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Accepted manuscript version. The final publication is available at Springer via <a href=http://dx.doi.org/10.1007/978-3-319-24462-4_22>http://dx.doi.org/10.1007/978-3-319-24462-4_22</a>. Biological data analysis is typically implemented using a deep pipeline that combines a wide array of tools and databases. These pipelines must scale to very large datasets, and consequently require parallel and distributed computing. It is therefore important to choose a hardware platform and underlying data management and processing systems well suited for processing large datasets. There are many infrastructure systems for such data-intensive computing. However, in our experience, most biological data analysis pipelines do not leverage these systems. We give an overview of data-intensive computing infrastructure systems, and describe how we have leveraged these for: (i) scalable fault-tolerant computing for large-scale biological data; (ii) incremental updates to reduce the resource usage required to update large-scale compendium; and (iii) interactive data analysis and exploration. We provide lessons learned and describe problems we have encountered during development and deployment. We also provide a literature survey on the use of data-intensive computing systems for biological data processing. Our results show how unmodified biological data analysis tools can benefit from infrastructure systems for data-intensive computing. | Accepted manuscript version. The final publication is available at Springer via <a href=http://dx.doi.org/10.1007/978-3-319-24462-4_22>http://dx.doi.org/10.1007/978-3-319-24462-4_22</a>. Biological data analysis is typically implemented using a deep pipeline that combines a wide array of tools and databases. These pipelines must scale to very large datasets, and consequently require parallel and distributed computing. It is therefore important to choose a hardware platform and underlying data management and processing systems well suited for processing large datasets. There are many infrastructure systems for such data-intensive computing. However, in our experience, most biological data analysis pipelines do not leverage these systems. We give an overview of data-intensive computing infrastructure systems, and describe how we have leveraged these for: (i) scalable fault-tolerant computing for large-scale biological data; (ii) incremental updates to reduce the resource usage required to update large-scale compendium; and (iii) interactive data analysis and exploration. We provide lessons learned and describe problems we have encountered during development and deployment. We also provide a literature survey on the use of data-intensive computing systems for biological data processing. Our results show how unmodified biological data analysis tools can benefit from infrastructure systems for data-intensive computing. | ||
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* [https://hdl.handle.net/10037/8816 https://hdl.handle.net/10037/8816] | * [https://hdl.handle.net/10037/8816 https://hdl.handle.net/10037/8816] | ||
* [https://munin.uit.no/bitstream/10037/8816/2/article.pdf https://munin.uit.no/bitstream/10037/8816/2/article.pdf] | * [https://munin.uit.no/bitstream/10037/8816/2/article.pdf https://munin.uit.no/bitstream/10037/8816/2/article.pdf] | ||
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+ | * [http://link.springer.com/content/pdf/10.1007/978-3-319-24462-4_22 http://link.springer.com/content/pdf/10.1007/978-3-319-24462-4_22], | ||
+ | : [http://dx.doi.org/10.1007/978-3-319-24462-4_22 http://dx.doi.org/10.1007/978-3-319-24462-4_22] under the license http://www.springer.com/tdm | ||
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+ | * [https://link.springer.com/chapter/10.1007/978-3-319-24462-4_22 https://link.springer.com/chapter/10.1007/978-3-319-24462-4_22], | ||
+ | : [https://core.ac.uk/display/141562029 https://core.ac.uk/display/141562029], | ||
+ | : [https://munin.uit.no/bitstream/10037/8816/2/article.pdf https://munin.uit.no/bitstream/10037/8816/2/article.pdf], | ||
+ | : [https://munin.uit.no/handle/10037/8816 https://munin.uit.no/handle/10037/8816], | ||
+ | : [https://dblp.uni-trier.de/db/conf/cibb/cibb2014.html#BongoPE14 https://dblp.uni-trier.de/db/conf/cibb/cibb2014.html#BongoPE14], | ||
+ | : [https://rd.springer.com/chapter/10.1007/978-3-319-24462-4_22 https://rd.springer.com/chapter/10.1007/978-3-319-24462-4_22], | ||
+ | : [https://academic.microsoft.com/#/detail/2296514683 https://academic.microsoft.com/#/detail/2296514683] |
Accepted manuscript version. The final publication is available at Springer via <a href=http://dx.doi.org/10.1007/978-3-319-24462-4_22>http://dx.doi.org/10.1007/978-3-319-24462-4_22</a>. Biological data analysis is typically implemented using a deep pipeline that combines a wide array of tools and databases. These pipelines must scale to very large datasets, and consequently require parallel and distributed computing. It is therefore important to choose a hardware platform and underlying data management and processing systems well suited for processing large datasets. There are many infrastructure systems for such data-intensive computing. However, in our experience, most biological data analysis pipelines do not leverage these systems. We give an overview of data-intensive computing infrastructure systems, and describe how we have leveraged these for: (i) scalable fault-tolerant computing for large-scale biological data; (ii) incremental updates to reduce the resource usage required to update large-scale compendium; and (iii) interactive data analysis and exploration. We provide lessons learned and describe problems we have encountered during development and deployment. We also provide a literature survey on the use of data-intensive computing systems for biological data processing. Our results show how unmodified biological data analysis tools can benefit from infrastructure systems for data-intensive computing.
The different versions of the original document can be found in:
Published on 01/01/2015
Volume 2015, 2015
DOI: 10.1007/978-3-319-24462-4_22
Licence: CC BY-NC-SA license
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