Brief Protocol for EDGE Bioinformatics: Analyzing Microbial and Metagenomic NGS Data   

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Original research article

A brief version of this protocol appeared in:
Nucleic Acids Research
Jan 2017


Next-generation sequencing (NGS) offers unparalleled resolution for untargeted organism detection and characterization. However, the majority of NGS analysis programs require users to be proficient in programming and command-line interfaces. EDGE bioinformatics was developed to offer scientists with little to no bioinformatics expertise a point-and-click platform for analyzing sequencing data in a rapid and reproducible manner. EDGE (Empowering the Development of Genomics Expertise) v1.0 released in January 2017, is an intuitive web-based bioinformatics platform engineered for the analysis of microbial and metagenomic NGS-based data (Li et al., 2017). The EDGE bioinformatics suite combines vetted publicly available tools, and tracks settings to ensure reliable and reproducible analysis workflows. To execute the EDGE workflow, only raw sequencing reads and a project ID are necessary. Users can access in-house data, or run analyses on samples deposited in Sequence Read Archive. Default settings offer a robust first-glance and are often sufficient for novice users. All analyses are modular; users can easily turn workflows on/off, and modify parameters to cater to project needs. Results are compiled and available for download in a PDF-formatted report containing publication quality figures. We caution that interpreting results still requires in-depth scientific understanding, however report visuals are often informative, even to novice users.

Keywords: Genomics, Bioinformatics, Next-generation sequencing, Metagenomics


EDGE bioinformatics was developed to help biologists rapidly process next-generation sequencing (NGS) data even if they have little to no bioinformatics expertise. EDGE is a highly integrated and interactive web-based platform that is capable of running many of the standard analyses that biologists require for viral, bacterial/archaeal, and metagenomic samples. EDGE provides an intuitive web-based interface for user input, allows users to visualize and interact with selected results, and generates a final detailed PDF report. Results in the form of tables, text files, graphic files, and PDFs, together with the raw output files of executed programs, can all be downloaded. A user management system allows tracking of an individual’s EDGE runs, along with the ability to share, post publicly, delete, or archive their results. Users can explore ongoing data processing within a user-friendly, intuitive web-based environment and interactive results are presented on a sample-by-sample basis. While EDGE was intentionally designed to be as simple as possible for the user, there is still no single ‘tool’ or algorithm that fits all use cases in the bioinformatics field. Our intent is to provide a detailed panoramic view of the user’s sample from various analytical standpoints. The initial release of EDGE in January 2017 provides six analytical workflows: pre-processing (data QC and host removal), assembly and annotation, reference-based analysis, taxonomy classification, phylogenetic analysis, and PCR analysis (validation and design). The latest release (version 1.5) includes several new features: identification of antimicrobial resistance and virulence genes, 16S/18S/fungal ITS analysis using QIIME, metadata collection/storage, and comparative analysis of taxonomic classification of multiple metagenomic samples. EDGE Bioinformatics is an ongoing effort to provide best of breed bioinformatics tools for NGS data analysis. Updates to current modules are continuous and more modules are under development.

Copyright: © 2017 The Authors; exclusive licensee Bio-protocol LLC.
How to cite: Philipson, C., Davenport, K., Voegtly, L., Lo, C., Li, P., Xu, Y., Shakya, M., Cer, R. Z., Bishop-Lilly, K. A., Hamilton, T. and Chain, P. S. (2017). Brief Protocol for EDGE Bioinformatics: Analyzing Microbial and Metagenomic NGS Data. Bio-protocol 7(23): e2622. DOI: 10.21769/BioProtoc.2622.

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