BioinformaticsBioinformaticsPeer-reviewed journal article
MetaSanity: An integrated, customizable microbial genome evaluation and annotation pipeline
Christopher J. NeelyiD, Elaina D. GrahamiD, Benjamin TullyiD
Abstract
SummaryAs the importance of microbiome research continues to become more prevalent and essential to understanding a wide variety of ecosystems (e.g., marine, built, host-associated, etc.), there is a need for researchers to be able to perform highly reproducible and quality analysis of microbial genomes. MetaSanity incorporates analyses from eleven existing and widely used genome evaluation and annotation suites into a single, distributable workflow, thereby decreasing the workload of microbiologists by allowing for a flexible, expansive data analysis pipeline. MetaSanity has been designed to provide separate, reproducible workflows, that (1) can determine the overall quality of a microbial genome, while providing a putative phylogenetic assignment, and (2) can assign structural and functional gene annotations with varying degrees of specificity to suit the needs of the researcher. The software suite combines the results from several tools to provide broad insights into overall metabolic function and putative extracellular localization of peptidases and carbohydrate-active enzymes. Importantly, this software provides built-in optimization for \"big data\" analysis by storing all relevant outputs in an SQL database, allowing users to query all the results for the elements that will most impact their research.\n\nAvailability and implementationMetaSanity is provided under the GNU General Public License v.3.0 and is available for download at https://github.com/cjneely10/MetaSanity. This application is distributed as a Docker image. MetaSanity is implemented in Python3/Cython and C++.\n\nSupplementary informationSupplementary data are available below.
- assay: whole-genome sequencing
- research focus: database / resource
- research focus: software / tool
- research focus: benchmarking
- approach: statistical model
- Citations
- 29
- FWCI
- 1.86
- Field percentile
- top 14%
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