Skip to content
This repository has been archived by the owner on Apr 7, 2018. It is now read-only.
/ deepSNV Public archive
forked from gerstung-lab/deepSNV

INACTIVE FORK. Please issue pull requests to gerstung-lab/deepSNV

Notifications You must be signed in to change notification settings

mg14/deepSNV

 
 

Repository files navigation

deepSNV

Build Status

Description

This package provides provides a 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 new shearwater algorithm (beta) 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.

Note

This repository contains the current development snapshot of the deepSNV package in the folder deepSNV. It is not guaranteed to work all times.

Installation

The good way

For unexperienced users it is recommended to use package as provided in bioconductor. Please follow the instructions at: http://master.bioconductor.org/packages/devel/bioc/html/deepSNV.html

The bad way

You can use devtools::github_install() to install from this repository. For advanced users.

> library(devtools); install_github("mg14/deepSNV")

The ugly way

To install this development snapshot of deepSNV, check out the repository and run

$ make install

Note that this will not install the necessary dependencies.

Previous (running) builds can be found in builds. These will also be mirrored to the bioconductor development branch.

About

INACTIVE FORK. Please issue pull requests to gerstung-lab/deepSNV

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Languages

  • R 92.1%
  • C++ 4.8%
  • C 1.8%
  • Other 1.3%