CFViSA: A comprehensive and free platform for visualization and statistics in omics-data.

INTRODUCTION: The rapid growth of omics technologies has led to the use of bioinformatics as a powerful tool for unravelling scientific puzzles. However, the obstacles of bioinformatics are compounded by the complexity of data processing and the distinct nature of omics data types, particularly in terms of visualization and statistics. OBJECTIVES: We developed a comprehensive and free platform, CFViSA, to facilitate effortless visualization and statistical analysis of omics data by the scientific community. METHODS: CFViSA was constructed using the Scala programming language and utilizes the AKKA toolkit for the web server and MySQL for the database server. The visualization and statistical analysis were performed with the R program. RESULTS: CFViSA integrates two omics data analysis pipelines (microbiome and transcriptome analysis) and an extensive array of 79 analysis tools spanning simple sequence processing, visualization, and statistics available for various omics data, including microbiome and transcriptome data. CFViSA starts from an analysis interface, paralleling a demonstration full course to help users understand operating principles and scientifically set the analysis parameters. Once analysis is conducted, users can enter the task history interface for figure adjustments, and then a complete series of results, including statistics, feature tables and figures. All the graphic layouts were printed with necessary statistics and a traceback function recording the options for analysis and visualization; these statistics were excluded from the five competing methods. CONCLUSION: CFViSA is a user-friendly bioinformatics cloud platform with detailed guidelines for integrating functions in multi-omics analysis with real-time visualization adjustment and complete series of results provision. CFViSA is available at http://www.cloud.biomicroclass.com/en/CFViSA/.

DCiPatho: deep cross-fusion networks for genome scale identification of pathogens.

Pathogen detection from biological and environmental samples is important for global disease control. Despite advances in pathogen detection using deep learning, current algorithms have limitations in processing long genomic sequences. Through the deep cross-fusion of cross, residual and deep neural networks, we developed DCiPatho for accurate pathogen detection based on the integrated frequency features of 3-to-7 k-mers. Compared with the existing state-of-the-art algorithms, DCiPatho can be used to accurately identify distinct pathogenic bacteria infecting humans, animals and plants. We evaluated DCiPatho on both learned and unlearned pathogen species using both genomics and metagenomics datasets. DCiPatho is an effective tool for the genomic-scale identification of pathogens by integrating the frequency of k-mers into deep cross-fusion networks. The source code is publicly available at https://github.com/LorMeBioAI/DCiPatho.

Bio-Organic Fertilizer Promotes Pear Yield by Shaping the Rhizosphere Microbiome Composition and Functions.

Bio-organic fertilizers (BOF) containing both organic amendments and beneficial microorganisms have been consistently shown to improve soils fertility and yield. However, the exact mechanisms which link amendments and yields remain disputed, and the complexity of bio-organic fertilizers may work in parallel in several ways. BOF may directly improve yield by replenishing soil nutrients or introducing beneficial microbial genes or indirectly by altering the soil microbiome to enrich native beneficial microorganisms. In this work, we aim to disentangle the relative contributions of direct and indirect effects on pear yield. We treated pear trees with either chemical fertilizer or organic fertilizer with/without the plant-beneficial bacterium Bacillus velezensis SQR9. We then assessed, in detail, soil physicochemical and biological properties (metagenome sequencing) as well as pear yield. We then evaluated the relative importance of direct and indirect effects of soil amendments on pear yield. Both organic treatments increased plant yield by up to 20%, with the addition of bacteria tripling the increase driven by organic fertilizer alone. This increase could be linked to alterations in soil physicochemical properties, bacterial community function, and metabolism. Supplementation of organic fertilizer SQR9 increased rhizosphere microbiome richness and functional diversity. Fertilizer-sensitive microbes and functions responded as whole guilds. Pear yield was most positively associated with the Mitsuaria- and Actinoplanes-dominated ecological clusters and with gene clusters involved in ion transport and secondary metabolite biosynthesis. Together, these results suggested that bio-organic fertilizers mainly act indirectly on plant yield by creating soil chemical properties which promote a plant-beneficial microbiome. IMPORTANCE Bio-organic fertilization is a widely used, eco-friendly, sustainable approach to increasing plant productivity in the agriculture and fruit industries. However, it remains unclear whether the promotion of fruit productivity is related to specific changes in microbial inoculants, the resident microbiome, and/or the physicochemical properties of rhizosphere soils. We found that bio-organic fertilizers alter soil chemical properties, thus manipulating specific microbial taxa and functions within the rhizosphere microbiome of pear plants to promote yield. Our work unveils the ecological mechanisms which underlie the beneficial impacts of bio-organic fertilizers on yield promotion in fruit orchards, which may help in the design of more efficient biofertilizers to promote sustainable fruit production.