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Metagenomics
Taxonomic and Functional Classification, MetaAssembly and GenePredictions, Comparative Analysis

The Metagenomics Module of OmicsBox allows to combine and integrate all necessary steps for a complete microbiome data analysis in and flexible, and intuitive way. Create custom pipelines for individual analysis strategies.

Quality Control And Assessment

Use FastQC and Trimmomatic to perform the quality control of your samples, to filter reads and to remove low quality bases.

Taxonomic Classification

Identify Bacteria, Archaea and Virus down to strain level with Kraken. Gain insights from rich visualizations and test for differential abundance between samples.

Metagenomic Assembly

Choose between MetaSPAdes and MEGAHIT to assemble large datasets easy and fast in the cloud.

Gene Prediction

Use FragGeneScan for plain reads and Prodigal for assembled data to identify and extract possible genes and proteins.

 

Functional Annotation

Use EggNOG-Mapper and PfamScan to obtain high-throughput functional annotations. Results can be represented and compared visually with GO graphs and charts.

  • All computational-intensive tasks are taken care of in the BioBam Bionformatics Cloud
    • An ordinary computer or laptop is sufficient to perform all tasks described on this page
  • Taxonomic classification with Kraken
    • Main result table, that shows all identified OTUs for each provided sample
    • PDF Report that shows overall input and carried-out analysis information
    • Stacked bar chart to compare samples at specific taxonomic levels.
    • Radial cladogram in form of a Krona chart to study OTU abundances in a sample
    • Rarefaction curves to assess the sequencing depth
    • Chao1 species richness curve to evaluate the species diversity in the whole data-set
    • Principal Coordinates Analysis plot to get an overview and to identify outliers
  • OTU Differential Abundance Testing identifies over and underrepresented OTUs between samples and conditions with the help of edgeR
  • Metagenomic assembly with meta-SPAdes and MEGAHIT
  • Metagenomic gene prediction with FragGeneScan and Prodigal
  • Metagenomic functional analysis with eggNOG-Mapper and PfamScan
  • Metagenomic GO-Slim
  • Comparative analysis of functions with bar charts and GO graphs

Taxonomic Classification

Sort and filter the taxonomic classification results, and extract all reads that belong to selected taxonomic units. Refine your reports with customized distribution charts in various styles.

 

Rich Visualizations

Colorful and interactive charts help you to intuitively explore your data. Explore all samples with their relative species abundances and confidence scores within the complex hierarchies of metagenomic classifications.

Intersample Comparison

Interactive bar charts help understanding inter-sample relations at for example the genus or species level. It shows you a quick overview of taxonomic abundance compositions between different samples.

OTU Differential Abundance Testing

Use edgeR to test OTUs at different levels in different groups against each other to determine differential abundance. In addition to the plain results, a PDF report and a heatmap and other visualizations help to interpret your findings.

Benchmark

Analysis of cyanobacterial bloom in lakes

1 M
Input Reads
1
Predicted Genes
1 %
High-Quality Annotated
1 h
Processing Time

Workflows

Taxonomic Classification

This example workflow shows a basic taxonomic classification of metagenomics data with OmicsBox. The reads are preprocessed (Trimmomatic) and a report is generated (FastQC). Kraken is used to identify and count all different operational taxonomic units (OTUs) for further interpretation.  The spreadsheet-like result can be filtered and organized. The PDF report gives a clean overview of most abundant OTUs at different levels for each sample. Results also include intra- and inter-sample comparison charts. The whole workflow can be started within a few clicks and computationally intensive parts run transparently on the cloud.

Functional Characterization

Functional characterization of metagenomics data is a complex task. The OmicsBox Metagenomics module allows you to design streamlined workflows to easily combine the typically resource-demanding assembly step with gene predictions, as well as high-throughput functional annotation for large metagenomics data-sets. This example workflow shows the combination of MEGAHIT with Prodigal. Fast and comprehensive functional annotation is achieved with the integration of EggNOG-Mapper and PfamScan. Results are presented in form of spreadsheets and can be filtered and visualized with hierachical bar- and graph charts.