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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >ADAPTIVE CONTROL OF HYBRIDIZATION NOISE IN DNA SEQUENCING-BY-HYBRIDIZATION
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ADAPTIVE CONTROL OF HYBRIDIZATION NOISE IN DNA SEQUENCING-BY-HYBRIDIZATION

机译：杂交DNA测序中杂交噪声的自适应控制

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We consider the problem of sequence reconstruction in sequencing-by-hybridization in the presence of spectrum errors. As suggested by intuition, and reported in the literature, false-negatives (i.e., missing spectrum probes) are by far the leading cause of reconstruction failures. In a recent paper we have described an algorithm, called "threshold-θ", designed to recover from false negatives. This algorithm is based on overcompensating for missing extensions by allowing larger reconstruction subtrees. We demonstrated, both analytically and with simulations, the increasing effectiveness of the approach as the parameter θ grows, but also pointed out that for larger error rates the size of the extension trees translates into an unacceptable computational burden. To obviate this shortcoming, in this paper we propose an adaptive approach which is both effective and efficient. Effective, because for a fixed value of θ it performs as well as its single-threshold counterpart, efficient because it exhibits substantial speed-ups over it. The idea is that, for moderate error rates a small fraction of the target sequence can be involved in error recovery; thus, expectedly the remainder of the sequence is reconstructible by the standard noiseless algorithm, with the provision to switch to operation with increasingly higher thresholds after detecting failure. This policy generates interesting and complex interplays between fooling probes and false negatives. These phenomena are carefully analyzed for random sequences and the results are found to be in excellent agreement with the simulations. In addition, the experimental algorithmic speed-ups of the multithreshold approach are explained in terms of the interaction amongst the different threshold regimes.
机译：我们考虑在频谱误差存在下逐杂交中序列重建的问题。如直觉所提出的，并在文献中报告，假阴性（即，缺失的频谱探针）是迄今为止重建失败的主要原因。在最近的一篇论文中，我们描述了一种称为“阈值-θ”的算法，该算法旨在从错误的否定。该算法基于允许较大的重建子树来超越缺失扩展。我们在分析和模拟中证明了这种方法的效率越来越大，因为参数θ增长，但也指出，对于更大的误差速率，延伸树的大小转化为不可接受的计算负担。为了避免这种缺点，本文提出了一种自适应方法，既有效又高效。有效，因为对于θ的固定值，它表现出以及其单阈值对应物，所以它效率高效，因为它表现出大量速度。这个想法是，对于中等的误差率，目标序列的一小部分可以涉及错误恢复;因此，预期的序列的其余部分是由标准无噪声算法重建的，在检测到失败后，该算法可以通过提供越来越高的阈值切换到操作。此策略产生鬼混探针和假底片之间的有趣和复杂相互作用。这些现象被仔细分析随机序列，结果被发现与模拟的良好一致。另外，在不同阈值制度之间的相互作用方面解释多线程方法的实验算法速度。

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