Efficient detection of viral transmission with threshold-based methods

Hepatitis 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. For instance, our relatively small dataset of 401 samples, a total of 80200 pairwise sample comparisons must be performed, which account for 4.56 x 1010 pairwise sequence comparisons. We present a fast and efficient three-step filtering strategy that 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 This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.