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Team Name

Reetu and Friends

 

Team Members

Reetu Tuteja - Created the Sequenceserver2.0 app on Discovery Environment

Jennen Maryniak - presentation

Jiatian Wang - initial Makeflow and Work Queue scripts

Hayden Dunn

Erika Tapia - benchmarking and documentation

Andy Garcia

Nick Reppe

 

Project Summary:

CyVerse provides life scientists with a powerful computational infrastructure to handle huge datasets and complex analyses that enable data-driven discovery. CyVerse's extensible platforms provide data storage, bioinformatics tools, image analyses, cloud services, APIs, and more.


 

The Discovery Environment (DE) is a key product of the CyVerse cyber-infrastructure, providing a modern web interface for powerful computing. The visual and interactive computing environment (VICE) is the recently introduced feature within CyVerse’s Discovery Environment (DE) for running interactive apps.

 

Our midterm project is an implementation of CyVerse's Discovery Environment using an app we created via VICE within CyVerse. 


Project Description


Code Availability


Initial Scripts:

https://github.com/KartinJulia/SequenceServer

 

Sequences used were provided by Team BlastEasy :

https://github.com/raptorslab/blastEasy/tree/master/queryseq



Installing/Running Instructions

(Through Discovery Environment, open a SequenceServer app, upload a query through the input GUI on DE, run, and look at analysis.)?

 

Project Timeline/Plan


Benchmarking


Benchmarking was ran by running the sequenceserver app we created in the Discovery Environment (DE) within CyVerse. Before launching the app, the number of cores was specified in the drop-down field "Number of threads":




The running analysis was accessed which allowed us to paste the protein sequences in Sequence Server to run BLAST:





All protein sequences ran through Sequence Server were 100 residues in length and the number of cores were pre-selected before launching the app analysis. A significant decrease in run-time was observed as more cores were involved in running queries. Running queries on 8 cores clearly reduces run-time as compared to running queries on simply 1 core. 

 

Number of Protein Sequences

Time (seconds)

1 CORE

Time (seconds)

2 CORES

Time (seconds)

4 CORES

Time (seconds)

8 CORES

13.280.550.450.75
58.383.221.862.23
1013.295.233.473.24
5045.2819.4113.5910.00
10084.8634.5818.3920.41
500427.94156.7280.74119.46

 

 

Presentation

https://docs.google.com/presentation/d/1D755s013X6MVVlTszQXayMuy0A3CpYIpEdLfHAsX_ig/edit?usp=sharing

https://www.loom.com/share/db109980b7954f0e81ae7b59fd9f8196

https://www.loom.com/share/fdcc53eeb53942d29577145851f8f0f9

 

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