CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets.

BACKGROUND: Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration. RESULTS: In this study, we have developed a Java-based statistical framework ( https://github.com/KociOrges/cviewer ) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case-control relationships. CONCLUSIONS: CViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn's disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential. Video Abstract.

An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights.

With an unprecedented growth in the biomedical literature, keeping up to date with the new developments presents an immense challenge. Publications are often studied in isolation of the established literature, with interpretation being subjective and often introducing human bias. With ontology-driven annotation of biomedical data gaining popularity in recent years and online databases offering metatags with rich textual information, it is now possible to automatically text-mine ontological terms and complement the laborious task of manual management, interpretation, and analysis of the accumulated literature with downstream statistical analysis. In this paper, we have formulated an automated workflow through which we have identified ontological information, including nutrition-related terms in PubMed abstracts (from 1991 to 2016) for two main types of Inflammatory Bowel Diseases: Crohn's Disease and Ulcerative Colitis; and two other gastrointestinal (GI) diseases, namely, Coeliac Disease and Irritable Bowel Syndrome. Our analysis reveals unique clustering patterns as well as spatial and temporal trends inherent to the considered GI diseases in terms of literature that has been accumulated so far. Although automated interpretation cannot replace human judgement, the developed workflow shows promising results and can be a useful tool in systematic literature reviews. The workflow is available at https://github.com/KociOrges/pytag.