利用NGS扩增子序列重建病毒准种。

Q2 Medicine In Silico Biology Pub Date : 2011-01-01 DOI:10.3233/ISB-2012-0458
Nicholas Mancuso, Bassam Tork, Pavel Skums, Lilia Ganova-Raeva, Ion Măndoiu, Alex Zelikovsky
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引用次数: 24

摘要

本文解决了从扩增子获得的下一代测序读取(即从预定义的扩增重叠区域产生的读取)重建病毒准种的问题。我们比较了该问题的简约模型和似然模型,并提出了几种新的装配算法。所提出的方法已在模拟无错误HCV和真实HBV扩增子读取上得到验证。新算法已被证明优于Prosperi等人的方法。我们的实验还表明,在大多数情况下,通过扩增子读取比霰弹枪读取更准确地重建病毒准种。所有算法都已实现,并可在https://bitbucket.org/nmancuso/bioa/上获得。
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Reconstructing viral quasispecies from NGS amplicon reads.

This paper addresses the problem of reconstructing viral quasispecies from next-generation sequencing reads obtained from amplicons (i.e., reads generated from predefined amplified overlapping regions). We compare the parsimonious and likelihood models for this problem and propose several novel assembling algorithms. The proposed methods have been validated on simulated error-free HCV and real HBV amplicon reads. The new algorithms have been shown to outperform the method of Prosperi et. al. Our experiments also show that viral quasispecies can be reconstructed in most cases more accurately from amplicon reads rather than shotgun reads. All algorithms have been implemented and made available at https://bitbucket.org/nmancuso/bioa/.

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来源期刊
In Silico Biology
In Silico Biology Computer 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.
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