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Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
Published on: August 20, 2021
Pathoscope: species identification and strain attribution with unassembled sequencing data.
Owen E Francis1, Matthew Bendall, Solaiappan Manimaran
1Department of Statistics, Brigham Young University, Provo, Utah 84602, USA;
Genome Research
|July 12, 2013
Summary
Pathoscope is a new Bayesian statistical framework for analyzing next-generation sequencing data. It enables rapid species identification and strain attribution from complex samples, even for closely related strains.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Next-generation sequencing (NGS) generates vast genomic data for bioforensics, biosurveillance, and clinical applications.
- Existing methods struggle with computational efficiency for large NGS datasets.
- Novel methodologies are required to analyze NGS data effectively and rapidly.
Purpose of the Study:
- To present Pathoscope, a novel statistical framework for analyzing raw NGS reads.
- To enable rapid species identification and strain attribution from purified or mixed samples.
- To accommodate complex biological samples and identify unknown or closely related strains.
Main Methods:
- Utilizes a Bayesian statistical framework to analyze NGS reads.
- Incorporates sequence and mapping quality information.
- Calculates posterior probabilities for matches against a reference genome database.
Main Results:
- Pathoscope accurately identifies species and attributes strains from complex samples.
- The method handles mixed species and samples with unknown species/strains.
- Achieves high accuracy in discriminating closely related strains with minimal genomic coverage.
Conclusions:
- Pathoscope offers a computationally efficient and accurate solution for analyzing NGS data.
- The framework is suitable for bioforensics, biosurveillance, and clinical applications.
- Enables rapid and reliable identification and attribution of biological agents.
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