of transcripts
        * `--genome-build "grch37" or "grch38" (default=grch37)`
        
        ### Gene or transcript based mutation rates
        You can generate mutation rates for either the union of alternative transcripts
        for a gene, or for a specific Ensembl transcript ID with the
        `construct_mutation_rates.py` script. Lof and missense mutation rates can be
        generated with:
        
        ```sh
        denovonear rates \
            --genes data/example_gene_ids.txt \
            --out output.txt
        ```
        
        The tab-separated output file will contain one row per gene/transcript, with
        each line containing a transcript ID or gene symbol, a log10 transformed
        missense mutation rate, a log10 transformed nonsense mutation rate, and a log10
        transformed synonymous mutation rate.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
