Early detection and improved genomic surveillance of SARS-CoV-2 variants from deep sequencing data

In the definition of fruitful strategies to contrast the worldwide diffusion of SARS-CoV-2, maximum efforts must be devoted to the early detection of dangerous variants. An effective help to this end is granted by the analysis of deep sequencing data of viral samples, which are typically discarded after the creation of consensus sequences. Indeed, only with deep sequencing data it is possible to identify intra-host low-frequency mutations, which are a direct footprint of mutational processes that may eventually lead to the origination of functionally advantageous variants. Accordingly, a timely and statistically robust identification of such mutations might inform political decision-making with significant anticipation with respect to standard analyses based on consensus sequences. To support our claim, we here present the largest study to date of SARS-CoV-2 deep sequencing data, which involves 220,788 high quality samples, collected over 20 months from 137 distinct studies. Importantly, we show that a relevant number of spike and nucleocapsid mutations of interest associated to the most circulating variants, including Beta, Delta and Omicron, might have been intercepted several months in advance, possibly leading to different public-health decisions. In addition, we show that a refined genomic surveillance system involving high- and low-frequency mutations might allow one to pinpoint possibly dangerous emerging mutation patterns, providing a data-driven automated support to epidemiologists and virologists.

Download Full-text

Identification of single nucleotide variants using position-specific error estimation in deep sequencing data

10.1101/475947 ◽

2018 ◽

Author(s):

Dimitrios Kleftogiannis ◽

Marco Punta ◽

Anuradha Jayaram ◽

Shahneen Sandhu ◽

Stephen Q. Wong ◽

...

Keyword(s):

Deep Sequencing ◽

Low Frequency ◽

Poisson Model ◽

Real Data ◽

Analytical Sensitivity ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Deep Sequencing Data ◽

Targeted Deep Sequencing

AbstractBackgroundTargeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs).MethodsTo address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection.ResultsOur tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments.ConclusionsAmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.

Download Full-text

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

BMC Genomics ◽

10.1186/s12864-015-1456-x ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 34

Author(s):

Richard J Orton ◽

Caroline F Wright ◽

Marco J Morelli ◽

David J King ◽

David J Paton ◽

...

Keyword(s):

Deep Sequencing ◽

Low Frequency ◽

Sequencing Data ◽

Rt Pcr ◽

Deep Sequencing Data ◽

Sequence Errors

Download Full-text

Reanalysis of deep-sequencing data from Austria points towards a small SARS-COV-2 transmission bottleneck on the order of one to three virions

10.1101/2021.02.22.432096 ◽

2021 ◽

Author(s):

Michael A. Martin ◽

Katia Koelle

Keyword(s):

Genetic Variation ◽

Deep Sequencing ◽

De Novo ◽

Low Frequency ◽

Variant Calling ◽

Population Level ◽

Sequencing Data ◽

Deep Sequencing Data ◽

Computational Analyses ◽

Transmission Bottleneck

An early analysis of SARS-CoV-2 deep-sequencing data that combined epidemiological and genetic data to characterize the transmission dynamics of the virus in and beyond Austria concluded that the size of the virus’s transmission bottleneck was large – on the order of 1000 virions. We performed new computational analyses using these deep-sequenced samples from Austria. Our analyses included characterization of transmission bottleneck sizes across a range of variant calling thresholds and examination of patterns of shared low-frequency variants between transmission pairs in cases where de novo genetic variation was present in the recipient. From these analyses, among others, we found that SARS-CoV-2 transmission bottlenecks are instead likely to be very tight, on the order of 1-3 virions. These findings have important consequences for understanding how SARS-CoV-2 evolves between hosts and the processes shaping genetic variation observed at the population level.

Download Full-text

Increased yields of duplex sequencing data by a series of quality control tools

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab002 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Gundula Povysil ◽

Monika Heinzl ◽

Renato Salazar ◽

Nicholas Stoler ◽

Anton Nekrutenko ◽

...

Keyword(s):

Low Frequency ◽

Variant Calling ◽

Data Loss ◽

Sequencing Data ◽

Bioinformatics Pipeline ◽

Consensus Sequences ◽

Sequencing Errors ◽

Data Output ◽

Reverse Strand ◽

Duplex Sequencing

Abstract Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

Download Full-text