We’ve added to ERGO’s rich suite of tools and workflows for 16S amplicon sequencing studies to include functional metagenome prediction utilizing Picrust2.

Do the differences you see on the ordination plot represent a significant difference? This is one of the key questions researchers ask themselves. One way to determine significance is permutational multivariate analysis of variance (PERMANOVA), a statistical test commonly used in ecology settings [1]. ERGO uses the 'adonis' function in the R package vegan which provides functions for the analysis of ecology [2].

Non-metric dimensional scaling attempts to closely represent pairwise dissimilarity between samples. It is a robust unconstrained ordination method that uses rank orders commonly used in ecology studies. Unlike many other ordination techniques NMDS iterates to find a solution that fits with an optimal stress value to the number of chosen dimensions. ERGO gives you a simple interface to choose the number of attempts, dimensions, along with an option to specify a previous best stress.

Data mining and visualization just got easier in ERGO’s newest microbiome analysis update.

We’ve added to ERGO’s rich visualizations with a new plot type: the violin plot. A violin plot is a way to visualize an underlying distribution of values (in our case, log fold change). Similar in utility to a box plot, a violin plot has a few advantages. Instead of just representing the median, quartiles, minimum and maximum, a violin plot uses a kernel density estimation algorithm to visualize the distribution of underlying points.

ERGO’s [Overbeek et. all 2003] rich suite of tools and workflows to examine amplicon and metagenome sequencing now includes Linear Discriminate Analysis using LEfSe [Segata et. al 2010]. In ERGO, LEfSe (Linear discriminate analysis Effect Size) enables researchers to discover biomarkers that most likely explain the differences between selected experimental groups.

Quickly identify significantly up and down regulated pathway and functional categories using ERGO’s gene set enrichment analysis (GSEA). Gene set enrichment analysis detects concordant movement of gene sets between two phenotypes, usually experimental conditions. In this way the researcher can identify the statistically significant pathways and infer the effect the experimental conditions had upon their organism of study.

Easily analyze your metagenome projects using ERGO by dragging and dropping your BIOM file into ERGO.