Environmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
Genome Biol. 2024 Aug 19;25(1):226. doi: 10.1186/s13059-024-03363-y.
Long-read sequencing holds great potential for characterizing complex microbial communities, yet taxonomic profiling tools designed specifically for long reads remain lacking. We introduce Melon, a novel marker-based taxonomic profiler that capitalizes on the unique attributes of long reads. Melon employs a two-stage classification scheme to reduce computational time and is equipped with an expectation-maximization-based post-correction module to handle ambiguous reads. Melon achieves superior performance compared to existing tools in both mock and simulated samples. Using wastewater metagenomic samples, we demonstrate the applicability of Melon by showing it provides reliable estimates of overall genome copies, and species-level taxonomic profiles.
长读测序在描述复杂微生物群落方面具有巨大的潜力，但专门为长读测序设计的分类工具仍然缺乏。我们介绍了 Melon，这是一种新颖的基于标记的分类工具，利用了长读测序的独特属性。Melon 采用了两阶段分类方案，以减少计算时间，并配备了基于期望最大化的后校正模块，以处理模糊读取。与现有工具相比，Melon 在模拟和模拟样本中都表现出了优越的性能。我们使用废水宏基因组样本，通过显示它提供了可靠的总基因组拷贝和种级分类分析的估计，证明了 Melon 的适用性。
