Efficient local alignment discovery amongst noisy long reads

有效的局部比对发现在嘈杂的长读

Long read sequencers portend the possibility of producing reference quality genomes not only because the reads are long, but also because sequencing errors and read sampling are almost perfectly random. However, the error rates are as high as 15%, necessitating an efficient algorithm for finding local alignments between reads at a 30% difference rate, a level that current algorithm designs cannot handle or handle inefficiently. In this paper we present a very efficient yet highly sensitive, threaded filter, based on a novel sort and merge paradigm, that proposes seed points between pairs of reads that are likely to have a significant local alignment passing through them. We also present a linear expected-time heuristic based on the classic O(nd) difference algorithm [1] that finds a local alignment passing through a seed point that is exceedingly sensitive, failing but once every billion base pairs. These two results have been combined into a software program we call DALIGN that realizes the fastest program to date for finding overlaps and local alignments in very noisy long read DNA sequencing data sets and is thus a prelude to de novo long read assembly.

长读测序器预示着产生参考质量基因组的可能性，不仅因为读长，还因为测序错误和读采样几乎完全随机。

然而，错误率高达15%，这就需要一种高效的算法来在30%的误码率下找到读取之间的局部对齐，这是目前的算法设计无法或不能有效处理的水平。在这篇论文中，我们提出了一种非常有效但高度敏感的线程过滤器，它基于一种新的排序和合并范例，提出读取对之间的种子点，这些种子点很可能有一个重要的局部对齐通过它们。

我们还提出了一个基于经典O(nd)差分算法[1]的线性期望时间启发式算法，该算法发现一个局部比对通过一个非常敏感的种子点，每10亿个碱基对失败一次。这两个结果已经被合并成一个软件程序，我们称之为DALIGN，它实现了迄今为止在非常嘈杂的长读DNA测序数据集中发现重叠和局部对齐的最快程序，因此是从头长读组装的前奏。

参考文献 :

https://link.springer.com/chapter/10.1007%2F978-3-662-44753-6_5

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