In contrast, achieving high sequencing depth for viral samples is very inexpensive, owing to the short length of viral genomes. Mapping based approaches to QSR are naturally preferred to de novo assembly since reference genomes are available (or easy to obtain) for viruses of interest, and viral genomes do not contain repeats. Thus, it is not surprising that such approaches were adopted in the two pioneering works on the QSR problem [24, 25] . Eriksson et al. [24] proposed a multi-step approach consisting of sequencing error correction via clustering, haplotype reconstruction via chain decomposition, and haplotype frequency estimation via expectation-maximization, with validation on HIV data. In Westbrooks et al. [25] , the focus is on haplotype reconstruction via transitive reduction, overlap probability estimation and network flows, with application to simulated error-free HCV data. Recently, the QSR software tool ShoRAH was developed [26] and applied to HIV data [27] . Another combinatorial method for QSR was also developed and applied to HIV and HBV data in [28] , with results similar to those of ShoRAH. Our contributions in this paper are as follows: