We do not have any estimates posted on the wiki currently for example
usage on the cloud, but this is a good idea and the team is discussing
the best way to add in something like this. The difficulty comes with
how variable actual job run-times can be, but there still are some ways
to break this down.
These examples are based on how *long* an instance would be up and
center on two primary costs: the type of instance and the size of the
EBS volume. The details at Amazon are on this link:
1 extra large high memory instance capable of RNA-seq w/ 200GB storage:
+ 1 worker instance, $10/day/each.
1 basic instance capable of general text manipulation w/ 50GB storage:
I am not sure if you will be using GATK or SAM Tools for your
processing, but running any variant analysis pipeline would be somewhat
similar to an RNA-seq pipeline since it would involve mapping, large
data file manipulations, etc. For you particular case, the data storage
would be larger than the estimate above, so using the table at Amazon
should help you to calculate a figure that reflects your storage needs.
It is difficult to say how long any job will run purely based on the
size of the inputs, as content and parameter settings have a significant
effect on run time, but after the first job, or first time through a
complete workflow, if the data is somewhat homogenous, you may be able
to estimate a total from there for future runs. Although I or almost
anyone else can tell you that these sorts of experiments can pop out
with surprises now and then!
Others on the list using a cloud are welcomed to post comments to this
thread. Once we get the initial wiki table posted, it will be open to
community input, so that this type of actual usage data can be captured.
If you or anyone else also wants to send back results meanwhile (post to
thread and/or ticket, with experiment & instance detail) please do, here
is the new development -> https://trello.com/c/pMbri7QI
Hopefully this helps a little bit! Apologies for not being able to give
more detail, this is a tough question to answer with precision for a
complete workflow! A pool of case examples is probably the best way to
get a bead on this data, so that's part of the goal now.
On 4/10/13 9:20 AM, Thompson, Andrew wrote:
Yes, that was my problem, I skipped some steps by relying too much on the
screencast and ignoring the text.
Now I am reluctant to launch the AMI as I having trouble estimating my usage
and costs on AWS - as a new user I have little
idea what to set many of the parameters in the usage calculator. Are there any
examples of typical parameters and costs for running Galaxy on the cloud?
My first task is to map about 80 gbp of total paired end reads from genomic DNA
from two accessions to a 900 mbp reference genome and
then find SNPs and INDELs. A ball-park figure would be reassuring!
From: Jennifer Jackson [j...@bx.psu.edu]
Sent: 09 April 2013 15:11
To: Thompson, Andrew
Subject: Re: [galaxy-user] merging fastq files
My first guess is that perhaps the region is not set correctly?
See " Step 1: One Time Amazon Setup", subsection 2, where region is " set your AWS
Region to US East (Virginia)".
The image in the wiki for step 1.2 is slightly outdated, instead it will look
Please give this a try and let us know if you continue to have issues.
On 4/8/13 3:56 PM, Thompson, Andrew wrote:
Thanks. I have merged the files and end up with 4 x 47 G fastq files for read
mapping to the reference.
It seems this is too much data to analyse on the public main instance if the
size limit is 250 G?
So I tried to set up the cloud option following the screencast
(http://screencast.g2.bx.psu.edu/cloud/), but when I search for the current AMI
name (861460482541/galaxy-cloudman-2011-03-22) it is not found in the list of
community AMIs under Amazon's EC2 Management Console. Any ideas why this is not
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