A toolkit for ARB to integrate custom databases and externally built phylogenies.

UNLABELLED:Researchers are perpetually amassing biological sequence data. The computational approaches employed by ecologists for organizing this data (e.g. alignment, phylogeny, etc.) typically scale nonlinearly in execution time with the size of the dataset. This often serves as a bottleneck for p...

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Main Authors: Steven D Essinger, Erin Reichenberger, Calvin Morrison, Christopher B Blackwood, Gail L Rosen
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4301908?pdf=render
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spelling doaj-c18c0ed4ef6e4e2c8ef97267011bcf5a2020-11-25T01:33:17ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01101e010927710.1371/journal.pone.0109277A toolkit for ARB to integrate custom databases and externally built phylogenies.Steven D EssingerErin ReichenbergerCalvin MorrisonChristopher B BlackwoodGail L RosenUNLABELLED:Researchers are perpetually amassing biological sequence data. The computational approaches employed by ecologists for organizing this data (e.g. alignment, phylogeny, etc.) typically scale nonlinearly in execution time with the size of the dataset. This often serves as a bottleneck for processing experimental data since many molecular studies are characterized by massive datasets. To keep up with experimental data demands, ecologists are forced to choose between continually upgrading expensive in-house computer hardware or outsourcing the most demanding computations to the cloud. Outsourcing is attractive since it is the least expensive option, but does not necessarily allow direct user interaction with the data for exploratory analysis. Desktop analytical tools such as ARB are indispensable for this purpose, but they do not necessarily offer a convenient solution for the coordination and integration of datasets between local and outsourced destinations. Therefore, researchers are currently left with an undesirable tradeoff between computational throughput and analytical capability. To mitigate this tradeoff we introduce a software package to leverage the utility of the interactive exploratory tools offered by ARB with the computational throughput of cloud-based resources. Our pipeline serves as middleware between the desktop and the cloud allowing researchers to form local custom databases containing sequences and metadata from multiple resources and a method for linking data outsourced for computation back to the local database. A tutorial implementation of the toolkit is provided in the supporting information, S1 Tutorial. AVAILABILITY:http://www.ece.drexel.edu/gailr/EESI/tutorial.php.http://europepmc.org/articles/PMC4301908?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Steven D Essinger
Erin Reichenberger
Calvin Morrison
Christopher B Blackwood
Gail L Rosen
spellingShingle Steven D Essinger
Erin Reichenberger
Calvin Morrison
Christopher B Blackwood
Gail L Rosen
A toolkit for ARB to integrate custom databases and externally built phylogenies.
PLoS ONE
author_facet Steven D Essinger
Erin Reichenberger
Calvin Morrison
Christopher B Blackwood
Gail L Rosen
author_sort Steven D Essinger
title A toolkit for ARB to integrate custom databases and externally built phylogenies.
title_short A toolkit for ARB to integrate custom databases and externally built phylogenies.
title_full A toolkit for ARB to integrate custom databases and externally built phylogenies.
title_fullStr A toolkit for ARB to integrate custom databases and externally built phylogenies.
title_full_unstemmed A toolkit for ARB to integrate custom databases and externally built phylogenies.
title_sort toolkit for arb to integrate custom databases and externally built phylogenies.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2015-01-01
description UNLABELLED:Researchers are perpetually amassing biological sequence data. The computational approaches employed by ecologists for organizing this data (e.g. alignment, phylogeny, etc.) typically scale nonlinearly in execution time with the size of the dataset. This often serves as a bottleneck for processing experimental data since many molecular studies are characterized by massive datasets. To keep up with experimental data demands, ecologists are forced to choose between continually upgrading expensive in-house computer hardware or outsourcing the most demanding computations to the cloud. Outsourcing is attractive since it is the least expensive option, but does not necessarily allow direct user interaction with the data for exploratory analysis. Desktop analytical tools such as ARB are indispensable for this purpose, but they do not necessarily offer a convenient solution for the coordination and integration of datasets between local and outsourced destinations. Therefore, researchers are currently left with an undesirable tradeoff between computational throughput and analytical capability. To mitigate this tradeoff we introduce a software package to leverage the utility of the interactive exploratory tools offered by ARB with the computational throughput of cloud-based resources. Our pipeline serves as middleware between the desktop and the cloud allowing researchers to form local custom databases containing sequences and metadata from multiple resources and a method for linking data outsourced for computation back to the local database. A tutorial implementation of the toolkit is provided in the supporting information, S1 Tutorial. AVAILABILITY:http://www.ece.drexel.edu/gailr/EESI/tutorial.php.
url http://europepmc.org/articles/PMC4301908?pdf=render
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