Metagenomics, the study of genome sequences of diverse organisms cohabiting in a shared environment, has experienced significant advancements across various medical and biological fields. Metagenomic analysis is crucial, for instance, in clinical applications such as infectious disease screening and the diagnosis and early detection of diseases such as cancer. A key task in metagenomics is to determine the species present in a sample and their relative abundances. Currently, the field is dominated by either alignment-based tools, which offer high accuracy but are computationally expensive, or alignment-free tools, which are fast but lack the needed accuracy for many applications. In response to this dichotomy, we introduce MetaFast, a tool based on heuristics, to achieve a fundamental improvement in accuracy-runtime tradeoff over existing methods. MetaFast delivers accuracy comparable to the alignment-based and highly accurate tool Metalign but with significantly enhanced efficiency. In MetaFast, we accelerate memory-frugal reference database indexing and filtering. We further employ heuristics to accelerate read mapping. Our evaluation demonstrates that MetaFast achieves a 4x speedup over Metalign without compromising accuracy. MetaFast is publicly available at https://github.com/CMU-SAFARI/MetaFast.

翻译：宏基因组学，即对共享环境中共存的不同生物体基因组序列的研究，已在多个医学和生物学领域取得显著进展。宏基因组分析至关重要，例如在传染病筛查以及癌症等疾病的诊断与早期检测等临床应用中。宏基因组学的关键任务之一是确定样本中存在的物种及其相对丰度。目前，该领域主要由两类工具主导：一类基于比对，准确性高但计算成本昂贵；另一类免比对，速度快但缺乏许多应用所需的准确性。针对这一矛盾，我们提出MetaFast——一种基于启发式方法的工具，旨在从根本上改善现有方法在准确性与运行时间之间的权衡。MetaFast在准确度上与基于比对的高精度工具Metalign相当，但效率显著提升。在MetaFast中，我们加速了内存节约型的参考数据库索引与过滤，并进一步采用启发式方法加速读取映射。评估结果表明，MetaFast在保证准确度不降低的前提下，实现了相对于Metalign的4倍加速。MetaFast已在https://github.com/CMU-SAFARI/MetaFast上公开。