ERGO now supports the integration of data from your metabolomics experiments. ERGO identifies the top most interesting pathways (KEGG and BioCyc) for your set of metabolites, taking into account selected genome context. Getting started is as easy as importing a table.
Metabolomics concentrations are displayed on pathways and can be integrated with gene expression data for additional insight.
We’re always aiming to improve the breadth and depth of data we provide to our clients. This is why we’ve integrated SRI’s MetaCyc* and EcoCyc* databases into ERGO. This adds over 3000 new pathways to ERGO. BioCyc functional assignments are computed for every genome in the ERGO database.
The pathway diagrams are drawn using a subset of the System Biology Graphical Notation. The pathway diagrams are completely interactive and can be customized and rearranged to your preference. Each pathway has a summary description and background so that the researcher can completely understand its context.
RNA-Seq and other expression data can be interactively projected onto the BioCyc pathways. BioCyc is also one of the ontologies available for Gene Set Enrichment Analysis (GSEA) and on differential gene expression plots.
ERGO’s differential analysis and GSEA plots can help you quickly identify which pathways are changing under your experimental conditions.
*BioCyc TM pathway/genome databases under license from SRI International. (https://www.biocyc.org).
“Feature Lists” are a list of genome features in ERGO. These could be coding sequences, genes, or other genomic features. Lists can contain features from any genome that is accessible to your account.
Feature Lists can be created in several places in ERGO:
Feature Dialogs
Feature Pages
Orthologs Table
Differential Gene Expression Experiments
Protein Properties Search
Directly by pasting data
For example, on an ortholog table for a feature, you can create a feature list of all the orthologs of a gene.
You can view and manage your feature lists in your settings, which you can access by clicking the “gear” icon at the top right. Then scroll down to “Feature Lists”.
A list of Feature LIsts accessible through your settings
Feature lists can be used several places in ERGO including:
Building phylogenetic trees
Exporting specific features.
Highlight in Volcano plots or other differential gene expression views
Expression Clustering
and more coming soon…
For example, we can use the feature list we created above, bacterial orthologs of Escherichia coli K-12 MG1655 gene gyrB to create a phylogenetic tree of those orthologs. We can do this by adding a new workflow to a project, by clicking “Create or Add Workflow”, then “Start New Workflow”, then selecting “Phylogentic Tree”.
In our workflow parameters, select “FROM” to be “feature_list”, then select our gyrB feature list. Then click “Save” and “Start Workflow”. After the workflow is completed you’ll be presented with an multiple sequence alignment and phylogenetic tree of all the gyrB orthologs.
You’ve requested and we’ve delivered! ERGO now has fast bulk downloads from projects and workflows. For the moment this feature is only available on up-to-date chromium browsers such as Google Chrome or Microsoft Edge.
Here is how it works, first on any project or workflow, click on the “Download … Files” button.
This will add all of those files to the download queue. Each file can be removed from the queue by clicking the toggle on the left.
Clicking on “Start Download” starts the download from ERGO using the number of simultaneous downloads you’ve selected. You can stop your download at any time by clicking “Stop Download”. Click “Start Download” will start the download where you left off.
Transferring files can also be managed using our API. Please enquire to learn more about this feature, ERGO’s API, or any other questions.
We’ve added to ERGO’s rich suite of tools and workflows for 16S amplicon sequencing studies to include functional metagenome prediction utilizing Picrust2.
Read MoreDo 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].
Read MoreNon-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.
Read MoreData mining and visualization just got easier in ERGO’s newest microbiome analysis update.
Read MoreWe’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.
Read MoreERGO’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.
Read MoreIgenbio’s developers have spent this summer creating many ‘quality of life’ improvements to ERGO’s user interfaces. Today I’ll highlight a few changes:
New color palette options available under chart customizations
General Changes
Most charts now support four new color modes, including two that are colorblind friendly palettes, such as the default color scheme.
Color palettes can be selected from the “Customize” menu below the chart.
In Expression Analysis:
More detail in error dialogs. For example, when you are running a DESeq2 analysis, if there is a problem (not enough replicates) you’ll get a more detailed message that could aid you in correcting the issue.
Changed the truncation of long titles. Now the analysis tab will auto expand to display the entire title. This will make it easier to differentiate between open analysises.
When creating new analysis, there is now an option to add/remove samples or conditions. This will allow you to quickly create customized analysis.
Easily remove conditions and samples to create the custom analysis that you want
Differential Analysis
New Column: ‘Category’. This contains all of the categories at the current ontological level that this feature belongs to.
Hovering over a row in the DEG table highlights the categories (or feature in Volcano plot) in the chart that the feature belongs to.
New table options to set the precision and number display preferences for fold change, p-value, q-value, and others.
More meta feature data is now indexed when searching the table, including KEGG, COG, Pfam, and pathways.
New option to sort the plot by value (smallest to largest)
New plot options, such as Min Fold Change, Max Fold Change, Median Fold Change, Count (of genes), Mean -log10 p-value, Mean -log10 q-value. This will enable you to quickly identify which category has the most significance.
New option option to plot two different series - on for all changes that have positive fold change and another for those with negative fold change. This will make it more obvious of the apparent direction of the category as a whole as averaging the fold change had the effect of making the category look as though at no change at all.
New filter option for filtering by absolute value of fold change. This way you can filter the table (and subsequent plots) by features with a fold change in either direction.
New “Bar” plot.
Gene Set Enrichment Analysis (GSEA)
Added condition toggle for heatmaps. Under “More Chart Options” there is an option to quickly toggle on/off a condition to be displayed on the heatmap.
KEGG Analysis results can now be projected onto KEGG Pathways.
Demonstration of new plot option to have two separate series - one representing the mean fold change of all positive fold changes and another representing the mean of all negative fold changes.
In Read QC Analysis
Added details of over-represented sequences, which could be an indication of adapters or other sequences that need to be filtered
Some speed improvements
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.
Read More
ERGO now supports viewing GFF (Generic Feature Format) files in the Genome Browser. ERGO previously supported importing GFF3 and GTF as new genomes for licensees of ERGO Enterprise Edition.
You can now drag and drop your GFF files directly into ERGO. ERGO then sorts and indexes your GFF file for quick access. After you associate the GFF file with your genome of choice, you will be able to add the GFF file as a new track in the Genome Browser.
Log into ERGO, then click the upload icon on the top toolbar. This will reveal your current uploads. Drag and drop your GFF file into the box or click on the “+” icon to browse and select your files. Multiple files are accepted.
After your file has uploaded, click on the box next to the name to edit the file. If your file does not end in “.gff” you may need to change the “Type” to “gff - Genome Feature Format Version 3 (gff)”. Click on “+Genome” to select a genome for this GFF file.
After your GFF has been uploaded and the file type and genome has been set, you can view it on ERGO’s Genome Browser. The GFF file will appear when your click on “>Tracks” on the Genome Browser for the selected genome.
Easily analyze your metagenome projects using ERGO by dragging and dropping your BIOM file into ERGO.
Read More
When you add annotated BCF or VCF files to ERGO, it automatically computes variant metrics, including the features that have been affected. Easily find which genomic features are affected by variation by clicking on the “Affected Features” tab. From here you can search for your feature or function of interest and see which features have been affected. Clicking on the arrow reveals more information about the feature, including which pathways it is in and which variants have affected this feature.
Hey ERGO users! You can now filter through the large lists of variants according to their fields and focus in on the ones that are most important. The variant filtering feature can be found under the “Variant List” tab in the variant analysis workflow. By simply clicking on “Add Filter”, located next to “Settings”, you can choose your filter(s) and focus in on specific contigs, frame shifts, specific annotations and attributes, and much more.
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