Solving the Problem of Comparing Whole Bacterial Genomes across Different Sequencing Platforms

By R. Kaas, P. Leekitcharoenphon, Frank Moller Aarestrup, Ole Lund. PLoS ONE, 2014

823 citations recorded by the source corpus.

Whole genome sequencing (WGS) shows great potential for real-time monitoring and identification of infectious disease outbreaks. However, rapid and reliable comparison of data generated in multiple laboratories and using multiple technologies is essential. So far studies have focused on using one technology because each technology has a systematic bias making integration of data generated from different platforms difficult. We developed two different procedures for identifying variable sites and inferring phylogenies in WGS data across multiple platforms. The methods were evaluated on three bacterial data sets and sequenced on three different platforms (Illumina, 454, Ion Torrent). We show that the methods are able to overcome the systematic biases caused by the sequencers and infer the expected phylogenies. It is concluded that the cause of the success of these new procedures is due to a validation of all informative sites that are included in the analysis. The procedures are available as web tools.

Subfields: Clinical Biochemistry, Epidemiology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8b76c51fa1cd77dd7cefb7de46cfabe5f860e9f4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Clinical Biochemistry

1. Frank Moller Aarestrup
2. Philip Thomas Lanken Conradsen Clausen
3. Ole Lund

In Epidemiology

1. Frank Moller Aarestrup
2. Ole Lund

In Molecular Biology

1. Frank Moller Aarestrup
2. Ole Lund
3. Philip Thomas Lanken Conradsen Clausen
4. David Wayne Ussery
5. Intawat Nookaew
6. Se-Ran Jun
7. Loren J. Hauser
8. Morten Nielsen

Citations in the corpus

• Insights from 20 years of bacterial genome sequencing (2015, methodology): “Work to evaluate and refine phylogeny methods have resulted in the NDtree and CSIphylogeny methods, which both were shown to be more accurate than the original SNPtree method (Kaas et al. 2014; Leekitcharoenphon et al. 2014).”
• Rapid and precise alignment of raw reads against redundant databases with KMA (2018, methodology): “Each nucleotide is then determined by majority voting, where the strength of the called nucleotide is tested with a McNemar test, giving a robust base calling performance across different sequencing platforms [10].”