scholarly journals In silico identification of bacteriocin gene clusters in the gastrointestinal tract, based on the Human Microbiome Project’s reference genome database

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Calum J. Walsh ◽  
Caitriona M. Guinane ◽  
Colin Hill ◽  
R. Paul Ross ◽  
Paul W. O’Toole ◽  
...  
Keyword(s):  
Gastrointestinal Tract ◽  
In Silico ◽  
Reference Genome ◽  
Human Microbiome ◽  
Gene Clusters ◽  
Genome Database ◽  
In Silico Identification ◽  
Reference Genome Database ◽  
Bacteriocin Gene

scholarly journals Improving the usability and comprehensiveness of microbial databases

2018 ◽  
Author(s):  
Caitlin Loeffler ◽  
Aaron Karlsberg ◽  
Lana S. Martin ◽  
Eleazar Eskin ◽  
David Koslicki ◽  
...  
Keyword(s):  
Translational Research ◽  
Basic Science ◽  
Reference Genome ◽  
Reference Database ◽  
Data Preparation ◽  
Genome Database ◽  
Reference Databases ◽  
Reference Genome Database

AbstractMetagenomics studies leverage genomic reference databases to generate discoveries in basic science and translational research. However, current microbial studies use disparate reference databases that lack consistent standards of specimen inclusion, data preparation, taxon labelling and accessibility, hindering their quality and comprehensiveness, and calling for the establishment of recommendations for reference genome database assembly. Here, we analyze existing fungal and bacterial databases and discuss guidelines for the development of a master reference database that promises to improve the quality and quantity of omics research.

In silico identification and construction of microbial gene clusters associated with biodegradation of xenobiotic compounds

2018 ◽  
Vol 114 ◽  
pp. 340-343 ◽  
Author(s):  
Garima Awasthi ◽  
Anjani Kumari ◽  
Aditya Bhushan Pant ◽  
Prachi Srivastava
Keyword(s):  
In Silico ◽  
Gene Clusters ◽  
In Silico Identification ◽  
Xenobiotic Compounds

scholarly journals Scalable metagenomic taxonomy classification using a reference genome database

2013 ◽  
Vol 29 (18) ◽  
pp. 2253-2260 ◽  
Author(s):  
Sasha K. Ames ◽  
David A. Hysom ◽  
Shea N. Gardner ◽  
G. Scott Lloyd ◽  
Maya B. Gokhale ◽  
...  
Keyword(s):  
Reference Genome ◽  
Genome Database ◽  
Reference Genome Database

scholarly journals A Profile Hidden Markov Model to investigate the distribution and frequency of LanB-encoding lantibiotic modification genes in the human oral and gut microbiome

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3254 ◽  
Author(s):  
Calum J. Walsh ◽  
Caitriona M. Guinane ◽  
Paul W. O’ Toole ◽  
Paul D. Cotter
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Human Microbiome ◽  
Gene Clusters ◽  
Human Microbiome Project ◽  
Genome Database ◽  
Profile Hidden Markov Model ◽  
Health And Disease ◽  
The Stability

Background The human microbiota plays a key role in health and disease, and bacteriocins, which are small, bacterially produced, antimicrobial peptides, are likely to have an important function in the stability and dynamics of this community. Here we examined the density and distribution of the subclass I lantibiotic modification protein, LanB, in human oral and stool microbiome datasets using a specially constructed profile Hidden Markov Model (HMM). Methods The model was validated by correctly identifying known lanB genes in the genomes of known bacteriocin producers more effectively than other methods, while being sensitive enough to differentiate between different subclasses of lantibiotic modification proteins. This approach was compared with two existing methods to screen both genomic and metagenomic datasets obtained from the Human Microbiome Project (HMP). Results Of the methods evaluated, the new profile HMM identified the greatest number of putative LanB proteins in the stool and oral metagenome data while BlastP identified the fewest. In addition, the model identified more LanB proteins than a pre-existing Pfam lanthionine dehydratase model. Searching the gastrointestinal tract subset of the HMP reference genome database with the new HMM identified seven putative subclass I lantibiotic producers, including two members of the Coprobacillus genus. Conclusions These findings establish custom profile HMMs as a potentially powerful tool in the search for novel bioactive producers with the power to benefit human health, and reinforce the repertoire of apparent bacteriocin-encoding gene clusters that may have been overlooked by culture-dependent mining efforts to date.

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