The analysis of differential abundance for features (e.g. species or genes) can provide us with a better understanding of microbial communities, thus increasing our comprehension and understanding of the behaviors of microbial communities.
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.
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.
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.
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.
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.
Employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE).
Simplifies quantitative investigation of comparative RNA-seq data. DESeq2 employs shrinkage estimators for dispersion and fold change. It counts the total number of reads that can be uniquely assigned to a gene. It serves for improved gene ranking and visualization, hypothesis tests above and below a threshold, and the regularized logarithm transformation for quality evaluation and clustering of over-dispersed count data. This version of DESeq uses shrinkage estimators for dispersion and fold change to ease quantitative analysis of comparative RNA-seq data.