Z Dimitrova, D S Campo, S Ramachandran, G Vaughan, L Ganova-Raeva, Y Lin, J C Forbi, G Xia, P Skums, B Pearlman, Y Khudyakov
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引用次数: 17
Abstract
Hepatitis C Virus sequence studies mainly focus on the viral amplicon containing the Hypervariable region 1 (HVR1) to obtain a sample of sequences from which several population genetics parameters can be calculated. Recent advances in sequencing methods allow for analyzing an unprecedented number of viral variants from infected patients and present a novel opportunity for understanding viral evolution, drug resistance and immune escape. In the present paper, we compared three recent technologies for amplicon analysis: (i) Next-Generation Sequencing; (ii) Clonal sequencing using End-point Limiting-dilution for isolation of individual sequence variants followed by Real-Time PCR and sequencing; and (iii) Mass spectrometry of base-specific cleavage reactions of a target sequence. These three technologies were used to assess intra-host diversity and inter-host genetic relatedness in HVR1 amplicons obtained from 38 patients (subgenotypes 1a and 1b). Assessments of intra-host diversity varied greatly between sequence-based and mass-spectrometry-based data. However, assessments of inter-host variability by all three technologies were equally accurate in identification of genetic relatedness among viral strains. These results support the application of all three technologies for molecular epidemiology and population genetics studies. Mass spectrometry is especially promising given its high throughput, low cost and comparable results with sequence-based methods.
In Silico BiologyComputer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍:
The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.