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Setting Up Mezzanine Projects in AWS

Go to EC2 management console, Security groups and add a Custom TCP inbound rule with port 8000. Select “Anywhere” from the list. Then follow [this to install Mezzanine]({% post_url 2015-05-01-how-to-install-mezzanine-on-ubuntulinux-mint %}) Above tutorial is also explains setting up a site record. Mezzanine default site record is 127.0.0.1:8000 which should be 0.0.0.0:8000 in our case. So, enter 0.0.0.0:8000 when you’re asked to enter a site record when you ru python manage.py createdb Also, you might still need to provide this site record while running the development server:

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Getting Started with Your AWS Instance and Installing and Setting Up an Apache Server

Update and upgrade packages: sudo apt-get update sudo apt-get upgrade Install Apache server: sudo apt-get install apache2 Set up a root folder in home folder and create an index file for testing: mkdir ~/www echo ‘Hello, World!’ > ~/www/index.html Set up your virtual host: sudo cp /etc/apache2/sites-available/000-default.conf /etc/apache2/sites-available/000-www.conf sudo nano /etc/apache2/sites-available/000-www.conf Modify DocumentRoot to point your “www” folder in home folder (e.g. /home/ubuntu/www) And add following lines after DocumentRoot line:

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AWS Start an Instance and Connect to it

Go to EC2 management console Create a new key-pair if necessary and download it Launch an instance Add HTTP security group for web applications over HTTP Get public DNS Change permissions on key-pair file: 1chmod 400 path/to/your/file.pem Connect: 1ssh -i path/to/your/file.pem ubuntu@PUBLIC_DNS Note: ubuntu is for connecting an Ubuntu 64 bit instance. It’s different for others

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How to Get Path to or Directory of Current Script in R

Use following code to get the path to or directory of current (running) script in R: 1scr_dir <- dirname(sys.frame(1)$ofile) 2scr_path <- paste(scr_dir, "script.R", sep="/") Taken from SO

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How to Get (or Load) NCBI GEO Microarray Data into R using GEOquery Package from Bioconductor

R, especially with lots of Bioconductor packages, provides nice tools to load, manage and analyze microarray data. If you are trying to load NCBI GEO data into R, use GEOquery package. Here, I’ll describe how to start with it and probably in my future posts I’ll mention more. Installation 1source("http://bioconductor.org/biocLite.R") 2biocLite("GEOquery") Usage 1library(GEOquery) 2gds <- getGEO("GDS5072") or 1library(GEOquery) 2gds <- getGEO(filename="path/to/GDS5072.soft.gz") getGEO function return a complex class type GDS object which contains the complete dataset.

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How to Install Mezzanine on Ubuntu/Linux Mint [Complete Guide]

Mezzanine is a CMS application built on Django web framework. The installation steps are easy but your environment may not just suitable enough for it work without a problem. So, here I’m going to describe complete installation from scratch on a virtual environment. First of all, install virtualenv: $ sudo apt-get install python-virtualenv Then, create a virtual environment: $ virtualenv testenv And, activate it: $ cd testenv $ source bin/activate

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How to Clear (or Drop) DB Table of A Django App

Let’s say you created a Django app and ran python manage.py syncdb and created its table. Everytime you make a change in the table, you’ll need to drop that table and run python manage.py syncdb again to update. And how you drop a table of a Django app: $ python manage.py sqlclear app_name | python manage.py dbshell Drop tables of an app with migrations (Django >= 1.8): $ python manage.py migrate appname zero Recreate all the tables:

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Salmonella - Host Interaction Network - A Detailed, Better Visualization

We’re almost done with the analyses and we’re making the final visualization of the network. As I previously posted, the network was clustered and visualized by time points. After that, we have done several more analyses and here I report how we visualized them. I’m going to post more about how we did the analyses separately. First, the nodes are grouped into experimental and not experimental (PCSF nodes). This can easily be done by parsing experimental network output and network outputs of PCSF.

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GO Enrichment of Network Clusters

In my previous post, I mentioned how I clustered the network we obtained at the end. For functional annotation gene ontology (GO) enrichment has been done on these clusters. There were 20 clusters and the HGNC names are obtained separately for each cluster and using DAVID functional annotation tool API, GO and pathway annotations are collected per cluster and these are saved separately. http://david.abcc.ncifcrf.gov/api.jsp?type=OFFICIAL_GENE_SYMBOL&tool=chartReport&annot=GOTERM_BP_FAT,GOTERM_CC_FAT,GOTERM_MF_FAT,BBID,BIOCARTA,KEGG_PATHWAY&ids=HGNC_NAME1,HGNC_NAME2,HGNC_NAME3,... Above URL was used to obtain chart report for some GO and pathways chart records.