PREMIER -在错误读取中使用马尔可夫推理的概率对象错误校正
PREMIER - PRobabilistic Error-correction using Markov Inference in Errored Reads
摘要：
在这项工作中，我们提出了一个灵活的，概率和参考的高通量DNA测序数据的纠错方法。关键是利用测序数据的高覆盖率，并将短序列输出建模为隐马尔可夫模型（HMM）的独立实现。我们提出的问题的错误校正的读取作为一个最大似然序列检测在这个HMM。虽然时间和内存的考虑排除了最佳鲍姆-韦尔奇算法（参数估计）和最佳维特比算法（纠错）的实施，我们提出了低复杂度的近似版本。具体来说，我们提出了一个近似维特比和顺序解码的纠错算法。我们的研究结果表明，当与爬行动物，一个国家的最先进的纠错方法相比，我们的方法始终实现优越的性能在模拟和真实的数据集。
英文摘要：
In this work we present a flexible, probabilistic and reference-free method of error correction for high throughput DNA sequencing data. The key is to exploit the high coverage of sequencing data and model short sequence outputs as independent realizations of a Hidden Markov Model (HMM). We pose the problem of error correction of reads as one of maximum likelihood sequence detection over this HMM. While time and memory considerations rule out an implementation of the optimal Baum-Welch algorithm (for parameter estimation) and the optimal Viterbi algorithm (for error correction), we propose low-complexity approximate versions of both. Specifically, we propose an approximate Viterbi and a sequential decoding based algorithm for the error correction. Our results show that when compared with Reptile, a state-of-the-art error correction method, our methods consistently achieve superior performances on both simulated and real data sets.
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