evaluating distributed platforms for protein-guided scientific workflow

Natasha Pavlovikj, Kevin Begcy, Sairam Behera,

Malachy Campbell, Harkamal Walia, Jitender S.Deogun

University of Nebraska-Lincoln

Evaluating Distributed Platforms for Protein-Guided

Scientific Workflow

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XSEDE '14, July 13 - 18 2014, Atlanta, GA, USA

Introduction

Gene expression and transcriptome analysis are one of the main focuses of research for a great number of biologists and scientists

The analysis of this so called “big data” is done by using a complex set of multitude of software tools

Enhanced demand of powerful computational resources where the data can be stored and analyzed

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Assembly Pipeline

Assembly of raw sequence data is a complex multi-stage process composed of preprocessing, assembling, and post-processing

Assembly pipeline is used to simplify the entire assembly process by automating steps of the pipeline

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blast2cap3

Multiple approaches used for assembling the filtered reads produce high redundancy of the resulting transcripts

Overlap-based assembly program CAP3 is used to merge transcripts based on the overlapping region with specified identity

However, because most of the produced transcripts code for a protein, a protein similarity should be also considered during the merging

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blast2cap3

Blast2cap3 is a protein-guided assembly approach that first clusters the transcripts based on similarity to a common protein and then passes each cluster to CAP3

Blast2cap3 is a Python script written by Vince Buffalo from Plant Sciences Department, UCD

The recent use of blast2cap3 on the wheat transcriptome assembly shows that blast2cap3 generates fewer artificially fused sequences and reduces the total number of transcripts by 8-9%

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blast2cap3

The assembled transcripts are aligned with protein datasets closely related to the organism for which the transcripts are generated, and afterwards, transcripts sharing a common protein hit are merged using CAP3

The current implementation of blast2cap3 supports only serial execution

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Pegasus Workflow Management System

The modularity of blast2cap3 allows us to decompose the existing approach on multiple tasks, some of which can be run in parallel

The protein-guided assembly can be structured into a scientific workflow

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Pegasus Workflow Management System

Pegasus WMS is a framework that automatically maps high-level scientific workflows organized as directed acyclic graph (DAG) onto wide range of execution platforms, including clusters, grids, and clouds

Pegasus uses DAX (directed acyclic graph in XML) files to specify an abstract workflow

The abstract workflow contains information and description of all executable files and logical names of the input files used by the workflow

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blast2cap3 with Pegasus WMS

Each node represents a workflow task, while each edge represents the dependency between the tasks

Archive of all required built libraries and tools (Python, Biopython, CAP3)

The step of downloading and extracting this archive is defined as a task in the workflow

Pegasus WMS implementation of blast2cap3 reduces the running time of the current serial implementation of blast2cap3 for more than 95%

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Execution Platforms

The resources that scientific workflows require can exceed the capabilities of the local computational resources

Scientific workflows are usually executed on distributed platforms, such as campus clusters, grids or clouds

Used execution platforms

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Sandhills: University of Nebraska Campus Cluster

Sandhills is one of the High Performance Computing (HPC) Clusters at the University of Nebraska – Lincoln Holland Computing Center (HCC)

Used by faculty and students Sandhills was constructed in 2011 and it has

1440 AMD cores housed in a total of 44 nodes Every new user account of HCC is required to be

associated with a faculty or research group

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OSG: Open Science Grid

OSG is a national consortium of geographically distributed academic institutions and laboratories that provide hundreds computing and storage resources to the OSG users

OSG is organized into Virtual Organizations OSG does not own any computing or storage

resources, but allows users to use the resources contributed by the other members of the OSG and VO’s

Every new user applies for an OSG certificate13

Amazon EC2: Amazon Elastic Compute Cloud

Amazon Elastic Compute Cloud (Amazon EC2) is a large commercial Web-based service provided by Amazon.com

Users have access to virtual machine (VM) instances where they deploy VM images with customized software and libraries

Amazon EC2 is a scalable, elastic and flexible platform

Amazon EC2 users are hourly billed for the number and the type of resources they are using

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Experiments

Investigate the behavior of the modified Pegasus WMS implementation of blast2cap3 when the workflow is composed of 30, 110, 210, 610, 1,010, and 2,010 tasks respectively

Run the workflow multiple times on the different execution platforms in order to detect the different workflow performance as well as the different resource availability over time

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Experiments

Compare the total workflow running time between different execution platforms

Examine the number of running versus the number of idle jobs over time for each workflow

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Experimental Data

Diploid wheat Triticum urartu dataset from NCBI The assembled transcripts were generated using

Velvet as a de novo assembler These transcripts were aligned with closely

related wheat organisms (Barley, Brachypodium, Rice, Maize, Sorghum, Arabidopsis)

“transcripts.fasta”, 404 MB big, 236,529 assembled transcripts

“alignments.out”, 155 MB big, 1,717,454 protein hits

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Comparing Running Time on Sandhills, OSG and Amazon EC2 for Workflows with Different Number of Tasks

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Comparing the Number of Running Jobsversus the Number of Idle Jobs Over Timefor Workflows with Different Task Number

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Cost Comparison of Different Execution Platforms

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The main and the most important difference between the commercial cloud and the academic distributed resources is the cost

Sandhills: generally free resources

OSG: completely free resources

Amazon EC2: complex pricing model 50 m1.large spot instance X $0.04 per hour = $122.84

Conclusion

Using more than 100 tasks in a workflow significantly reduces the running time for all execution platforms

The resource allocation on Sandhills and OSG is opportunistic, and its availability changes over time

The results are almost constant when Amazon EC2 is used

Workflow failures were not encountered on Sandhills and Amazon EC2

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Conclusion

The predictability of the Amazon EC2 resources leads to better workflow running time when the cloud is used as a platform

For our blast2cap3 workflow, better running time and better usage of the allocated resources were achieved when Amazon EC2 is used

Due to the Amazon EC2 cost, the academic distributed systems can be a good alternative

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Acknowledgments

University of Nebraska Holland Computing Center

Open Science Grid

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