A tutorial using ARB_Toolkit is available: http://www.ece.drexel.edu/gailr/EESI/tutorial.php

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 supplementary material.

example usage:

	getAccession.py -i MFS_metaData.txt -o ListAccessions.txt
	addUIDtoFasta.py -i ListAcessions.txt -a MFS_Align.fasta -o MFS_UID.fasta
	rename_tree_leaves.py -a MFS_Align.fasta -i MFS_UID.fasta -l TreeLables_orig.txt -o treelabels_mapped.txt

	build_ift_from_metalabels.py -i ../arb/MFS_Field_labels.txt -o mfs-importer.ift

first generate your ift from a file called Field labels. this is a file that contains the label of each field in your database each on it's own line. If you look in examples there is an example of this.

	build_ift_from_metalabels.py -i ../arb/MFS_Field_labels.txt -o mfs-importer.ift

now you need to copy your import filter to the $ARBHOME/lib/import folder

Steve Essinger Erin Reichenberger Chris Blackwood Gail Rosen Calvin Morrison