A tool to benchmark mappers and different parameters within minutes
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README.md

Teaser

Teaser analyzes the performance of read mappers based on a data set provided by you. After you enter key characteristics such as read length and reference genome, Teaser will simulate read data including the gold standard alignment. After the simulation, Teaser automatically runs and evaluates each mapper for the selected parameters and summarizes the results in a report. Teaser also supports benchmarking read mappers on real data or custom simulations, as well as testing new mappers and custom parameter sets. You can start using Teaser right now using our web application, or download and install it to use all advanced features.

Documentation

The GitHub Wiki contains all information on how to start benchmarking mappers with Teaser as well as customization and extension options.

Quick Start

Get Teaser

First, get Teaser and enter the Teaser directory with:

git clone https://github.com/Cibiv/Teaser.git
cd Teaser

Run Teaser using Docker

To run Teaser with Docker use the following command:

docker build -t teaser_git . && docker run -v $(pwd):/teaser -it teaser_git "/usr/bin/teaser_shell.sh"

Or: Run Teaser directly on your system

To install Teaser directly on your system without using Docker, follow the instructions below. For all installation requirements, see the Installation page in our wiki.

To install Teaser use the following command:

./install.py

Important: Teaser requires an internet connection during installation in order to download the mappers.

Benchmarking Mappers on an E. coli data set

To see if everything is working, you can try benchmarking mappers for a simple E. coli dataset using:

./teaser.py example_ecoli.yaml

To start the Teaser graphical interface use:

./server.py

Then head to http://localhost:8888 in a web browser of your choice.

Citation

If you use Teaser to optimize read mapping in your study, please consider citing:

Smolka M, Rescheneder P, Schatz MC, von Haeseler A and Sedlazeck FJ. Teaser: Individualized benchmarking and optimization of read mapping results for NGS data. Genome Biology 2015, 16:235 (22 October 2015). DOI: 10.1186/s13059-015-0803-1

License

Teaser is made available under the MIT License.