# Running a next-gen sequence analysis course using Amazon Web Services

So, I've been teaching a course on next-generation sequence analysis for the last week, and one of the issues I had to deal with before I proposed the course was how to deal with the volume of data and the required computation.

You see, next-generation sequence analysis involves analyzing not just entire genomes (which are, after all, only 3gb or so in size) but data sets that are 100x or 1000x as big! We want to not just map these data sets (which is CPU-intensive), but also perform memory-intensive steps like assembly. If you have a class with 20+ students in it, you need to worry about a lot of things:

• computational power: how do you provide 24 "good" workstations
• memory
• disk space
• bandwidth
• "take home" ability

One strategy would be to simply provide some Linux or Mac workstations, with cut-down data sets. But then you wouldn't be teaching reality -- you'd be teaching a cut-down version of reality. This would make the course particularly irrelevant given that one of the extra-fun things about next-gen sequence analysis is how hard it is to deal with the volume of data. You also have to worry that the course would be made even more irrelevant because the students would leave the course and be unable to use the information without finding infrastructure and installing a bunch of software and then administering the machine.

While I enjoy setting up computers and installing software and managing users, I'm clearly masochistic. It's also entirely besides the point for bioinformaticians and biologists - they just want to analyze data!

The solution I came up with was to use Amazon Web Services and rent some EC2 machines. There's a large variety of hardware configurations available (see instance types) and they're not that expensive per hour (see pricing).

This has worked out really, really well.

It's hard to enumerate the benefits, because there have been so many of them ;). A few of the obvious ones --

We've been able to write tutorials (temporary home here: http://ged.msu.edu/angus/) that make use of specific images and should be as future-proof as they can be. We've given students cut and paste command lines that Just Work, and that they can tweak and modify as they want. If it borks, they always just throw it away and start from a clean install.

It's dirt cheap. We spent less than \$50 the first week, for ~30 people using an average of 8 hours of CPU time. The second week will increase to an average of 8 hours of CPU time a day, and for larger instances -- so probably about \$300 total, or maybe even \\$500 -- but that's ridiculously cheap, frankly, when you consider that there are no hardware issues or OS re-install problems to deal with!

Students can choose whatever machine specs they need in order to do their analysis. More memory? Easy. Faster CPU needed? No problem.

All of the data analysis takes place off-site. As long as we can provide the data sets somewhere else (I've been using S3, of course) the students don't need to transfer multi-gigabyte files around.

The students can go home, rent EC2 machines, and do their own analyses -- without their labs buying any required infrastructure.

Home institution computer admins can use the EC2 tutorials as documentation to figure out what needs to be installed (and potentially, maintained) in order for their researchers to do next-gen sequence analysis.

The documentation should even serve as a general set of tutorials, once I go through and remove the dependence on private data sets! There won't be any need for students to do difficult or tricky configurations on their home machines in order to make use of the tutorial info.

So, truly awesome. I'm going to be using it for all my courses from now on, I think.

There have been only two minor hitches.

First, I'm using Consolidated Billing to pay for all of the students' computer use during the class, and Amazon has some rules in place to prevent abuse of this. They're limiting me to 20 consolidated billing accounts per AWS account, which means that I've needed to get a second AWS account in order to add all 30 students, TAs, and visiting instructors. I wouldn't even mention it as a serious issue but for the fact that they don't document it anywhere, so I ran into this on the first day of class and then had to wait for them to get back to me to explain what was going on and how to work around it. Grr.

Second, we had some trouble starting up enough Large instances simultaneously on the day we were doing assembly. Not sure what that was about.

Anyway, so I give a strong +1 on Amazon EC2 for large-ish style data analysis. Good stuff.

cheers, --titus

Posted by Tracy on 2010-07-07 at 12:40.

Following Titus' success, I used the Amazon EC2 to run a web
application for a Microbial Metagenomics course.  Having 16 people use
the web tool on our one server at the same time would have made it
slow for everyone.  EC2 solved this problem.  I ran 4 servers and it
was cheap and speedy.      Since I was using a web tool, I wanted
people to access it through a web interface.  So, to add to Titus'
notes, I only had the standard Linux issue of enabling cgi-scripts.
To address this there were just a few steps.    I used a Debian server
(ami-ed16f984), so these are specific to Debian and things that are
the same if you're setting up any Debian server.    When I started up
the server at Amazon, I added a rule to allow http in the security
settings.    Once the server was started, and I logged in, I installed
apache  'apt-get -y install apache2'    Then added the ability to do
cgi, by adding a 'cgi' file to /etc/apache2/conf.d/ that has the line
"AddHandler cgi-script .cgi"    In the /etc/apache2/sites-
available/default file, I took out the "AllowOverride None" statements
in the /var/www directories section    Then to the
/etc/apache2/apache2.conf file added  "  &lt;Directory "/"&gt;
AllowOverride All  &lt;/Directory&gt;  "      I added a .htaccess file
to /var/www with    Options +ExecCGI  AddHandler cgi-script .cgi
and restarted apache with 'apache2ctl restart'    Then I tested to see
if cgi was working by creating a test script hello.cgi    "  #!
/usr/bin/python  print 'Content-type: text/html\n\nhello, world'  "
and putting it in /var/www    Then I went to
'http://amazon_server_ip_address/hello.cgi" to see if it was working
A new web server, ready to go!