Cloud-Based Workbench

Using the cloud-based version of the sRNA Workbench on Amazon Web Services (AWS)

We have created a preconfigured virtual machine (known as an Amazon Machine Instance or AMI) for using the workbench on AWS. The following page details instructions for creating an AWS account (if you do not already have one) and obtaining and running the workbench via the EC2 (Elastic Compute Cloud) web service for Windows, MacOS and Linux.

Create an AWS account

Follow the following link to sign up to AWS if you haven’t already created an account:

Sign up for an AWS account

Launch the Workbench Amazon Machine Instance (AMI)

Use our preconfigured AMI

Follow the steps required to configure an AMI from the EC2 management console. Usage options are up to you and depend on your budget. If using from the free tier (see this page for more info) then ensure you select the free tier eligible options.

The preconfigured AMI containing libraries for displaying a GUI over the linux install and the workbench binaries (plus tutorial data) can be found at the following bookmark:

https://eu-west-2.console.aws.amazon.com/ec2/v2/home?region=eu-west-2#Images:visibility=public-images;search=ami-3bfee25f;sort=name

Right click on the AMI and then click on ‘Launch’. You will then be asked to choose an Instance Type; please select an instance with the resourcesL dependant on your needs and budget.

Once you have selected an instance type you should consider how much storage you need. EC2 allows up to 30GB but if you require more then you should also create an AWS S3 bucket to store the data.

Click on ‘Review and Launch’ and finally ‘Launch’. You will then be asked to select an existing key pair or create a new one. Amazon EC2 uses public–key cryptography to encrypt and decrypt login information. Public–key cryptography uses a public key to encrypt a piece of data, such as a password, then the recipient uses the private key to decrypt the data. The public and private keys are known as a key pair.
We recommend creating one and storing it in a safe location as you will need it later.

Instructions per operating system

This section contains instructions on how to connect and use the AWS Workbench instance on Windows, MacOS and Linux (Ubuntu 16.04).

Windows

Required software

You are going to need an SFTP client, we recommend FileZilla that can download from the following link:
https://filezilla-project.org/download.php
However, if there is another client you are more familiar with then that will work too.

You are going to need a terminal emulator that supports SSH. We recommend PuTTY and you can download it from the following link:
https://www.chiark.greenend.org.uk/~sgtatham/putty/latest.html
However, if there is another client you are more familiar with then that will work too.
Next, you’re going to need an X Window System Server and we recommend using Xming that can be downloaded from the following link:
https://sourceforge.net/projects/xming/

Converting private key file

Once you have downloaded and installed PuTTY and Xming and Xming is running, you need to convert the .pem file from AWS into a format that PuTTY and FileZilla will accept. To do this, we are going to use a software app called PuTTYgen that is installed with PuTTY. Open PuTTYgen, click on ‘Load’ and change the file type to ‘All files (*.*)’. Select the .pem file you downloaded when you launched the AWS instance.
Next, where it says ‘Type of key to generate’ select the SSH-1 (RSA). Finally, click ‘Save private key’ ignoring any warnings and choose a save location.

Uploading sequence data

Open FileZilla, click on ‘File’ and then ‘Site Manager’ from the menu bar and then ‘New Site’. Find your instance public IP address from the AWS EC2 dashboard and put it into the ‘Host’ box. Make sure you select SFTP as the protocol from the dropdown menu. Next, select ‘Key file’ in the ‘Logon type’ dropdown and enter ‘ubuntu’ as the user. Finally, click browse for the key file and select the .ppk file you created in the previous step.
From here you should be able to upload any data that you need to perform the analysis.

Command line analysis

Open PuTTY and in the ‘Host Name’ box enter ‘ubuntu@your-instance-ip-address’. Next, click the SSH category in the options pane on the left. In the ‘Auth’ section, select ‘Browse’ and choose the key file you created with PuTTYgen. Finally, click the ‘X11’ option and check the ‘Enable X11 forwarding’ check box.

Please make sure that Xming is running when you connect to the AWS instance.
Once you are connected, change directory to the location of the workbench folder using:

cd Desktop/workbench/

You can then run the workbench as you would normally using the command line for example:

java -Xms$g -Xmx$g -jar Workbench.jar -tool [tool_name] [tool_parameters]

Replace the $ symbol with the amount of RAM in GB you wish to assign to the workbench

Analysis using the graphical user interface (GUI)

To access the GUI on the AWS instance you must first install a VNC viewer. We recommend using RealVNC that can be downloaded from the following location:

https://www.realvnc.com/en/connect/download/viewer/

Open PuTTY and in the ‘Host Name’ box enter ‘ubuntu@your-instance-ip-address’. Next, click the SSH category in the options pane on the left.

In the ‘Auth’ section, select ‘Browse’ and choose the key file you created with PuTTYgen. Finally, click ‘Tunnels’ in the options panel on the left and enter ‘5900’ in the ‘Source port’ text box and ‘127.0.0.1:5901’ in the ‘Destination’ textbox.

Click on ‘Add’ to save the tunnel and then click ‘Open’.
Open VNC Viewer and click ‘File’ and then ‘New connection’. Next, put ‘127.0.0.1:5900’ in the ‘VNC server’ text box and click ‘Ok’.

Double click the new connection and click continue on any warnings that appear. Finally, enter ‘UEAsRNAWorkbench’ as the password and click ‘OK’.
Once you are connected, navigate to the location of the workbench directory on the Desktop. Open the folder, right click and select ‘Open in Terminal’. Finally, run the workbench using the command:

java -Xms$g -Xmx$g -jar Workbench.jar -tool

Replace the $ symbol with the amount of RAM in GB you wish to assign to the workbench

You can now perform the analysis through the GUI as normal.

MacOS

Required software

You are going to need an SFTP client, we recommend FileZilla that can download from the following link:
https://filezilla-project.org/download.php
However, if there is another client you are more familiar with then that will work too.

You’re going to need an X Window System Server and we recommend using XQuartz that can be downloaded from the following link:
https://www.xquartz.org/

Uploading sequence data

Open FileZilla, click on ‘File’ and then ‘Site Manager’ from the menu bar and then ‘New Site’. Find your instance public IP address from the AWS EC2 dashboard and put it into the ‘Host’ box. Make sure you select SFTP as the protocol from the dropdown menu. Next, select ‘Key file’ in the ‘Logon type’ dropdown and enter ‘ubuntu’ as the user. Finally, click browse for the key file and select the .ppk file you created in the previous step.
From here you should be able to upload any data that you need to perform the analysis.

Command line analysis

Please make sure XQuartz is running.

here you should be able to ssh to the AWS instance through the terminal with the -Y flag set using the following command:

ssh -Y -i “/your/pem/file/location/file.pem “ubuntu@your-instance-public-ip”

Once you are connected you can navigate to the workbench directory on the desktop and run the workbench through the command line as normal.

However, you must set the -Dprism.order=sw flag. For example, to run a tool via the command line, the command would be:

java -jar -Dprism.order=sw /path/to/Workbench.jar -tool [tool name] [parameters]

Analysis using the graphical user interface (GUI)

To access the GUI on the AWS instance you must first install a VNC viewer. We recommend using RealVNC that can be downloaded from the following location:
https://www.realvnc.com/en/connect/download/viewer/
However, any VNC viewer should work the same way.
Next, we need to set a password for the user account on the AWS instance. Connect to the AWS instance via ssh using the following command:

ssh -i “/your/pem/file/location/file.pem “ubuntu@your-instance-public-ip”

Next, run the following command and set your chosen password:

sudo passwd ubuntu

Now exit the ssh by running the command:

exit

Open a new terminal window and execute the following command:

ssh -NfL 5900:127.0.0.1:5901 “ubuntu@your-instance-public-ip”

Open VNC Viewer and click ‘File’ and then ‘New connection’. Next, put ‘127.0.0.1:5900’ in the ‘VNC server’ text box and click ‘Ok’. Double click the new connection and click continue on any warnings that appear. Finally, enter ‘UEAsRNAWorkbench’ as the password and click ‘OK’.

Once you are connected, navigate to the location of the workbench directory on the Desktop. Open the folder, right click and select ‘Open in Terminal’. Finally, run the workbench using the command:

java -jar Workbench.jar

You can now perform the analysis through the GUI as normal.

 

Linux

Required software

You are going to need an SFTP client, we recommend FileZilla that can download from the following link:
https://filezilla-project.org/download.php
However, if there is another client you are more familiar with then that will work too.

You’re going to need an X11 client and we recommend installing xauth if you haven’t already using the following command:

sudo apt-get install xauth

Uploading sequence data

Open FileZilla, click on ‘File’ and then ‘Site Manager’ from the menu bar and then ‘New Site’. Find your instance public IP address from the AWS EC2 dashboard and put it into the ‘Host’ box. Make sure you select SFTP as the protocol from the dropdown menu. Next, select ‘Key file’ in the ‘Logon type’ dropdown and enter ‘ubuntu’ as the user. Finally, click browse for the key file and select the .ppk file you created in the previous step.
From here you should be able to upload any data that you need to perform the analysis.

Command line analysis

You should be able to ssh to the AWS instance through the terminal with the -Y flag set using the following command:

ssh -Y -i “/your/pem/file/location/file.pem “ubuntu@your-instance-public-ip”

Once you are connected you can navigate to the workbench directory on the desktop and run the workbench through the command line as normal. However, you must set the -Dprism.order=sw flag. For example, to run a tool via the command line, the command would be:

java -jar -Dprism.order=sw /path/to/Workbench.jar -tool [tool name] [parameters]

Analysis using the graphical user interface (GUI)

To access the GUI of the AWS instance we must first install a VNC viewer. We recommend installing xtightvncviewer using the following command:

sudo apt-get install xtightvncviewer

Next, we need to set a password for the user account on the AWS instance. Connect to the AWS instance via ssh using the following command:

ssh -i “/your/pem/file/location/file.pem “ubuntu@your-instance-public-ip”

Next, run the following command and set your chosen password:

sudo passwd ubuntu

Now exit the ssh by running the command:

exit

Open a new terminal window and execute the following command:

xtightvncviewer -via “ubuntu@your-instance-public-ip” -compresslevel 9 -quality 5 :1

Enter the password you set in the previous step and then then enter the password ‘UEAsRNAWorkbench’ as the VNC authentication password.
Once you are connected, navigate to the location of the workbench directory on the Desktop.

Open the folder, right click and select ‘Open in Terminal’. Finally, run the workbench using the command:

java -jar /path/to/Workbench.jar

You can now perform the analysis through the GUI as normal.

A suite of tools for analysing micro RNA and other small RNA data from High-Throughput Sequencing devices