MetaPhage: an Automated Pipeline for Analyzing, Annotating, and Classifying Bacteriophages in Metagenomics Sequencing Data.

Phages are the most abundant biological entities on the planet, and they play an important role in controlling density, diversity, and network interactions among bacterial communities through predation and gene transfer. To date, a variety of bacteriophage identification tools have been developed that differ in the phage mining strategies used, input files requested, and results produced. However, new users attempting bacteriophage analysis can struggle to select the best methods and interpret the variety of results produced. Here, we present MetaPhage, a comprehensive reads-to-report pipeline that streamlines the use of multiple phage miners and generates an exhaustive report. The report both summarizes and visualizes the key findings and enables further exploration of key results via interactive filterable tables. The pipeline is implemented in Nextflow, a widely adopted workflow manager that enables an optimized parallelization of tasks in different locations, from local server to the cloud; this ensures reproducible results from containerized packages. MetaPhage is designed to enable scalability and reproducibility; also, it can be easily expanded to include new miners and methods as they are developed in this continuously growing field. MetaPhage is freely available under a GPL-3.0 license at https://github.com/MattiaPandolfoVR/MetaPhage. IMPORTANCE Bacteriophages (viruses that infect bacteria) are the most abundant biological entities on earth and are increasingly studied as members of the resident microbiota community in many environments, from oceans to soils and the human gut. Their identification is of great importance to better understand complex bacterial dynamics and microbial ecosystem function. A variety of metagenome bacteriophage identification tools have been developed that differ in the phage mining strategies used, input files requested, and results produced. To facilitate the management and the execution of such a complex workflow, we developed MetaPhage (MP), a comprehensive reads-to-report pipeline that streamlines the use of multiple phage miners and generates an exhaustive report. The pipeline is implemented in Nextflow, a widely adopted workflow manager that enables an optimized parallelization of tasks. MetaPhage is designed to enable scalability and reproducibility and offers an installation-free, dependency-free, and conflict-free workflow execution.

Salivary microbiota composition may discriminate between patients with eosinophilic oesophagitis (EoE) and non-EoE subjects.

BACKGROUND: Data on the role of the microbiome in adult patients with eosinophilic oesophagitis (EoE) are limited. AIMS: To prospectively collect and characterise the salivary, oesophageal and gastric microbiome in patients with EoE, further correlating the findings with disease activity. METHODS: Adult patients with symptoms of oesophageal dysfunction undergoing upper endoscopy were consecutively enrolled. Patients were classified as EoE patients, in case of more than 15 eosinophils per high-power field, or non-EoE controls, in case of lack of eosinophilic infiltration. Before and during endoscopy, saliva, oesophageal and gastric fundus biopsies were collected. Microbiota assessment was performed by 16 s rRNA analysis. A Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was implemented to identify biomarkers. RESULTS: Saliva samples were collected from 29 EoE patients and 20 non-EoE controls;, biopsies from 25 EoE and 5 non-EoE controls. In saliva samples, 23 Amplicon Sequence Variants (ASVs) were positively associated with EoE and 27 ASVs with controls, making it possible to discriminate between EoE and non-EoE patients with a classification error (CE) of 24%. In a validation cohort, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of this model were 78.6%, 80%, 75%, 80% and 60%, respectively. Moreover, the analysis of oesophageal microbiota samples observed a clear microbial pattern able to discriminate between active and inactive EoE (CE = 8%). CONCLUSION: Our preliminary data suggest that salivary metabarcoding analysis in combination with machine learning approaches could become a valid, cheap, non-invasive test to segregate between EoE and non-EoE patients.