EDGE bioinformatics

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Empowering the Development of Genomics Expertise

mission

EDGE bioinformatics is intended to help truly democratize the use of Next Generation Sequencing for exploring genomes and metagenomes. Given that bioinformatic analysis is now the rate limiting factor in genomics, we developed EDGE bioinformatics with a user-friendly interface that allows scientists to perform a number of tailored analyses using many cutting-edge tools. This website is available for use with publicly available data, while local stand-alone implementations can accommodate data generated on-site for immediate analysis, eliminating the need to move large datasets.

EDGE Workflow

Close workflow

Features of EDGE

  • No need for high-level bioinformaticists
  • Allow users to address a wide range of use cases including the assembly/annotation and comparison of novel genomes, and the characterization of complex clinical or environmental samples
  • Can present the results of several taxonomy classification tools for easy comparison
  • Focus on accurate and rapid analysis
  • Enables sequencing as a solution in facilities where human-resources, space, bandwidth, and time are limited

Implementation

  • EDGE Bioinformatics is built around a collection of publicly available, open-source software packaged or in-house developed tools/algorithms/scripts that form six mainly independent modules
  • The EDGE bioinformatics web-based graphic user interface is primarily implemented using the JQuery Mobile javascript framework and HTML5 on the client-side, and implements perl CGI using Apache or Python on the server-side
  • The current version of EDGE pipeline has been extensively tested on Linux platforms with Ubuntu 14.04 and CentOS 6/7 operation system and will only work on 64bit Linux environments
  • Due to the involvement of several memory/time consuming steps, we normally recommend computers with at least 16GB memory and 8 CPUs, though we typically use on servers with a minimum of 128GB memory with 16 CPUs

Download & Updates

Please check our LANL-Bioinformatics GitHub site.

Tutorial & Help

The tutorial and guide are hosted in edge.readthedocs.org in (click here for PDF version). User discussion group can be found here.

Input Your Sample

EDGE requires sequence data files in FASTQ format. EDGE allows both paired-end and single-end sequences.

Input Raw Reads

Input from NCBI Sequence Reads Archive(SRA)

Paired-end reads:

file
file Delete

and/or

file Delete

(Internet requried) Input SRA accessions support studies (SRP*/ERP*/DRP*), experiments (SRX*/ERX*/DRX*), samples (SRS*/ERS*/DRS*), runs (SRR*/ERR*/DRR*), or submissions (SRA*/ERA*/DRA*). ex: SRR1553609

| additional options |
Add Paired-end Input Add Single-end Input
file

Your customized parameters can be used again. You can utilize the file selector above to upload a standard config file generated by EDGE bioinformatics.

Batch Project Submission

Run EDGE with Multiple projects using a tools set configuration. Click [Sample Input] to see the example.

or enter your batch sample descriptions here

Choose Processes / Analyses

EDGE provides many modules to do various analyses. You can choose to run or skip a specific process. Parameters/options are provided for most of the analyses. You can click here to turn all on, expand all sections or close all sections.

  1. Quality Trim and Filter
    Run Quality Trim and Filter
    file
  2. Host Removal
    Run Host Removal

    and/or

    file

Bypass assembly and use pre-assembled contigs
file
Assembler

IDBA_UD performs well on isolates as well as metagenomes but it may not work well on very large genomes.

SPAdes performs well on isolates as well as single cell data but it may not work on larger genome, and it takes more computational resource. PacBio CLR and Oxford Nanopore reads are used for gap closure and repeat resolution.

Single Cell Data
file
file
Annotation
Annotation Tool
Specify Kingdom

Please choose the genome type you would like to annotate for Prokka to do genome annotation.

file

Please provide the reference/source annotation (Genbank file), EDGE will use RATT to transfer the annotation from the reference genome. The reference genome must be close relative to the sample.

Given one or multiple reference genome FASTA files, EDGE will turn on the analysis of the reads/contigs mapping to reference and JBrowse reference track generation. Given a reference genome genbank file, EDGE will also turn on variant analysis.

and/or

file Add
Identify Unmapped Reads
Identify Unmapped Contigs

EDGE will try to classify reads and contigs that are unmapped to references by mapping them to NCBI RefSeq database.

  1. Read-based Taxonomy Classification
  2. EDGE will use all reads by default. You can change the behavior to use reads that are unmapped to the reference if Reference-based Analysis is on.

    Always use all reads

    EDGE uses multiple tools for taxonomy classification including GOTTCHA (bacterial & viral databases), MetaPhlAn, MetaPhyler (short read version), Kraken, MetaScope and reads mapping to NCBI RefSeq using BWA.

    Classification Tools
  3. Contig-based Taxonomy Classification
  4. EDGE will map contigs to NCBI genomes and make taxonomy inference to each contigs.

    Contigs Classification

EDGE supports 5 pre-computed databases for SNP phylogeny analysis and two tree builders. FastTree is faster and RAxML is slower but more accurate.

Tree Build Method

or

Select/Add Genomes or SRA Reads: The same species or at least within the same genus are recommended.

file Add

  1. Primer Validation
    Run Primer Validation

    Given a primer file, EDGE will run validation of the primer pair to the reference and/or assembled contigs, as available.

    file
    Maximum Mismatch
  2. Primer Design
    Run Primer Design

    EDGE will design primers based on the assembled contigs.

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Max file size is 5gb. Allowed File types are fastq, fasta and genbank and can be in gzip format. Files will be kept for 7 days.

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EDGE version 1.1