NG-Tax 2.0 is a semantic framework for FAIR high-throughput analysis and classification of marker gene amplicon sequences including bacterial and archaeal 16S ribosomal RNA (rRNA), eukaryotic 18S rRNA and ribosomal intergenic transcribed spacer sequences. It can directly use single or merged reads, paired-end reads and unmerged paired-end reads from long range fragments as input to generate de novo amplicon sequence variants (ASV). Using the RDF data model, ASV's can be automatically stored in a graph database as objects that link ASV sequences with the full data-wise and element-wise provenance, thereby achieving the level of interoperability required to utilize such data to its full potential. The graph database can be directly queried, allowing for comparative analyses of over thousands of samples and is connected with an interactive Rshiny toolbox for analysis and visualization of (meta) data. Additionally, NG-Tax 2.0 exports an extended BIOM 1.0 (JSON) file as starting point for further analyses by other means. The extended BIOM file contains new attribute types to include information about the command arguments used, the sequences of the ASVs formed, classification confidence scores and is backwards compatible. The performance of NG-Tax 2.0 was compared with DADA2, using the plugin in the QIIME 2 analysis pipeline. Fourteen 16S rRNA gene amplicon mock community samples were obtained from the literature and evaluated. Precision of NG-Tax 2.0 was significantly higher with an average of 0.95 vs 0.58 for QIIME2-DADA2 while recall was comparable with an average of 0.85 and 0.77, respectively. NG-Tax 2.0 is written in Java. The code, the ontology, a Galaxy platform implementation, the analysis toolbox, tutorials and example SPARQL queries are freely available at http://wurssb.gitlab.io/ngtax under the MIT License.

Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Results Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Conclusion The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). Many important PGPR as well as plant pathogens belong to the genus Pseudomonas. There is, however, uncertainty on the divide between beneficial and pathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here we used the Genome properties (GP) common biological pathways annotation system and Machine Learning (ML) to establish the relationship between the genome wide GP composition and the plant-associated lifestyle of 91 Pseudomonas strains isolated from the rhizosphere and the phyllosphere representing both plant-associated phenotypes. GP enrichment analysis, Random Forest model fitting and feature selection revealed 28 discriminating features. A test set of 75 new strains confirmed the importance of the selected features for classification. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated lifestyle.

The rhizosphere, the region of soil surrounding roots of plants, is colonized by a unique population of Plant Growth Promoting Rhizobacteria (PGPR). By enhancing nutrient uptake from the soil and through modulation of plant phytohormone status and metabolism, PGPR can increase the stress tolerance, growth and yield of crop plants. Many important PGPR as well as plant pathogens belong to the genus  Pseudomonas . There is, however, uncertainty on the divide between phytobeneficial and phytopathogenic strains as previously thought to be signifying genomic features have limited power to separate these strains. Here the Genome properties (GP) common biological pathways annotation system was applied to establish the relationship between the genome wide GP composition and the plant-associated phenotype of 91  Pseudomonas  strains representing both phenotypes. GP enrichment analysis, RandomForest model fitting and feature selection revealed 28 discriminating features. A validation dataset of 67 new strains confirmed the importance of the selected features for classification. A number of unexpected discriminating features were found, suggesting involvement of novel molecular mechanisms. The results suggest that GP annotations provide a promising computational tool to better classify the plant-associated phenotype.

This paper presents a method to automatically measure cardio-thoracic ratio (CTR) from a chest radiographic images using non-linear least square approximation and local minimum. The proposed method consists of initial boundary point identification, cardiac diameter measurement, thoracic diameter measurement and cardio-thoracic ratio measurement. First, the initial boundary points used to approximate the region of thoracic cavity are identified using general human anatomy features. Then the non-linear least square approximation and local minimum are used to detect the heart boundary. Finally, the thoracic cage boundary is detected and the cardio-thoracic ratio can be measured. The proposed method is tested on a set of 255 chest radiographs. The experimental results are evaluated using correlation test between two sets of numerical measurement which are measured by our proposed method and by the radiologists. The evaluation reveals that the correlation result on CTR is about 78%.