Ximmer is a tool designed to help users of exome and targeted genomic sequencing data accurately detect and interpret copy number variants (CNVs). Ximmer is not a copy number detection tool itself. Rather, it is a framework for running other copy number detection tools and interpreting their results. It offers three essential features that users of CNV detection tools need:
- A suite of pipelines for running a variety of well known CNV detection tools
- A simulation tool that can create artificial CNVs in sequencing data for the purpose of evaluating performance
- A visualisation and curation tool that can combine results from multiple CNV detection tools and allow the user to inspect them, along with relevant annotations.
All of these are integrated into one streamlined package that you can run easily on any data set you want to analyse.
We created Ximmer because although there are very many CNV detection tools, they can be hard to run and their performance can be highly variable and hard to estimate. This is why Ximmer builds in simulation: to allow a quick and easy estimation of the performance of any tool on any data set.
See an online example report to get an idea what Ximmer's output looks like.
- Java 1.8+
- Python 2.7, preferably the Anaconda installation (you will need support for pandas, numpy and other computational libraries that are not always easy to compile in a vanilla installation).
- R 3.2 or higher
- 24GB of RAM
or
- Use Docker!
See the online documentation for more details!