NDLI: Efficient and Scalable Workflows for Genomic Analyses

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Efficient and Scalable Workflows for Genomic Analyses

Content Provider	ACM Digital Library
Author	Athreya, Arjun P. Mainzer, Liudmila S. Banerjee, Subho S. Hwu, Wen-Mei Kalbarczyk, Zbigniew T. Iyer, Ravishankar K. Jongeneel, C. Victor
Abstract	Recent growth in the volume of DNA sequence data and associated computational costs of extracting meaningful information warrants the need for efficient computational systems at-scale. In this work, we propose the Illinois Genomics Execution Environment (IGen), a framework for efficient and scalable genome analyses. The design philosophy of IGen is based on algorithmic analysis and extensive measurements on compute- and data-intensive genomic analyses workflows (such as variant discovery and genotyping analysis) executed on high-performance and cloud computing infrastructures. IGen leverages the advantages of existing designs and proposes new software improvements to overcome the ine ciencies we observe in our measurements. Based on these composite improvements, we demonstrate that IGen is able to accelerate the alignment from 13.1 hours to 10.8 hours (1.2x) and the variant from 10.1 hours to 1.25 hours (8x) calling on a single node, and its modular design scales e ciently in a parallel computing environment.
Starting Page	27
Ending Page	36
Page Count	10
File Format	PDF
ISBN	9781450343527
DOI	10.1145/2912152.2912156
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-06-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Design Measurement Performance Bioinformatics
Content Type	Text
Resource Type	Article

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