Details

Autor(en) / Beteiligte

• LaPierre, Nathan
• Alser, Mohammed
• Eskin, Eleazar
• Koslicki, David
• Mangul, Serghei

Titel

Metalign: efficient alignment-based metagenomic profiling via containment min hash

Ist Teil von

• Genome Biology, 2020-09, Vol.21 (1), p.1-242, Article 242

Ort / Verlag

London: BioMed Central

Erscheinungsjahr

2020

Link zum Volltext

Quelle

2022 ECC(Springer)

Beschreibungen/Notizen

• Metagenomic profiling, predicting the presence and relative abundances of microbes in a sample, is a critical first step in microbiome analysis. Alignment-based approaches are often considered accurate yet computationally infeasible. Here, we present a novel method, Metalign, that performs efficient and accurate alignment-based metagenomic profiling. We use a novel containment min hash approach to pre-filter the reference database prior to alignment and then process both uniquely aligned and multi-aligned reads to produce accurate abundance estimates. In performance evaluations on both real and simulated datasets, Metalign is the only method evaluated that maintained high performance and competitive running time across all datasets.