Open Access. Powered by Scholars. Published by Universities.®
Research Methods in Life Sciences Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Algorithms (1)
- Bacillus anthracis (1)
- Bacteria (1)
- Bacteriophages (1)
- Base Sequence (1)
-
- Bayes Theorem (1)
- Bioterrorism (1)
- Burkholderia mallei (1)
- Burkholderia pseudomallei (1)
- Clostridium botulinum (1)
- Cluster Analysis (1)
- Computational Biology (1)
- Conserved Sequence (1)
- Databases, Genetic (1)
- Escherichia coli (1)
- Escherichia coli Infections (1)
- Europe (1)
- Francisella tularensis (1)
- Genes, Viral (1)
- Genome, Bacterial (1)
- High-Throughput Nucleotide Sequencing (1)
- Humans (1)
- Likelihood Functions (1)
- Molecular Sequence Data (1)
- Polymerase Chain Reaction (1)
- Sequence Analysis, DNA (1)
- Software (1)
- Species Specificity (1)
- Viral Proteins (1)
Articles 1 - 2 of 2
Full-Text Articles in Research Methods in Life Sciences
Pathoscope: Species Identification And Strain Attribution With Unassembled Sequencing Data., Owen E Francis, Matthew Bendall, Solaiappan Manimaran, Changjin Hong, Nathan L Clement, Eduardo Castro-Nallar, Quinn Snell, G Bruce Schaalje, Mark J Clement, Keith A Crandall, W Evan Johnson
Pathoscope: Species Identification And Strain Attribution With Unassembled Sequencing Data., Owen E Francis, Matthew Bendall, Solaiappan Manimaran, Changjin Hong, Nathan L Clement, Eduardo Castro-Nallar, Quinn Snell, G Bruce Schaalje, Mark J Clement, Keith A Crandall, W Evan Johnson
Computational Biology Institute
Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence …
Phage Cluster Relationships Identified Through Single Gene Analysis., Kyle C Smith, Eduardo Castro-Nallar, Joshua Nb Fisher, Donald P Breakwell, Julianne H Grose, Sandra H Burnett
Phage Cluster Relationships Identified Through Single Gene Analysis., Kyle C Smith, Eduardo Castro-Nallar, Joshua Nb Fisher, Donald P Breakwell, Julianne H Grose, Sandra H Burnett
Computational Biology Institute
BACKGROUND: Phylogenetic comparison of bacteriophages requires whole genome approaches such as dotplot analysis, genome pairwise maps, and gene content analysis. Currently mycobacteriophages, a highly studied phage group, are categorized into related clusters based on the comparative analysis of whole genome sequences. With the recent explosion of phage isolation, a simple method for phage cluster prediction would facilitate analysis of crude or complex samples without whole genome isolation and sequencing. The hypothesis of this study was that mycobacteriophage-cluster prediction is possible using comparison of a single, ubiquitous, semi-conserved gene. Tape Measure Protein (TMP) was selected to test the hypothesis because it …