Abstract

The sequential paradigm of data acquisition and analysis in next-generation sequencing leads to high turnaround times for the generation of interpretable results. We combined a novel real-time read mapping algorithm with fast variant calling to obtain reliable variant calls still during the sequencing process. Thereby, our new algorithm allows for accurate read mapping results for intermediate cycles and supports large reference genomes such as the complete human reference. This enables the combination of real-time read mapping results with complex follow-up analysis. In this study, we showed the accuracy and scalability of our approach by applying real-time read mapping and variant calling to seven publicly available human whole exome sequencing datasets. Thereby, up to 89% of all detected SNPs were already identified after 40 sequencing cycles while showing similar precision as at the end of sequencing. Final results showed similar accuracy to those of conventional post-hoc analysis methods. When compared to standard routines, our live approach enables considerably faster interventions in clinical applications and infectious disease outbreaks. Besides variant calling, our approach can be adapted for a plethora of other mapping-based analyses.

Details

Title
Reliable variant calling during runtime of Illumina sequencing
Author
Loka, Tobias P 1   VIAFID ORCID Logo  ; Tausch, Simon H 2   VIAFID ORCID Logo  ; Renard, Bernhard Y 1   VIAFID ORCID Logo 

 Bioinformatics Division (MF 1), Department for Methodology and Research Infrastructure, Robert Koch Institute, Berlin, Germany 
 Bioinformatics Division (MF 1), Department for Methodology and Research Infrastructure, Robert Koch Institute, Berlin, Germany; Centre for Biological Threats and Special Pathogens: Highly Pathogenic Viruses (ZBS 1), Robert Koch Institute, Berlin, Germany; German Federal Institute for Risk Assessment (BfR), Department of Biological Safety, Berlin, Germany 
Pages
1-8
Publication year
2019
Publication date
Nov 2019
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2313768251
Copyright
© 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.