[论文解读] AirLift: A Fast and Comprehensive Technique for Translating Alignments between Reference Genomes

Jeremie S. Kim, Can Fırtına+6 more|arXiv (Cornell University)|Dec 18, 2019

Genomics and Phylogenetic Studies参考文献 33被引用 4

一句话总结

AirLift 是一种快速且全面的技术，通过最多减少 99.99% 的全基因组重映射需求，显著提升了参考基因组更新时的效率，与全映射相比，人类基因组执行时间减少 6.7 倍、线虫（C. elegans）减少 6.6 倍、酿酒酵母（yeast）减少 2.8 倍，同时保持与基线全映射相当的变异检测准确性。

ABSTRACT

As genome sequencing tools and techniques improve, researchers are able to incrementally assemble more accurate reference genomes, which enable sensitivity in read mapping and downstream analysis such as variant calling. A more sensitive downstream analysis is critical for a better understanding of the genome donor (e.g., health characteristics). Therefore, read sets from sequenced samples should ideally be mapped to the latest available reference genome that represents the most relevant population. Unfortunately, the increasingly large amount of available genomic data makes it prohibitively expensive to fully re-map each read set to its respective reference genome every time the reference is updated. There are several tools that attempt to accelerate the process of updating a read data set from one reference to another (i.e., remapping). However, if a read maps to a region in the old reference that does not appear with a reasonable degree of similarity in the new reference, the read cannot be remapped. We find that, as a result of this drawback, a significant portion of annotations are lost when using state-of-the-art remapping tools. To address this major limitation in existing tools, we propose AirLift, a fast and comprehensive technique for remapping alignments from one genome to another. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces 1) the number of reads that need to be fully mapped to the new reference by up to 99.99\% and 2) the overall execution time to remap read sets between two reference genome versions by 6.7x, 6.6x, and 2.8x for large (human), medium (C. elegans), and small (yeast) reference genomes, respectively. We validate our remapping results with GATK and find that AirLift provides similar accuracy in identifying ground truth SNP and INDEL variants as the baseline of fully mapping a read set.

研究动机与目标

• 解决现有重映射工具在参考基因组更新时效率低下和数据丢失的问题。
• 降低参考基因组更新后对测序读段进行重映射的计算成本。
• 最小化需要对新参考基因组进行全量重映射的读段数量。
• 在重映射后保持下游变异检测的高准确性。
• 为频繁更新参考基因组的大规模基因组数据提供可扩展的解决方案。

提出的方法

• AirLift 识别旧参考基因组中在新参考基因组中缺失或保守性差的区域。
• 采用基于快速比对的过滤步骤，排除映射到无法可靠重映射区域的读段。
• 该方法采用两阶段重映射策略：首先使用轻量级比对方法仅映射那些可能成功重映射的读段。
• 利用参考基因组之间的序列相似性和结构保守性，优先选择区域进行全量重映射。
• 仅将映射到保守区域的读段提交至新参考基因组进行全量比对。
• 该框架可与标准变异检测流程（如 GATK）集成，以验证重映射的准确性。

实验结果

研究问题

• RQ1 如何在从一个参考基因组更新到另一个参考基因组时，减少需要全量重映射的读段数量？
• RQ2 在最小化全量重映射操作的前提下，我们能在多大程度上保持变异检测的准确性？
• RQ3 我们能否在不牺牲读段比对敏感性的情况下，实现显著的重映射时间性能提升？
• RQ4 由于新参考基因组中缺乏保守区域，最先进的重映射工具中有多少比例的读段会丢失？
• RQ5 AirLift 在不同大小的基因组（人类、线虫、酿酒酵母）上的性能表现如何扩展？

主要发现

• 与全映射相比，AirLift 将需要全量重映射的读段数量最多减少了 99.99%。
• 重映射的整体执行时间在人类基因组上减少 6.7 倍、线虫（C. elegans）上减少 6.6 倍、酿酒酵母（yeast）上减少 2.8 倍。
• AirLift 维持了与全映射相当的变异检测准确性，经 GATK 在真实 SNP 和 INDEL 变异上的验证确认。
• 在最先进的重映射工具中，由于存在无法比对的区域，大量注释信息会丢失，而 AirLift 有效缓解了这一问题。
• 该方法在不同大小的基因组上均表现出高效的可扩展性，展现出一致的性能提升。