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MEGAN / MEtaGenome ANalyzer

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Allows users to taxonomically and functionally explore and analyze large-scale microbiome sequencing data. MEGAN is a comprehensive microbiome analysis toolbox for metagenome, meta-transcriptome, amplicon and from other sources data. Users can perform taxonomic, functional or comparative analysis, map reads to reference sequences, reference-based multiple alignments and reference-guided assembly and integrate their own classifications.


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Performs differential gene expression analysis. DEseq is a method that integrates methodological advances with features to facilitate quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change. The software is suitable for small studies with few replicates as well as for large observational studies. Its heuristics for outlier detection assist in recognizing genes for which the modeling assumptions are unsuitable and so avoids type-I errors caused by these.

edgeR / empirical analysis of DGE in R

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Allows differential expression analysis of digital gene expression data. edgeR implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi likelihood tests. The package and methods are general, and can work on other sources of count data, such as barcoding experiments and peptide counts.

STAMP / Statistical Analysis of Metagenomic Profiles

A graphical software package that provides statistical hypothesis tests and exploratory plots for analysing taxonomic and functional profiles. It supports tests for comparing pairs of samples or samples organized into two or more treatment groups. Effect sizes and confidence intervals are provided to allow critical assessment of the biological relevancy of test results. A user-friendly graphical interface permits easy exploration of statistical results and generation of publication-quality plots.


A package for differential abundance analysis in sparse high-throughput marker gene survey data. metagnomeSeq relies on a normalization technique and a statistical model that accounts for under-sampling: a common feature of large-scale marker gene studies. It provides a way to determine features (Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.

OMiAT / Optimal Microbiome-based Association Test

Provides a method for microbial association. OMiAT offers a data-driven approach which aims to help in discovering significant association signals from multiple underlying association patterns. The package is based on a generalized linear model framework and on score tests. In addition, the software can also be used into a hierarchical multiple testing scheme to detect association between microbes and phenotype of interest in the lowest taxonomic rank.

RADs / Rank Abundance Distributions

Compares communities in many areas of biology. RADs enables novel quantitative approaches that help to understand structures and dynamics of complex generalize communities. The tool can be applied to very complex communities, such as the community of the myriads of microbes in a human gut (gut microbiome), or the diverse set of human immune cells. With this software, it is possible to computationally “normalize” Rank Abundance Distributions so that they can be quantitatively compared across many different communities.

MMiRKAT / Microbiome Regression-Based Kernel Association Test with Multivariate Outcomes

Tests association between multiple continuous outcomes and overall microbiome composition. MMiRKAT directly regresses all outcomes on the microbiome profiles via a semiparametric kernel machine regression framework, which allows for covariate adjustment and evaluates the association via a variance-component score test. It incorporates a correction procedure to correct for the conservativeness of the association test when the sample size is small or moderate. The tool provides a statistically powerful and computationally fast way to test associations between microbiome community composition and multiple outcomes of interest.


Performs powerful false discovery rate (FDR) control for microbiome data, taking into account the prior phylogenetic relationship among bacteria species. StructFDR was implemented in an R package available on the CRAN. The main function ‘TreeFDR’ requires the operational taxonomic unit (OTU) counts, the phylogenetic tree, the testing function, which produces the association p-values and the signs of the effect, and a permutation function, which permutes the data in a user-defined way.


Extends the standard supervised binning with an unsupervised clustering step, which enables quantification of metagenomes at a sub-bin level. HirBin is a method for gene-centric analysis of metagenomics data. It permits to identify changes at a more specific functional level than what is possible by using traditional methods. This method is useful for studying complex metagenomic datasets where it can facilitate the data interpretation and generate results that are more biologically relevant.


Encodes a series of well documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalization steps, alpha- and beta-diversity analysis, taxonomic composition, and many others. Rhea is primarily a straightforward starting point for beginners, but can also be a framework for advanced users who can modify and expand the tool. As the community standards evolve, Rhea will adapt to always represent the current state-of-the-art in microbial profiles analysis in the clear and comprehensive way allowed by the R language.


Detects ‘differentially abundant’ populations between samples and groups in mass cytometry data. Cydar is a computational strategy to perform differentially abundant (DA) analyses of mass cytometry data that does not rely on an initial clustering step. This software allocates cells to hyperspheres, tests for differential abundance of cells between conditions for each hypersphere, and controls the false discovery rate (FDR) across the high-dimensional space. Cydar can be used to robustly detect differentially abundant subpopulations or shifts in marker expression between conditions.


Helps in differential distribution analysis of microbiome sequencing data. MicrobiomeDDA is a robust framework of differential analysis of microbiome data based on a zero-inflated negative binomial (ZINB) regression model. It models over-specification and misspecification. This algorithm is very general and could be used to test different types of hypotheses. A natural extension of this framework is to account for sample correlations in differential distribution analysis.

COMMET / COmpare Multiple METagenomes

Provides a global similarity overview between all datasets of a metagenomic project. COMMET is able to (1) filter reads, given user-defined parameters, (2) compare all-against-all read sets, and (3) output a user-friendly visualization of results. This tool was tested on 28 metagenomes from the integrated microbial genomes & microbiomes (IMG/M) database. It returns all-against-all comparisons results and opens the way for further statistical analysis.

LEfSe / Linear discriminant analysis effect size

Allows high-dimensional biomarker discovery and explanation. LEfSe identifies genomic features (genes, pathways, or taxa) characterizing the differences between two or more biological conditions (or classes). The software supports high-dimensional class comparisons with a particular focus on metagenomic analyses. It enables the characterization of microbial taxa specific to an experimental or environmental condition, the detection of pathways and biological mechanisms over- or under-represented in different communities, and the identification of metagenomic biomarkers in mammalian microbiomes.

mcaGUI / Microbial Community Analysis GUI

Provides an interface to access a set of statistical tools to summarize and analyze microbial community data such as principal component analysis (PCA), cluster analysis and others. mcaGUI is a graphical user interface (GUI) for the R-programming language. With this application, researchers can input aligned and clustered sequence data to create custom abundance tables and perform analyses specific to their needs. It provides a flexible modular platform, expandable to include other statistical tools for microbial community analysis in the future.


Allows comparison of metagenomics and other meta-omics data. MetaComp is a graphical software that incorporates metagenomics, metatranscriptomics, metaproteomics and metabolomics data. The software provides a series of statistical analysis and the visualization for the comparison of functional, physiological and taxonomic signatures in two-, multi- and two-group sample tests. It can automatically select the proper statistical method in two-group sample test. MetaComp can be used in for revealing the relationship between environmental factors and meta-omic samples directly through a nonlinear regression analysis.

RegLRSD / Regularized Low Rank-Sparse Decomposition

Detects biomarkers by using a matrix. RegLRSD models the abundance profiles of relevant and irrelevant microbes as sparse and low-rank matrices, respectively. It constrains the low rank matrix to be smooth in order to integrate the prior knowledge that the abundance profiles of irrelevant bacteria do not exhibit strong variation between different phenotypes in the biomarker detection process. The tool offers a convex formulation of the biomarker detection problem.


Provides a unified interface to read, modify, and write BIOM (Biological Observation Matrix) data. Biojs-io-biom can be readily used as a library by applications that need to handle BIOM data for import or export directly in the browser. This module was developed to enhance the import and export of BIOM data into JavaScript. It is implemented using latest web technologies, well tested and well documented. It provides a unified interface and abstracts from details like version or internal data representation. Therefore, it will facilitate the development of web applications that rely on the BIOM format.


A Python command line tool, which is compatible with the widely adopted BIOM format, for microbial ecologists that implements information-theoretic subset selection methods for biological data formats. Commonly used software for analyzing data from microbial ecology, such as Qiime, requires a Biom file containing the 16S data and a map file contain the meta-data of the samples within the Biom file. However, Fizzy allows users to store the meta-data in the Biom file directly, thus avoiding requirements for both a Biom and map file.


A pipeline for top-k based functional characterization of multiple metagenome samples to infer the major functions as well as their quantitative scores in a comparative metagenomics manner. The pipeline performs the annotation of functions related to expected proteins in the metagenome samples, calculates their enrichment scores based on the reads per kilobase per million reads (RPKM) measure, and predicts the relative abundance of associated functions by a statistical test. The results from single sample analysis are then used to find common and sample-specific major functions.

SIAMCAT / Statistical Inference of Associations between Microbial Communities And host phenoTypes

Aims to identify changes in community composition that are related with environmental factors. SIAMCAT analyses relation between microbial communities and host phenotypes. It supports data pre-processing, statistical association testing, statistical modelling. This tool provides functions for evaluation and interpretation of statistical models, such as cross validation, parameter selection, ROC analysis and diagnostic model plots.


A GUI-based comparative metagenomic analysis application which implements a correlation-based graph layout algorithm that not only facilitates a quick visualization of the differences in the analyzed microbial communities (in terms of their taxonomic composition), but also provides insights into the inherent inter-microbial interactions occurring therein. Notably, this layout algorithm also enables grouping of the metagenomes based on the probable inter-microbial interaction patterns rather than simply comparing abundance values of various taxonomic groups. In addition, the tool implements several interactive GUI-based functionalities that enable users to perform standard comparative analyses across microbiomes.