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Aggiunta skipmapping e keepTemp (#32)


Aggiunta di due opzioni alla command line e relativa gestione nel sorgente Scaffolder.java:


    

  • l’opzione “skipMapping” indicando il percorso della cartella dove cercare gli output di mummer per partire dalla fase 2
    • 

  • l’opzione “keepTemp”, di default a false; se specifica deve essere seguita dal valore true nella linea di comando per saltare il blocco in cui vengono cancellati i file temporanei .coords, .delta, .mgaps e .ntref.
    •
5年前65次提交
README.md

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 has not yet been published. A web interface is available at http://combo.dbe.unifi.it/medusa. The source code, precompiled version and the present manual are accessible at https://github.com/combogenomics/medusa.

Medusa depends on the following packages being installed on your system and available in your PATH:

  1. MUMmer: this software is available at http://mummer.sourceforge.net/.

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

  3. Java (from 1.6).

The following Python packages should be present:

  1. Networkx

  2. Numpy

  3. Biopython

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 python scripts needed to run the program (medusa_scripts). Leave it in the same folder of the .jar file.

  3. A sub-folder with a dataset (test) that can be used to test the tool.

  4. A sub-folder with scripts useful for benchmarking 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 interested 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. These 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_SUMMARY: a textual file containing information about your data. Number of scaffolds, N50 value etc..

  • targetGenomeScaffold.fasta: a fasta file with the sequences grouped in scaffolds. Contigs in the same scaffolds are separated by 100 Ns by default, or a variable number of Ns (estimate of the distance between the contigs), if the option “-d” is used.

The following output files can optionally be produced.

  • targetGenome_distanceTable: a tabular file with the estimation of the distance between successive contigs (bp).

  • targetGenome_network.gexf: the contig network in gexf format.

  • targetGenome_cover.gexf: the final path cover in gexf format.

Usage

The project folder must contain:

  • 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 usinf the “-f” option)

Medusa can be run 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, 5 rounds are usually sufficient to find the best score.

  6. The option -w2 is optional and allows for a sequence similarity based weighting scheme. Using a different weighting scheme may lead to better results.

  7. The option -d allows for the estimation of the distance between pairs of contigs based on the reference genome(s): in this case the scaffolded contigs will be separated by a number of N characters equal to this estimate. The estimated distances are also saved in the “*_distanceTable” file. By default the scaffolded contigs are separated by 100 Ns.

  8. The -gexf is optional. With this option the gexf format of the contig network and the path cover are porvided.

  9. The option -n50 allows the calculation of the N50 statistic on a FASTA file. In this case the usage is the following: java -jar medusa.jar -n50 All the other options will be ignored.

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

The Medusa archive

When medusa archive is unzipped the following files will be extracted:

  • the medusa.jar file.

  • the scripts sub-folder “medusa_scripts”.

  • the utility test scripts folder “medusa_testing”

  • a folder “test”, containing one test bacterial datasets.

Running an example

java -jar medusa.jar -f test/reference_genomes/ -i test/Rhodobacter_target.fna -v

Additional datasets for benchmarking

Additional datasets can be retrieved at the medusa_datasets repository https://github.com/combogenomics/medusa_datasets.

Just type

git clone https://github.com/combogenomics/medusa_datasets.git

Compile

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

ant
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README.md
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