Metagenome-wide association studies software tools | Shotgun metagenomic sequencing data analysis
Metagenome-wide association studies (MWAS) have enabled the high-resolution investigation of associations between the human microbiome and several complex diseases, including type 2 diabetes, obesity, liver cirrhosis, colorectal cancer and rheumatoid arthritis. The associations that can be identified by MWAS are not limited to the identification of taxa that are more or less abundant, as is the case with taxonomic approaches, but additionally include the identification of microbial functions that are enriched or depleted.
Leverages pre-existing databases, annotations and frameworks, along with manual curation. BugBase is an algorithm with a user-friendly graphical user interface (GUI) for analyzing microbiome data developed to provide users with biologically relevant microbiome phenotype predictions at the organism level. This method is also unable to model and predict cell-to-cell heterogeneity within strains due to genetic mutations and horizontal gene transfer. BugBase is available as a desktop app and as a web app.
Allows users to test the mediation effect of the human microbiome. MedTest is a program assisting researchers to detect the structured mediators and can be applied to any genomics data with different structures (e.g., linkage disequilibrium (LD) structure for genetic data). It focuses on identifying mediation effect by using an ensemble of distance measures and can recognize specific taxa or operational taxonomic units (OTUs) accounting for the mediation effect.
Characterizes microbial samples from nucleotide or protein sequences. Traitar provides phenotype classifiers to predict 67 traits related to the use of various substrates as carbon and energy sources, oxygen requirement, morphology, antibiotic susceptibility, proteolysis, and enzymatic activities. The software suggests protein families associated with the presence of particular phenotypes. It may help researchers in microbiology to pinpoint the traits of interest, reducing the amount of wet lab work required.
Provides a method for studying population-scale microbiome. tmap enables the adoption of topological data analysis (TDA) in microbiome data analysis pipelines, thus permitting users to interpret large-scale complex data. It also offers a network-based statistical method for enterotype analysis, driver species identification, and microbiome-wide association of host meta-data.
Generates "policy prescriptions" for microbiome engineering. MDPbiome builds a model suggesting a “prescription” of external perturbations that should be applied to a given microbiome, and will result in its navigation through a subset of healthy or acceptable states, avoiding disease or other undesirable states, for finally reaching a goal state. The software can evaluate multiple microbiome states and enables a variety of different questions to be asked of the same dataset. It can be applied to various temporal metagenomics datasets.
Allows users to discover a family of binary/ternary/ratio (BTR) models with equivalent predictive power in a given model-size range. Predomics is based on a genetic algorithm and supports learning high-quality models. It can perform regression tasks by searching models that correlate with the quantitative variable to predict. Moreover, this tool is specifically designed for learning BTR models from metagenomics data.
Executes exact variance component tests in longitudinal microbiome study. VCmicrobiome.jl is composed of three computationally efficient exact variance components tests (eScore, eLRT and eRLRT). The method utilized by this tool extends previous exact variance component tests to the case when null hypothesis contains more than one variance component. This software suits for longitudinal studies testing overall microbiome effects and cross-sectional studies that identify microbiome associations at fine-grained level.