Efficient detection of viral transmission with threshold-based methods

Rytsareva, Inna; Sims, Seth; Chockalingam, Sriram P.; Aluru, Srinivas; Campo, David S.; Tetik, Cansu; Thankachan, Sharma V.; Khudyakov, Yury; Zheng, Yueli; Chirag, Jain; Sue, Amanda

doi:10.1109/iccabs.2015.7344723

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Further, such graphs can be constructed using any type of validated genetic distances; e.g., distances among mass-spectrometric profiles as was shown earlier in our laboratory [46]. Finally, the current approach allows the implementation of very efficient computational algorithms to remove patient-pairs that can’t have sequences with a distance below the threshold, reducing considerably demand on computational resources [47]. …”

Section: Discussionmentioning

confidence: 99%

Accurate Genetic Detection of Hepatitis C Virus Transmissions in Outbreak Settings

et al. 2015

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Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections are associated with unsafe injection practices, drug diversion, and other exposures to blood, being difficult to detect and investigate. Here, we developed and validated a simple approach for molecular detection of HCV transmissions in outbreak settings. We obtained sequences from the HCV hypervariable region 1 (HVR1) using End-Point Limiting-Dilution (EPLD) from 127 cases involved in 32 epidemiologically defined HCV outbreaks and 193 individuals with unrelated HCV strains. We compared several types of genetic distances and calculated a threshold using minimal Hamming distances that identifies transmission clusters in all tested outbreaks with 100% accuracy. The approach was also validated on sequences from 239 individuals obtained using next-generation sequencing, showing the same accuracy as EPLD. In average, nucleotide diversity of the intra-host population was 6.2-times greater in the source than in any incident case, allowing the correct detection of transmission direction in 8 outbreaks for which source cases were known. A simple and accurate distance-based approach for detecting HCV transmissions developed here streamlines molecular investigation of outbreaks, thus improving the public health capacity for rapid and effective control of hepatitis C.

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Section: Discussionmentioning

confidence: 99%

Accurate Genetic Detection of Hepatitis C Virus Transmissions in Outbreak Settings

et al. 2015

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A Study of Next Generation Sequencing Data, Workflow, Application and Platform Comparison

Thankachan

Thomas

2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences

Campo

Khudyakov

2020

BMC Bioinformatics

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Background In molecular epidemiology, comparison of intra-host viral variants among infected persons is frequently used for tracing transmissions in human population and detecting viral infection outbreaks. Application of Ultra-Deep Sequencing (UDS) immensely increases the sensitivity of transmission detection but brings considerable computational challenges when comparing all pairs of sequences. We developed a new population comparison method based on convex hulls in hamming space. We applied this method to a large set of UDS samples obtained from unrelated cases infected with hepatitis C virus (HCV) and compared its performance with three previously published methods. Results The convex hull in hamming space is a data structure that provides information on: (1) average hamming distance within the set, (2) average hamming distance between two sets; (3) closeness centrality of each sequence; and (4) lower and upper bound of all the pairwise distances among the members of two sets. This filtering strategy rapidly and correctly removes 96.2% of all pairwise HCV sample comparisons, outperforming all previous methods. The convex hull distance (CHD) algorithm showed variable performance depending on sequence heterogeneity of the studied populations in real and simulated datasets, suggesting the possibility of using clustering methods to improve the performance. To address this issue, we developed a new clustering algorithm, k-hulls, that reduces heterogeneity of the convex hull. This efficient algorithm is an extension of the k-means algorithm and can be used with any type of categorical data. It is 6.8-times more accurate than k-mode, a previously developed clustering algorithm for categorical data. Conclusions CHD is a fast and efficient filtering strategy for massively reducing the computational burden of pairwise comparison among large samples of sequences, and thus, aiding the calculation of transmission links among infected individuals using threshold-based methods. In addition, the convex hull efficiently obtains important summary metrics for intra-host viral populations.

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Efficient detection of viral transmission with threshold-based methods

Cited by 3 publications

References 16 publications

Accurate Genetic Detection of Hepatitis C Virus Transmissions in Outbreak Settings

Accurate Genetic Detection of Hepatitis C Virus Transmissions in Outbreak Settings

A Study of Next Generation Sequencing Data, Workflow, Application and Platform Comparison

Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences

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