cyberbea / medusa-1

A draft genome scaffolder that uses multiple reference genomes in a graph-based approach.

Medusa

A draft genome scaffolder that uses multiple reference genomes in a graph-based approach.

Availability and dependencies

The present document provides a short guide for using the stand-alone version of the software Medusa. This software is not yet been published. The web interface, a stand-alone version and the present manual are available at http://combo.dbe.unifi.it/medusa. The source code is accessible at https://github.com/combogenomics/medusa.

Before try our software assure you to have installed the following packages:

1. MUMmer: this software is available at http://mummer.sourceforge.net/. Warning: after installation remember to add MUMmer relative path to the system variable PATH!

2. Python (from 2.6) and BioPython (from 1.61)

3. Java (from 1.6).

Now you are ready for obtaining and use our tool. The archive Medusa.tar.gz contains the following files:

1. A runnable .jar file Medusa.jar This is the program you will run.

2. A sub-folder with auxiliary scripts. Leave it in the same folder of the .jar

3. A sub-folder with a dataset you can use to test the tool.

Input and Output

The following inputs are required:

• The targetGenome file: a draft genome in fasta format. This is the genome you are interesting in scaffolding.

• An arbitrary long list of auxiliaryDraft files: other draft genomes in fasta format. The closest these organisms are related to the target, the better the results will be. This files are expected to be collected in a specific directory. It is possible to specify the path to the directory, see the command “-f” in the next section.

The following output files will be produced.

• targetGenome: a textual file containing information about your data. Number of scaffolds, N50 value etc..

• targetGenome.Scaffold.fasta: a fasta file with the sequences grouped in scaffolds. Between two contigs in the same scaffolds 100 N are inserted to fill the gap.

Usage

The project folder must contains:

• the targetGenome in fasta format.

• the medusa.jar file

• the scripts sub-folder “medusa-scripts”.

• the comparison genomes sub-folder “drafts”. (In alternative you can specify another path for this folder.)

You can now run the java application with the following parameters:

1. The option -i is required and indicates the name of the target genome file.

2. The option -o is optional and indicates the name of output fasta file.

3. The option -v (recommended) print on console the information given by the package MUMmer. This option is strongly suggested to understand if MUMmer is not running properly.

4. The option -f is optional and indicates the path to the comparison drafts folder.

5. The option -random is available (not required). This option allows the user to run a given number of cleaning rounds and keep the best solution. Since the variability is small In practice 5 rounds are usually sufficient to find the best score.

6. Finally the -h option provides a small recap of the previous ones.

An Example

When medusa archive is unzipped the files are the followings:

• the medusa.jar file.

• the scripts sub-folder “medusa-scripts”.

• the comparison genomes sub-folder “drafts”. Empty

• a folder “datasets”.

We provide you a dataset as an example. Move the target file in the main folder and all the comparison genomes in the “drafts” folder. Now you can run the program with the following command line:

java -jar medusa.jar -i targetgenome -v

Running the example

java -jar medusa.jar -f datasets -i datasets/Burkholderia_target -v

Compile

The project can be compiled by calling ant in the top-level directory:

ant