lifescience/ngb repository overview

⁠Documentation

• Latest guides are available at NGB Docs⁠
• Make sure that you are using latest Google Chrome to browse NGB data

⁠What is NGB

New Genome Browser (NGB) is a Web client-server tool that has been developed with the several key distinctive features in mind:

• Visualization of Structural Variations (SVs) and their supporting reads
• Performance and scalability while working with big/cloud genome data
• Integration with various data sources, including ENSEMBL, UniPROT and other internal/external databases
• User experience that is based on a set of useful features like hotkeys, variation tables, docking widgets, etc.
• Web 3D molecular viewer integrated

We have done our best to make those features at the highest possible level and thus make NGB one the best web-based genome browser The community lead by EPAM intends to develop NGB extending it functionality and improving user experience. Your suggestions and comments are welcome.

We believe that NGB (being a namesake of a Neuroglobin (NGB) gene⁠, coding a protein that is involved in oxygen transport in the brain) will help researchers and clinicians to discover the valuable insights in the huge volumes of genomic data.

⁠How to use this image

There are two version of NGB in the repository:

• ngb:latest - a "core" version - contains image of NGB without any data in it, only binaries
• ngb:latest-demo - a "demo" version - contains demo data set, which does not require any data registration, you need only to run an image

⁠Running demo version

Warning: a demo version could take up to 2Gb of the disk space (FASTA sequence, genes annotations, BAM, VCFs) For a demo version run the following command

$ docker run -p 8080:8080 -d --name ngbcore lifescience/ngb:latest-demo

You can go to http://localhost:8080/catgenome⁠ or http://ip-of-the-host:8080/catgenome⁠ in a browser and view demo datasets (Sample 1 and Sample 2), which contain Structural Variations

⁠Running core image

For a core version replace <YOUR_NGS_DATA_FOLDER> placeholder with a real path to a folder with NGS data, and then run command

$ docker run -p 8080:8080 -d --name ngbcore -v <YOUR_NGS_DATA_FOLDER>:/ngs lifescience/ngb

This will create and start the container in a background mode and map port 8080 of the container to port 8080 of the host, then mount <YOUR_NGS_DATA_FOLDER> of the host to /ngs folder of the container and at last - make container accessible by name ngbcore

You can go to http://localhost:8080/catgenome⁠ or http://ip-of-the-host:8080/catgenome⁠ in a browser (Chrome) and verify that server started successfully (you should see empty list of datasets)

⁠Registering data

To register your own data you should attach to a running container

$ docker exec -it ngbcore /bin/bash

This will put you inside a container's console and make ngb command available First of all you should register reference (genome data), using a mounted folder /ngs. NGB accepts FASTA files for reference sequence

# ngb reg_ref /ngs/<PATH_TO_FASTA> -n my_genome -t

According to FASTA size you should wait several minutes.

To make NGS data available via NGB, you should create a DATASET, that is used to group linked files You can register files and then add them to a dataset

Register file

# ngb reg_file my_genome /ngs/<PATH_TO_FILE> -n my_file1 -t

Note that you should provide reference name (my_genome in this case), also -n (name) key is optional, if it is not specified - original file name will be used

Create dataset and add file(s) to it

# ngb reg_dataset my_genome my_sample my_file1

Or you can create dataset and register files simultaneously

# ngb reg_dataset my_genome my_sample /ngs/<PATH_TO_FILE> /ngs/<PATH_TO_FILE2>

Note that when registering a dataset, you should specify a genome name, to which files correspond

After all you can leave container's console using

# exit

NGB container will continue running in a background. When datasets are created - you can immediately browse NGS data.

⁠Demo data description

ngb:latest-demo container is built to show some basic features of NGB. It uses mostly shrinked data to minimize a container size

⁠Points of interest

• SV_Sample1 dataset: ALK-EML4 fusion⁠

• SV_Sample2 dataset: ROS1-SLC34A2 fusion⁠

• FGFR3-TACC-Fusion-Sample dataset: FGFR3-TACC3 fusion⁠

• PIK3CA-E545K-Sample dataset: E545K SNV⁠

• Fruitfly dataset: LIMK 1 SNV-INDELS⁠