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Package to examine de novo clustering

Project description



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, but you can use your own rates, or even longer sequence contexts, such as 5-mers or 7-mers.


pip install denovonear


Analyse de novo mutations within python:

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

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

You can also analyse de novo clustering via the denovonear command:

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 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 \

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 script. Lof and missense mutation rates can be generated with:

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.

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