An Automated Infrastructure to Support High-throughput Bioinformatics
Proc. IEEE International Conference on High Performance Computing & Simulation (HPCS 2014), page 600-607 - july 2014
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The number of domains affected by the big data phenomenon is
constantly increasing, both in science and industry, with
high-throughput DNA sequencers being among the most massive data
producers.
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Building analysis frameworks that can keep up with such a high
production rate, however, is only part of the problem: current
challenges include dealing with articulated data repositories where
objects are connected by multiple relationships, managing complex
processing pipelines where each step depends on a large number of
configuration parameters and ensuring reproducibility, error control
and usability by non-technical staff.
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Here we describe an automated infrastructure built to address the above
issues in the context of the analysis of the data produced by the CRS4
next-generation sequencing facility. The system integrates open
source tools, either written by us or publicly available, into a
framework that can handle the whole data transformation process, from
raw sequencer output to primary analysis results.
BibTex references
@InProceedings{CLLMPPUAFZ14,
author = {Cuccuru, G. and Leo, S. and Lianas, L. and Muggiri, M. and Pinna, A. and Pireddu, L. and Uva, P. and Angius, A. and Fotia, G. and Zanetti, G.},
title = {An Automated Infrastructure to Support High-throughput Bioinformatics},
booktitle = {Proc. IEEE International Conference on High Performance Computing \& Simulation (HPCS 2014)},
pages = {600-607},
month = {july},
year = {2014},
editor = {Smari, Waleed W. and Zeljkovic, Vesna Eds.},
publisher = {IEEE},
note = {IEEE Catalog Number: CFP1478H-CDR},
keywords = {Genomics,Muscles,Simple object access protocol,Bioinformatics,MapReduce,NGS},
doi = {10.1109/HPCSim.2014.6903742},
isbn = {978-1-4799-5313-4},
url = {https://publications.crs4.it/pubdocs/2014/CLLMPPUAFZ14},
}
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