LISA：走向学习型DNA序列搜索
LISA: Towards Learned DNA Sequence Search
摘要：
下一代测序（NGS）技术已经能够以高通量对数十亿个短DNA片段进行经济实惠的测序，为人口规模的基因组学铺平了道路。这种规模的基因组学数据分析需要克服性能瓶颈，例如在长参考序列上搜索短DNA序列。在本文中，我们介绍了LISA（序列分析的学习索引），一种新的基于学习的DNA序列搜索方法。作为概念的第一个证明，我们专注于加速问题的最基本的风味之一，称为精确搜索。LISA建立并扩展了FM索引，这是广泛部署在基因组学工具链中的最先进技术。人类基因组数据集的初步实验表明，LISA与传统的同类产品相比，性能加速高达4倍。
英文摘要：
Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics. Genomics data analytics at this scale requires overcoming performance bottlenecks, such as searching for short DNA sequences over long reference sequences. In this paper, we introduce LISA (Learned Indexes for Sequence Analysis), a novel learning-based approach to DNA sequence search. As a first proof of concept, we focus on accelerating one of the most essential flavors of the problem, called exact search. LISA builds on and extends FM-index, which is the state-of-the-art technique widely deployed in genomics tool-chains. Initial experiments with human genome datasets indicate that LISA achieves up to a factor of 4X performance speedup against its traditional counterpart.
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