E sufficient to estimate 1516647 the population diversity, i.e., number and frequencies of clones. Second, we have attempted to assemble short reads into global haplotypes. This approach is statistically and computationally more challenging, but has the potential to recover all full-length haplotype sequences. In dealing with NGS data, one has to take into account sequencing errors. Without a proper treatment, they would artificially inflate the estimated diversity. The approach presented here uses clustering of reads as a method to correct errors. Further measures would be to take quality scores into account, or to correct for strand bias. Variants that are observed prevalently on one strand are more likely to be artifacts than real biological variants [15]. The improved results obtained for the non-PCR amplified samples show that an additional source of noise is given by the PCR amplification, which can contribute in different ways to inflate and distort the observed diversity. Amplification efficiency can vary among different haplotypes, leading to an amplification bias. Moreover, PCR can introduce Thiazole Orange artificial variants into the sample by point mutations and, to a much larger extent, by recombination [16]. These in vitro chimera resulted in a larger number of false positives for 454/Roche than for Illumina (Table 2), because recombination is more likely to occur and to be detected in longer reads. Carefully chosen PCR conditions can minimize the impact of these 
artifacts [32]. For global haplotype reconstruction, we employed a combinatorial inference algorithm based on the read graph. This approach can easily generate recombinant sequences that are not part of the true underlying population, especially if diversity is low and not all read errors have been corrected. Such artificial in silico chimera areresponsible for a large number of false positives in global haplotype reconstruction at deep coverage and might explain the decreasing global reconstruction performance with increasing coverage in some situations. Global haplotype inference may be improved by using alternative methods [20,24,26,33], or by exploiting pairedend reads to phase variants detected 
at large genomic distances. The results presented here are subject to the specific limitations of ShoRAH’s reconstruction algorithm. Other computational tools, including also improved error correction [34], might perform better under some circumstances, but the general limitation observed in this study will remain. Future studies are needed to delineate the feasibility of global haplotype reconstruction in terms of the underlying population diversity, the employed sequencing technology and parameters, and the computational strategy for haplotype inference. The ability to detect and reconstruct diversity improves with decreasing sequencing error rate and with increasing number of polymorphic sites. As a consequence, for any given level of viral diversity in the sample, sequencing a longer region will result in better diversity estimates, for a given error rate. Since the diversity is usually unknown in advance, it is generally impossible to determine a priori the expected performance of a specific Salmon calcitonin price platform in reconstructing the viral population. We have highlighted and quantified here the trade-off between read length and depth of coverage, namely higher accuracy in global haplotype reconstruction with long reads versus improved sensitivity and specificity in local haplotype reconstruction, e.E sufficient to estimate 1516647 the population diversity, i.e., number and frequencies of clones. Second, we have attempted to assemble short reads into global haplotypes. This approach is statistically and computationally more challenging, but has the potential to recover all full-length haplotype sequences. In dealing with NGS data, one has to take into account sequencing errors. Without a proper treatment, they would artificially inflate the estimated diversity. The approach presented here uses clustering of reads as a method to correct errors. Further measures would be to take quality scores into account, or to correct for strand bias. Variants that are observed prevalently on one strand are more likely to be artifacts than real biological variants [15]. The improved results obtained for the non-PCR amplified samples show that an additional source of noise is given by the PCR amplification, which can contribute in different ways to inflate and distort the observed diversity. Amplification efficiency can vary among different haplotypes, leading to an amplification bias. Moreover, PCR can introduce artificial variants into the sample by point mutations and, to a much larger extent, by recombination [16]. These in vitro chimera resulted in a larger number of false positives for 454/Roche than for Illumina (Table 2), because recombination is more likely to occur and to be detected in longer reads. Carefully chosen PCR conditions can minimize the impact of these artifacts [32]. For global haplotype reconstruction, we employed a combinatorial inference algorithm based on the read graph. This approach can easily generate recombinant sequences that are not part of the true underlying population, especially if diversity is low and not all read errors have been corrected. Such artificial in silico chimera areresponsible for a large number of false positives in global haplotype reconstruction at deep coverage and might explain the decreasing global reconstruction performance with increasing coverage in some situations. Global haplotype inference may be improved by using alternative methods [20,24,26,33], or by exploiting pairedend reads to phase variants detected at large genomic distances. The results presented here are subject to the specific limitations of ShoRAH’s reconstruction algorithm. Other computational tools, including also improved error correction [34], might perform better under some circumstances, but the general limitation observed in this study will remain. Future studies are needed to delineate the feasibility of global haplotype reconstruction in terms of the underlying population diversity, the employed sequencing technology and parameters, and the computational strategy for haplotype inference. The ability to detect and reconstruct diversity improves with decreasing sequencing error rate and with increasing number of polymorphic sites. As a consequence, for any given level of viral diversity in the sample, sequencing a longer region will result in better diversity estimates, for a given error rate. Since the diversity is usually unknown in advance, it is generally impossible to determine a priori the expected performance of a specific platform in reconstructing the viral population. We have highlighted and quantified here the trade-off between read length and depth of coverage, namely higher accuracy in global haplotype reconstruction with long reads versus improved sensitivity and specificity in local haplotype reconstruction, e.
