
본 연구에서는 대규모 메타바코딩 데이터셋에 대한 신속하고 정확한 분류 방법을 제시한다. 이를 위해 근사 우도 기반 계통수 배치 방법을 개발하였으며, 다양한 모의실험 및 실제 데이터 분석을 통해 기존 방법들에 비해 우수한 성능을 보였다.


coremsg

Environmental DNA (eDNA) is becoming an increasingly important tool in diverse scientific fields from ecological biomonitoring to wastewater surveillance of viruses. The fundamental challenge in eDNA analyses has been the bioinformatical assignment of reads to taxonomic groups. It has long been known that full probabilistic methods for phylogenetic assignment are preferable, but unfortunately, such methods are computationally intensive and are typically inapplicable to modern Next-Generation Sequencing data. We here present a fast approximate likelihood method for phylogenetic assignment of DNA sequences. Applying the new method to several mock communities and simulated datasets, we show that it identifies more reads at both high and low taxonomic levels more accurately than other leading methods. The advantage of the method is particularly apparent in the presence of polymorphisms and/or sequencing errors and when the true species is not represented in the reference database.

### Competing Interest Statement

The authors have declared no competing interest.

A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets

대규모-메타바코딩-데이터셋에-대한-정확한-커뮤니티-프로파일링을-위한-신속한-계통수-기반-방법


대규모 메타바코딩 데이터셋에 대한 정확한 커뮤니티 프로파일링을 위한 신속한 계통수 기반 방법
