medusa on Biowulf
A draft genome scaffolder that uses multiple reference genomes in a graph-based approach.
Features
- MEDUSA (Multi-Draft based Scaffolder), an algorithm for genome scaffolding. MEDUSA exploits information obtained from a set of (draft or closed) genomes from related organisms to determine the correct order and orientation of the contigs. MEDUSA formalizes the scaffolding problem by means of a combinatorial optimization formulation on graphs and implements an efficient constant factor approximation algorithm to solve it. In contrast to currently used scaffolders, it does not require either prior knowledge on the microrganisms dataset under analysis (e.g. their phylogenetic relationships) or the availability of paired end read libraries.
References:
- Bosi E, Donati B, Galardini M, Brunetti S, Sagot MF, Lió P, Crescenzi P, Fani R, Fondi M. MeDuSa: a multi-draft based scaffolder. Bioinformatics. 2015 Aug 1;31(15):2443-51. doi: 10.1093/bioinformatics/btv171. Epub 2015 Mar 25. PMID: 25810435. PubMed | Journal
Documentation
- medusa Main Site:Main Site
Important Notes
- Module Name: medusa (see the modules page for more information)
- Current medusa command lines could be run as
medusa --help
- Environment variables set
- $MEDUSAPATH
- Example files in $MEDUSA_TEST_DATA
Interactive job
Interactive jobs should be used for debugging, graphics, or applications that cannot be run as batch jobs.
Allocate an interactive session and run the program.
Sample session (user input in bold):
[user@biowulf]$ sinteractive
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job
[user@cn3144 ~]$ module load medusa
[user@cn3144 ~]$ cp -r $MEDUSA_TEST_DATA/* .
[user@cn3144 ~]$ medusa --help
medusa --help
Medusa version 1.6
usage: java -jar medusa.jar -i inputfile -v
available options:
-d OPTIONAL PARAMETER;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
-f <> OPTIONAL PARAMETER; The option *-f*
is optional and indicates the path
to the comparison drafts folder
-gexf OPTIONAL PARAMETER;Conting network
and path cover are given in gexf
format.
-h Print this help and exist.
-i <> REQUIRED PARAMETER;The option *-i*
indicates the name of the target
genome file.
-n50 <> OPTIONAL PARAMETER; 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.
-o <> OPTIONAL PARAMETER; The option *-o*
indicates the name of output fasta
file.
-random <> OPTIONAL PARAMETER;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.
-scriptPath <> OPTIONAL PARAMETER; The folder
containing the medusa scripts.
Default value: medusa_scripts
-v RECOMMENDED PARAMETER; 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.
-w2 OPTIONAL PARAMETER;The option *-w2*
is optional and allows for a
sequence similarity based weighting
scheme. Using a different weighting
scheme may lead to better results.
[user@cn3144 ~]$ medusa -f reference_genomes/ -i Rhodobacter_target.fna -v
INPUT FILE:Rhodobacter_target.fna
------------------------------------------------------------------------------------------------------------------------
Running MUMmer...done.
------------------------------------------------------------------------------------------------------------------------
Building the network...done.
------------------------------------------------------------------------------------------------------------------------
Cleaning the network...done.
------------------------------------------------------------------------------------------------------------------------
Scaffolds File saved: Rhodobacter_target.fnaScaffold.fasta
------------------------------------------------------------------------------------------------------------------------
Number of scaffolds: 78 (singletons = 32, multi-contig scaffold = 46)
from 564 initial fragments.
Total length of the jointed fragments: 4224838
Computing N50 on 78 sequences.
N50: 143991.0
----------------------
Summary File saved: Rhodobacter_target.fna_SUMMARY
[user@cn3144 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$
Batch job
Most jobs should be run as batch jobs.
Create a batch input file (e.g. medusa.sh). For example:
#!/bin/bash set -e module load medusa medusa -f reference_genomes/ -i Rhodobacter_target.fna -v
Submit this job using the Slurm sbatch command.
sbatch --cpus-per-task=2 --mem=2g medusa.sh
Swarm of Jobs
A swarm of jobs is an easy way to submit a set of independent commands requiring identical resources.
Create a swarmfile (e.g. medusa.swarm). For example:
cd dir1;medusa -f reference_genomes/ -i 1_target.fna -v cd dir2;medusa -f reference_genomes/ -i 2_target.fna -v cd dir3;medusa -f reference_genomes/ -i 3_target.fna -v cd dir4;medusa -f reference_genomes/ -i 4_target.fna -v
Submit this job using the swarm command.
swarm -f medusa.swarm [-g #] [-t #] --module medusawhere
| -g # | Number of Gigabytes of memory required for each process (1 line in the swarm command file) |
| -t # | Number of threads/CPUs required for each process (1 line in the swarm command file). |
| --module medusa | Loads the medusa module for each subjob in the swarm |