NDLI: PAGE: A Framework for Easy PArallelization of GEnomic Applications

Content Provider	IEEE Xplore Digital Library
Author	Kutlu, M. Agrawal, G.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Ohio State Univ., Columbus, OH, USA (Kutlu, M.; Agrawal, G.)
Abstract	With the availability of high-throughput and low-cost sequencing technologies, an increasing amount of genetic data is becoming available to researchers. There is clearly a potential for significant new scientific and medical advances by analysis of such data, however, it is imperative to exploit parallelism and achieve effective utilization of the computing resources to be able to handle massive datasets. Thus, frameworks that can help researchers develop parallel applications without dealing with low-level details of parallel coding are very important for advances in genetic research. In this study, we develop a middleware, PAGE, which supports 'map reduce-like' processing, but with significant differences from a system like Hadoop, to be useful and effective for parallelizing analysis of genomic data. Particularly, it can work with map functions written in any language, thus allowing utilization of existing serial tools (even those for which only an executable is available) as map functions. Thus, it can greatly simplify parallel application development for scenarios where complex data formats and/or nuanced serial algorithms are involved, as is often the case for genomic data. It allows parallelization by partitioning by-locus or partitioning by-chromosome, provides different scheduling schemes, and execution models, to match the nature of algorithms common in genetic research. We have evaluated the middleware system using four popular genomic applications, including VarScan, Unified Genotyper, Realigner Target Creator, and Indel Realigner, and compared the achieved performance against with two popular frameworks (Hadoop and GATK). We show that our middleware outperforms GATK and Hadoop and it is able to achieve high parallel efficiency and scalability.
Starting Page	72
Ending Page	81
File Size	337520
Page Count	10
File Format	PDF
ISBN	9781479937998
ISSN	15302075
e-ISBN	9781479938001
DOI	10.1109/IPDPS.2014.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genomics Bioinformatics Middleware Algorithm design and analysis Partitioning algorithms Biological cells
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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