ArticleBMC genomics2017
Efficient detection of viral transmissions with Next-Generation Sequencing data.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Next-generation sequencing studies on the E1-HVR1 region of hepatitis C virus (HCV) from non-high-risk HCV patients living in Punjab and Khyber Pakhtunkhwa, Pakistan.Archives of virology · 2021Article
- Epidemiological data analysis of viral quasispecies in the next-generation sequencing era.Briefings in bioinformatics · 2021Review
- Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences.BMC bioinformatics · 2020Article
- HCV transmission in high-risk communities in Bulgaria.PloS one · 2019Article
- Automated quality control for a molecular surveillance system.BMC bioinformatics · 2018Article
- Fast estimation of genetic relatedness between members of heterogeneous populations of closely related genomic variants.BMC bioinformatics · 2018Article
- Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2018Article
- GHOST: global hepatitis outbreak and surveillance technology.BMC genomics · 2017Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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