Bioinformatic Tools for studying evolution of metabolic diversity Tools:
- EvoMining
- CORASON
CORASON is a visual tool that searchs for gene clusters similar to a given one, if exists a genomic core on this clusters CORASON finds it and sort them phylogenetically according to its core.
Input: query gen and RAST genome database.
Output: SVG graph with clusters sorted according to core genomic tree from clusters.
CORASON was developed to find and prioritize biosynthetic gene clusters, but can be used for any kind of clusters.
####Advantages
-SVG graphs Scalable graphs that allows metadata easy display.
-Interactive CORASON is not an static database, it allows you to explore your own genomes.
-Reproducibility CORASON runs on docker, that allows to perform the same analysis even if you change your local perl/blast/muscle/Gblocks/quicktree versions.
- Install docker engine
- Download nselem/evodivmet docker-image
- Run CORASON
Follow the steps, and type the commands into your terminal, do not type $.
CORASON runs on docker, if you have docker engine installed skip this step. This are Linux minimal docker installation guide, if you don't use Linux or you look for a detailed tutorial on Linux/Windows/Mac Docker engine installation please consult [Docker getting Starting] (https://docs.docker.com/linux/step_one/).
$ curl -fsSL https://get.docker.com/ | sh
*if you don’t have curl search on this document curl installation
Then type:
$ sudo usermod -aG docker your-user
Log out from your ubuntu session (restart your machine) and get back in into your user session before the next step. You may need to restart your computer and not just log out from your session in order to changes to take effect.
Test your docker engine with the command:
$ docker run hello-world
###1 Download CORASON images from DockerHub
$ docker pull nselem/evodivmet:latest
#####Important
docker pull may be slow depending on your internet connection, because nselem/evodivmet docker-image is being downloaded, its only this time won’t happen again.
Create an empty directory that contains your [[Input Files]]: RAST-genome data base, Rast_Ids file and file.query
$ mkdir mydir
place inside my dir your files:
GENOMES (dir)
RAST_IDs (tab separated file)
file.query (aminoacid fasta file) Save as many queries as you wish to process.
$ docker run -i -t -v /mypath/mydir:/usr/src/CORASON nselem/evodivmet /bin/bash
/mypath/mydir/ is your local directory were you store your inputs, can have any name you choose.
Use absolute paths, if you don’t know the path to your dir, place yourself on your directory and type on the terminal
$ pwd
/usr/src/CORASON is fixed at the docker images, you should always use this name.
$ corason.pl -q DesC.query -rast_ids RAST_CORASON -s 242137
once you finished all your queries exit the container
$ exit
Outputs will be on the new folder /mypath/mydir/query
- query.svg SVG file with clusters similar to you query sorted phylogenetically
- query_Report Functional cluster genomic core report.
- *.tre Phylogenetic tree of the genomic cluster core.
On this example query file was DesC.query and input directory was /home/mydir then output files are located on /home/mydir/DesC.
Code and docker file located at:
[Code] (https://github.com/nselem/EvoDivMet )
[Docker] (https://hub.docker.com/r/nselem/evodivmet/ )
$ which curl$ sudo apt-get update$ sudo apt-get install curl
- Create a direct access with Logo
- Redirect process to a different folder so multiple runs can be performed without data mess
- [1/2] Write the tutorial
- Write a myRast Docker file
- Learn Docker-Apache to link with Evomining
- Test with many users