Sentence examples for microbial genomes based from inspiring English sources

A previous study suggested that long single-molecule reads are sufficient to assemble most known microbial genomes based on a bioinformatics analysis of 2,267 complete genomes for bacteria and archaea and sequencing results for six bacteria [ 14].

OperomeDB represents one of the first attempts to provide a comprehensive resource for operon structures in microbial genomes based on RNA-sequencing data, providing a one stop portal for understanding the genome organization in the context of transcriptional regulation in a condition-specific manner.

The most common gene prediction system for microbial systems is GLIMMER (Gene Locator and Interpolated Markov ModelER), which identifies the coding region on the microbial genome based on interpolated Markov models [ 83, 84].

From both Bear Paw and Octopus hot springs, each assembled contig had more similarity to other metagenome contigs than to any sequenced microbial genome based on GSPC analysis, suggesting a genome signature common to each of these extreme environments.

Chang found two new enzyme versions in published sequences of microbial genomes, and based on her understanding of the enzyme pathway, substituted the new versions at critical points that would not interfere with the hundreds of other chemical reactions going on in a living E. coli cell.

CARD assigns putative antibiotic resistance genes to unannotated microbial genome sequences based on sequence similarity.

Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants.

DNA sequences from microbial genomes can be clustered based on compositional characteristics such as oligonucleotide usage patterns.

Microbial genomes are millions of base pairs in length, requiring both a global view of the genome and the ability to zoom into detail interactively, enabled by the OptIPortal.

In this work, we developed a method for identifying the core structure among moderately related microbial genomes by constructing a genome alignment based on the consensus gene order.

We describe a new in silico method for the prediction of bacterial pathogenicity, based on the identification in microbial genomes of features that appear to correlate with virulence.