Efficient detection of viral transmissions with Next-Generation Sequencing data

Abstract

BackgroundHepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Molecular analysis has been frequently used in the study of HCV outbreaks and transmission chains; helping identify a cluster of sequences as linked by transmission if their genetic distances are below a previously defined threshold. However, HCV exists as a population of numerous variants in each infected individual and it has been observed that minority variants in the source are often the ones responsible for transmission, a situation that precludes the use of a single sequence per individual because many such transmissions would be missed.The use of Next-Generation Sequencing immensely increases the sensitivity of transmission detection but brings a considerable computational challenge because all sequences need to be compared among all pairs of samples.MethodsWe developed a three-step strategy that filters pairs of samples according to different criteria: (i) a k-mer bloom filter, (ii) a Levenhstein filter and (iii) a filter of identical sequences. We applied these three filters on a set of samples that cover the spectrum of genetic relationships among HCV cases, from being part of the same transmission cluster, to belonging to different subtypes.ResultsOur three-step filtering strategy rapidly removes 85.1% of all the pairwise sample comparisons and 91.0% of all pairwise sequence comparisons, accurately establishing which pairs of HCV samples are below the relatedness threshold.ConclusionsWe present a fast and efficient three-step filtering strategy that removes most sequence comparisons and accurately establishes transmission links of any threshold-based method. This highly efficient workflow will allow a faster response and molecular detection capacity, improving the rate of detection of viral transmissions with molecular data.

Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions

“…GHOST (Campo et al, 2015; Longmire et al, 2017; Rytsareva et al, 2017) was used to genetically characterize HCV strains and detect a transmission network. GHOST generates networks where 2 nodes representing infected individuals are linked by transmission if the minimal Hamming distance between any pair of HCV HVR1 sequences obtained from these individuals is below a relatedness threshold of 3.77% (Campo et al, 2015).…”

“…Here, we take advantage of a complex HCV transmission network, which was recently identified among PWID in Indiana (Conrad et al, 2015; Peters et al, 2016; Ramachandran et al, 2016) using the Global Hepatitis Outbreak and Surveillance Technology (GHOST) (Campo et al, 2015; Longmire et al, 2017; Rytsareva et al, 2017), to devise and evaluate a new targeted, network-based strategy for reducing circulation and dissemination of HCV infection in 2 epidemiological settings: (1) long-established HCV infection as was observed in a PWID community during investigation in Indiana (Peters et al, 2016) and (2) a hypothetical rapid spread of a single HCV strain as observed during an outbreak.…”

Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants

“…We used a set of 413 samples from [2] with 501.5 haplotypes per sample in average produced by NGS; 8 datatsets d 1 , ..., d 8 with 1000, 2000, . .…”

“…Consider two sets T 1 and T 2 each containing N DNA or RNA sequences of length L. The similarity join problem consists in locating the set P of all pairs of sequences, with one sequence from T 1 and the other from T 2 , within an edit distance or Hamming distance defined by the specified threshold t. In molecular epidemiology, this computational problem needs to be solved for detection of viral transmissions from sequences of intra-host viral variants sampled from infected individuals [1,2]. Viral populations, for which the minimal inter-sample distance does not exceed the threshold, are considered to be potentially linked by transmission [1], while the number of pairs in P may suggest the time since a transmission event [3].…”

“…• Sample pair filtering: given two NGS sequence samples T 1 and T 2 , quickly determine whether the distances between all inter-sample pairs of sequences are greater than the threshold t. • Inter-sample sequence retrieval (similarity join): given two NGS sequence samples T 1 and T 2 , find all inter-sample pairs of sequences at edit distance or hamming distance below the threshold t. • Intra-sample sequence retrieval (or genetic network construction): given an NGS sequence sample T 1 , find all pairs of sequences at edit distance or hamming distance below the threshold t. The tool was validated using Hepatitis C Virus (HCV) data in the settings used for detection of viral transmissions and outbreaks [1,2].…”