Dataset Information

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

ABSTRACT:

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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.

SUBMITTER: Essinger SD

PROVIDER: S-EPMC4301908 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTaxonomic identification through DNA barcodes gained considerable traction through the invention of next-generation sequencing and DNA metabarcoding. Metabarcoding allows for the simultaneous identification of thousands of organisms from bulk samples with high taxonomic resolution. However, reliable identifications can only be achieved with comprehensive and curated reference databases. Therefore, custom reference databases are often created to meet the needs of specific research questions. Due to taxonomic inconsistencies, formatting issues, and technical difficulties, building a custom reference database requires tremendous effort. Here, we present taxalogue, an easy-to-use software for creating comprehensive and customized reference databases that provide clean and taxonomically harmonized records. In combination with extensive geographical filtering options, taxalogue opens up new possibilities for generating and testing evolutionary hypotheses.Methodstaxalogue collects DNA sequences from several online sources and combines them into a reference database. Taxonomic incongruencies between the different data sources can be harmonized according to available taxonomies. Dereplication and various filtering options are available regarding sequence quality or metadata information. taxalogue is implemented in the open-source Ruby programming language, and the source code is available at https://github.com/nwnoll/taxalogue. We benchmark four reference databases by sequence identity against eight queries from different localities and trapping devices. Subsamples from each reference database were used to compare how well another one is covered.Resultstaxalogue produces reference databases with the best coverage at high identities for most tested queries, enabling more accurate, reliable predictions with higher certainty than the other benchmarked reference databases. Additionally, the performance of taxalogue is more consistent while providing good coverage for a variety of habitats, regions, and sampling methods. taxalogue simplifies the creation of reference databases and makes the process reproducible and transparent. Multiple available output formats for commonly used downstream applications facilitate the easy adoption of taxalogue in many different software pipelines. The resulting reference databases improve the taxonomic classification accuracy through high coverage of the query sequences at high identities.

Dataset Information

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

Unlabelled

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