MOTIVATION: Ecological patterns of the human microbiota exhibit high inter-subject variation, with few operational taxonomic units (OTUs) shared across individuals. To overcome these issues, non-parametric approaches, such as the Mann-Whitney U-test and Wilcoxon rank-sum test, have often been used to identify OTUs associated with host diseases. However, these approaches only use the ranks of observed relative abundances, leading to information loss, and are associated with high false-negative rates. In this study, we propose a phylogenetic tree-based microbiome association test (TMAT) to analyze the associations between microbiome OTU abundances and disease phenotypes. Phylogenetic trees illustrate patterns of similarity among different OTUs, and TMAT provides an efficient method for utilizing such information for association analyses. The proposed TMAT provides test statistics for each node, which are combined to identify mutations associated with host diseases. RESULTS: Power estimates of TMAT were compared with existing methods using extensive simulations based on real absolute abundances. Simulation studies showed that TMAT preserves the nominal type-1 error rate, and estimates of its statistical power generally outperformed existing methods in the considered scenarios. Furthermore, TMAT can be used to detect phylogenetic mutations associated with host diseases, providing more in-depth insight into bacterial pathology. AVAILABILITY AND IMPLEMENTATION: The 16S rRNA amplicon sequencing metagenomics datasets for colorectal carcinoma and myalgic encephalomyelitis/chronic fatigue syndrome are available from the European Nucleotide Archive (ENA) database under project accession number PRJEB6070 and PRJEB13092, respectively. TMAT was implemented in the R package. Detailed information is available at http://healthstat.snu.ac.kr/software/tmat. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Presented in the paper is the PLC symbol naming rules in order to simulate the virtual factory which is operated by the PLC control logic. In the PLC simulation, the corresponding model, normally called as the plant model, to the PLC receives the PLC output signal, operates virtual devices, and sends the changed PLC input signal which is updated by the virtual device movements. Therefore the plant model is necessary to verify PLC code and to simulate the virtual factory. Normally, the plant model construction takes much time and effort, requires in-depth knowledge about the simulation and PLC code, and even it might has modelling errors. To prevent the modelling error and to save the time and effort for the I/O model construction, this paper proposes the naming rules of PLC symbol to extract the necessary information. The simple example cell is applied to test the proposed approach.