makePrior

COSMIC

A GRanges object with the regions (gene) of interest.

regions

pi.gene

Background probability of a locus being mutated. Default 1e-4, corresponding to an expected value of 1 SNV per 1e4 bases.

pi.backgr


This function computes the prior probability of detecting a true variant from a variation data base. It assumes a
VCF file with a CNT slot for the count of a given base substitution. Such a VCF file can be downloaded at
ftp://ngs.sanger.ac.uk/production/cosmic/. The prior probability
is simply defined as pi.mut * CNT[i]/sum(CNT). On sites with no count, a background probability of pi0 is used.


This package provides provides quantitative variant callers for
detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing
experiments. The deepSNV algorithm is used for a comparative setup with a
control experiment of the same loci and uses a beta-binomial model and a
likelihood ratio test to discriminate sequencing errors and subclonal SNVs.
The shearwater algorithm computes a Bayes classifier based on a
beta-binomial model for variant calling with multiple samples for
precisely estimating model parameters such as local error rates and
dispersion and prior knowledge, e.g. from variation data bases such as
COSMIC.

Moritz Gerstung

deepSNV

Detection of subclonal SNVs in deep sequencing data.

makePrior function

This function computes the prior probability of detecting a true variant from a variation data base. It assumes a
VCF file with a CNT slot for the count of a given base substitution. Such a VCF file can be downloaded at
ftp://ngs.sanger.ac.uk/production/cosmic/. The prior probability
is simply defined as pi.mut * CNT[i]/sum(CNT). On sites with no count, a background probability of pi0 is used.

makePrior: Compute a prior from a COSMIC VCF object

Description

Usage

Arguments

Value

Examples