MetaCache-GPU：超快速宏基因组分类
MetaCache-GPU: Ultra-Fast Metagenomic Classification
摘要：
DNA测序的成本在过去十年中呈指数级下降，使越来越多的科学家能够获得基因组数据。在生物信息学中，通常通过构建索引数据结构来促进大基因组序列内的短DNA序列（读段）的定位，所述索引数据结构允许子串的有效查询。最近的宏基因组分类流水线通过相对于参考基因组数据库分析其$k$ -mer直方图来用分类标签注释读段。基于CPU的索引构造通常在预处理阶段执行，这是因为构建不规则数据结构（如哈希映射）的成本相对较高。然而，快速增长的数量可用的参考基因组建立了索引建设和查询的互动速度的需要。在本文中，我们介绍了MetaCache-GPU -一种超快速的宏基因组短读分类器，专门为适应CUDA加速器的特性而量身定制。我们的方法采用了一种新的哈希表变体，具有高效的minhash指纹读取位置敏感的哈希和他们的快速插入使用翘曲聚合操作。我们的性能评估表明，MetaCache-GPU能够在几秒钟内构建大型参考数据库，实现即时可操作性，而流行的基于CPU的工具（如Kraken 2）需要一个多小时才能在相同的数据上构建索引。在参考基因组数量不断增长的背景下，MetaCache-GPU是第一个宏基因组分类器，它使分析管道与大规模参考基因组集的按需组合变得实用。源代码可在https://github.com/muellan/metacache上公开获得。
英文摘要：
The cost of DNA sequencing has dropped exponentially over the past decade, making genomic data accessible to a growing number of scientists. In bioinformatics, localization of short DNA sequences (reads) within large genomic sequences is commonly facilitated by constructing index data structures which allow for efficient querying of substrings. Recent metagenomic classification pipelines annotate reads with taxonomic labels by analyzing their $k$-mer histograms with respect to a reference genome database. CPU-based index construction is often performed in a preprocessing phase due to the relatively high cost of building irregular data structures such as hash maps. However, the rapidly growing amount of available reference genomes establishes the need for index construction and querying at interactive speeds. In this paper, we introduce MetaCache-GPU -- an ultra-fast metagenomic short read classifier specifically tailored to fit the characteristics of CUDA-enabled accelerators. Our approach employs a novel hash table variant featuring efficient minhash fingerprinting of reads for locality-sensitive hashing and their rapid insertion using warp-aggregated operations. Our performance evaluation shows that MetaCache-GPU is able to build large reference databases in a matter of seconds, enabling instantaneous operability, while popular CPU-based tools such as Kraken2 require over an hour for index construction on the same data. In the context of an ever-growing number of reference genomes, MetaCache-GPU is the first metagenomic classifier that makes analysis pipelines with on-demand composition of large-scale reference genome sets practical. The source code is publicly available at https://github.com/muellan/metacache .
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