Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA.
Environmental Genomics and Systems Biology Division, E O Lawrence Berkeley National Laboratory, Berkeley, CA.
Bioinformatics. 2018 Dec 15;34(24):4284-4286. doi: 10.1093/bioinformatics/bty510.
Genome-resolved metagenomics allows the construction of draft microbial genomes from short-read shotgun metagenomics (Metagenome-Assembled Genomes, or MAGs); however, even high-quality MAGs are typically somewhat incomplete and contain a small amount of contaminant sequence, making accurate prediction of metabolic potential challenging. Here, we describe MetaPOAP, an algorithm for probabalistic assessment of the statistical likelihoods for the presence or absence of metabolic pathways in MAGs.
MetaPOAP is available as Python scripts on GitHub or from the Fischer lab webpage, https://github.com/lmward/MetaPOAP.
Supplementary data are available at Bioinformatics online.
基因组解析宏基因组学允许从短读长 shotgun 宏基因组学（宏基因组组装基因组，或 MAGs）构建微生物基因组草案；然而，即使是高质量的 MAGs 通常也有些不完整，并包含少量污染物序列，这使得准确预测代谢潜力具有挑战性。在这里，我们描述了 MetaPOAP，这是一种用于评估 MAGs 中代谢途径存在或缺失的统计可能性的概率算法。
MetaPOAP 可作为 GitHub 上的 Python 脚本或从 Fischer 实验室网页获得，网址为 https://github.com/lmward/MetaPOAP。
补充数据可在 Bioinformatics 在线获得。
