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ve-SEQ: An improved approach to high throughput, whole genome viral sequencing

Bonsall D, Ansari MA, et al. ve-SEQ: Robust, unbiased enrichment for streamlined detection and whole-genome sequencing of HCV and other highly diverse pathogens. F1000 Research 4:1062.

Citation summary: Read how scientists at the University of Oxford use xGen Lockdown Probes in ve-SEQ, a novel method for detecting and sequencing HCV genotypes.


Hepatitis C virus (HCV) is prevalent in 2.8% of the global population and is currently incurable, largely due to its extensive genetic diversity. While antiviral drug combination therapies have improved treatment outcomes, they are ineffective against certain HCV genotypes and are also extremely expensive. Recent clinical trials assessing resistant-associated variants (RAVs) suggest treatment efficacy largely depends on the genetic background of the HCV virus. Therefore, efficient methods for whole genome viral sequencing are in high demand. Such methods could offer insights into the relevance of low frequency variants, the impact of mutation combinations on viral fitness, and the drug susceptibilities of specific viral genotypes.

In this paper, the authors introduce a novel approach for high throughput, whole genome HCV sequencing. They demonstrate how their method, ve-SEQ, improves on conventional viral sequencing methods such as PCR amplification and RNA-seq based metagenomics.


Bonsall et al. began by evaluating an RNA-seq, metagenomic approach to HCV sequencing. They constructed indexed sequencing libraries from plasma RNA of HCV-infected patients and mapped the relationship between viral load (VL) and the number of HCV reads through sequencing on the Illumina HiSeq 2500.

The researchers then performed their ve-SEQ method, comparing its sequencing efficiency to the conventional RNA-seq method. In their ve-SEQ approach, they enriched the previously-sequenced pool of libraries with an optimized capture panel made from IDT xGen Lockdown Probes. The panel contained 4 sets of 155–157 biotinylated probes, each 120 nt, and provided capture of 4 HCV genotypes.

Initially, they used this capture panel to investigate how varying probe-target sequence identities can affect ve-SEQ success. The information gathered from these investigations prompted the researchers to develop an algorithm for probe design that allowed them to build a comprehensive HCV panel for use in a clinical setting.


The researchers found that the RNA-seq based, metagenomic approach generated 1% total HCV sequence yield from samples with high viral load (VL). They note how this approach, while unbiased, is costly and insensitive to samples with low to mid-range VL. With their ve-SEQ method, however, the group was able to generate linear detection of VLs down to 1000 IU/mL—more than 10X lower than by regular RNA-seq. In addition, ve-SEQ (with xGen probes) led to a 224X increase in total HCV reads, including 1000X enrichment for samples with low to mid-range VL, and nearly 100% HCV sequence content for high VL samples.

Through investigation of probe-target sequence identity effects on ve-SEQ success, they found that near-maximal enrichment is achievable if the sequence of a sample segment and its closest matching probe is ≥80% identical. This finding prompted the researchers to implement an algorithm for optimizing probe design, which resulted in the assembly of a comprehensive HCV panel representing 6 of the 7 currently recognized HCV genotypes.

The ve-SEQ based, optimized panel ultimately offered robust, unbiased detection of resistance-associated variants (RAVs) in a clinical setting. The researchers successfully monitored viral re-emergence in patients undergoing antiviral drug treatments, and were able to identify known drug resistant alleles within these new populations. Overall, the group’s findings support ve-SEQ as a cost-effective improvement over other high throughput approaches to whole genome viral sequencing, and suggest similar methods could be applied to other pathogens.

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The authors of this paper obtained technical advice from IDT’s NGS support group. The expertise they received prompted them to acknowledge Nick Downey, one of our Scientific Application Specialists, as a contributor to their research.

If you have questions regarding the design of your NGS research, contact our support team at

Published Jan 14, 2016
Revised/updated Aug 24, 2016