Identifying bacterial genes and endosymbiont DNA with Glimmer
By Arthur L. Delcher, Kirsten A. Bratke, E. Powers, Steven L. Salzberg. Bioinform., 2007
3,075 citations recorded by the source corpus.
MOTIVATION The Glimmer gene-finding software has been successfully used for finding genes in bacteria, archaea and viruses representing hundreds of species. We describe several major changes to the Glimmer system, including improved methods for identifying both coding regions and start codons. We also describe a new module of Glimmer that can distinguish host and endosymbiont DNA. This module was developed in response to the discovery that eukaryotic genome sequencing projects sometimes inadvertently capture the DNA of intracellular bacteria living in the host. RESULTS The new methods dramatically reduce the rate of false-positive predictions, while maintaining Glimmer's 99% sensitivity rate at detecting genes in most species, and they find substantially more correct start sites, as measured by comparisons to known and well-curated genes. We show that our interpolated Markov model (IMM) DNA discriminator correctly separated 99% of the sequences in a recent genome project that produced a mixture of sequences from the bacterium Prochloron didemni and its sea squirt host, Lissoclinum patella. AVAILABILITY Glimmer is OSI Certified Open Source and available at http://cbcb.umd.edu/software/glimmer.
Subfields: Ecology, Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/c6c7e0e18f6e9cee4d0443ae928e7c8a31e67705
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Citations in the corpus
- Performance comparison of benchtop high-throughput sequencing platforms (2012, methodology): “using the automated xBASE annotation pipeline [16] which utilises Glimmer for coding sequence prediction [17] and tRNAScan-SE and RNAmmer for stable RNA prediction [18, 19].”
- Clustering metagenomic sequences with interpolated Markov models (2010, background): “By demonstrating the ability of IMMs to successfully cluster sequences, we add to the growing evidence of the effectiveness of IMMs for modeling DNA sequences [38,53].”
- Genome Majority Vote Improves Gene Predictions (2011, neutral): “Comparison of inconsistencies for Prodigal vs. GenBank or Glimmer3 start sites.”
- Identification of protein coding regions in RNA transcripts (2014, methodology): “Self-training has already been used in algorithms for ab initio gene finding in prokaryotic genomes, particularly in the frequently used GeneMarkS (7), Prodigal (8,9) and Glimmer3 (8,9).”