Skip to content

Array Server on Cloud

Array Server can also run jobs on the cloud. It only requires the ArrayServer administrator to perform a simple one-time set up. To other users, it is a transparent process. They do not need to do any extra setup, nor will they find any differences with other non-cloud jobs.

This tutorial is drafted for standard users. To configure Server with Cloud, please contact Omicsoft Support to get the manual for Server on Cloud admin. After ArrayServer admin has configured the Server with Cloud, standard users do not need to set up Cloud Preferences but only need to connect to the server with cloud integration through the Server tab:

image38_png

When connected, the window looks the same as the server window.

Notice that the Cloud tab will not appear.

image39_png

Uploading Files to Server cloud

Before running server jobs on cloud, the users should upload the data files on the specific cloud folder they have been assigned. Go to Server File | Browse Files window:

image40_png

Then go to the cloud folder configured in advance. Please contact the admin if the user does not know where their cloud folder can be found. In the folder, users can create their own folder and upload the data the same way as running a server project:

image41_png

Run Server Project on Cloud

Once files are uploaded to the cloud folder, users can not run a server project on the cloud. Please create a server project in the Analysis tab first. The analysis window and analysis steps are the same as running a server project. When adding data to a project, remember to browse the right cloud folder for your files:

image42_png

After sending the data to queue, the job progress could be monitored the same way as server project:

image43_png

Run Multiple Jobs on Cloud

When running multiple jobs (For example, multiple samples sequencing data alignment), multiple cloud instances will be allocated. This makes it much faster to perform the analyses.

To test this, users can use the RNA-seq data downloaded in the previous chapter. For illustration purpose, we will only use two samples to reduce the process time. Again, remember to go to load the data to the correct cloud folder prior to starting the project:

image44_png

image45_png

The demo dataset is paired-end sequencing data; please check the Reads are paired check box. For Server project to run on cloud, the users must specify output folder. The directory has to be under the cloud folder (not necessary to be the same cloud folder as the raw data). The principle is that all data, including raw and analyzed data, are on the cloud, while users' local machines and company server only store small data objects linking to the files on cloud. Upon job submission, again, the job could be monitored:

image46_png

The users can right click on the job and select View Full Log:

image47_png

In the Log window, as you can see, the jobs are being submitted to cloud NGS instances, 2 cloud instances will be started as we have two samples to align:

image48_png

As a general user, you cannot monitor the Cloud Instances for Server Cloud.

The users can go back to the Analysis tab and continue any downstream analyses and visualization:

image49_png

Congratulations! Now you can successfully run server projects on cloud!