使用最小散列的变体容限读取映射
Variant tolerant read mapping using min-hashing
摘要：
DNA读段作图是生物信息学中普遍存在的任务，并且已经开发了许多工具来解决读段作图问题。然而，有两个趋势正在改变readmapping的前景：首先，新的测序技术提供了具有高错误率（高达15%）的非常长的读取。其次，群体中的许多遗传变异是已知的，因此参考基因组不被认为是ACGT上的单个字符串，而是包含这些变异的复杂对象。大多数现有的读取映射器不能适当地处理这些新情况。我们引入了一个新的读映射器原型称为VATRAM，认为变量。它基于参考基因组窗口的q-gram集的最小散列。Min-Hashing是一种局部敏感哈希。变体直接插入到VATRAM索引中，从而实现快速映射过程。我们的研究结果表明，VATRAM实现了更好的精度和召回比国家的最先进的读取映射器，如BWA在某些情况下。VATRAM是开源的，可以在https://bitbucket.org/Quedenfeld/vatram-src/上访问。
英文摘要：
DNA read mapping is a ubiquitous task in bioinformatics, and many tools have been developed to solve the read mapping problem. However, there are two trends that are changing the landscape of readmapping: First, new sequencing technologies provide very long reads with high error rates (up to 15%). Second, many genetic variants in the population are known, so the reference genome is not considered as a single string over ACGT, but as a complex object containing these variants. Most existing read mappers do not handle these new circumstances appropriately. We introduce a new read mapper prototype called VATRAM that considers variants. It is based on Min-Hashing of q-gram sets of reference genome windows. Min-Hashing is one form of locality sensitive hashing. The variants are directly inserted into VATRAMs index which leads to a fast mapping process. Our results show that VATRAM achieves better precision and recall than state-of-the-art read mappers like BWA under certain cirumstances. VATRAM is open source and can be accessed at https://bitbucket.org/Quedenfeld/vatram-src/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
