BMC BioinformaticsBioinformaticsPeer-reviewed journal article
MicrobeAnnotator: a user-friendly, comprehensive microbial genome annotation pipeline
Carlos A. Ruiz-PéreziD, Roth E. ConradiD, Konstantinos T. KonstantinidisiD
Abstract
SummaryHigh-throughput sequencing has increased the number of microbial genomes from isolates, single cells, and metagenomes available. To analyze and compare these genomes, fast and comprehensive annotation pipelines are needed. Although several approaches exist for genome annotation, these are typically not designed for easy incorporation into analysis pipelines, do not combine results from several annotation databases or offer easy-to-use summaries of metabolic reconstructions in a high-throughput mode. Here, we introduce MicrobeAnnotator, a fully automated pipeline for the comprehensive annotation of microbial genomes that combines results from several reference protein databases to reliably summarize the metabolic potential of the genomes based on KEGG modules. AvailabilityMicrobeAnnotator is implemented in Python and is freely available under an open source Artistic Licence 2.0 from https://github.com/cruizperez/MicrobeAnnotator Contactcruizperez3@gatech.edu; kostas@ce.gatech.edu
- Citations
- 143
- FWCI
- 6.27
- Field percentile
- top 2%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- mettannotator: a comprehensive and scalable Nextflow annotation pipeline for prokaryotic assemblies
Tatiana A. Gurbich, Martín Beracochea, … Robert D. Finn·BioinformaticsPeer-reviewed·Jan 22, 2025
- Combining multiple functional annotation tools increases coverage of metabolic annotation
Marc Griesemer, Jeffrey A. Kimbrel, … Patrik D’haeseleer·BMC GenomicsPeer-reviewed·Dec 1, 2018
- MetaSanity: An integrated, customizable microbial genome evaluation and annotation pipeline
Christopher J. Neely, Elaina D. Graham, Benjamin Tully·BioinformaticsPeer-reviewed·May 14, 2020
- MetaPathPredict: A machine learning-based tool for predicting metabolic modules in incomplete bacterial genomes
David Geller‐McGrath, Kishori M. Konwar, … Jason McDermott·eLifePeer-reviewed·Apr 16, 2024
- Adaptive adjustment of significance thresholds produces large gains in microbial gene annotations and metabolic insights
Kathryn Kananen, Iva Veseli, … Patrick H. Bradley·Bioinformatics AdvancesPeer-reviewed·Dec 26, 2024
- metabolisHMM: Phylogenomic analysis for exploration of microbial phylogenies and metabolic pathways
Elizabeth McDaniel, Karthik Anantharaman, Katherine D. McMahon·bioRxiv·Dec 20, 2019