Datasets

Benchmark datasets for WGS analysis of SARS-CoV-2.

Purpose

Technical Outreach and Assistance for States Team (TOAST) developed benchmark datasets for SARS-CoV-2 sequencing which are designed to help users at varying stages of building sequencing capacity. It consists of six datasets summarized in the table below, each chosen to represent a different use case.

Summary Table

| Dataset | Name | Description | Intended Use |tsv name | Primer Set | Reference | ------------- | ------------- | ------------- | ------------- | ------------- | ----------| ------------- | | 1 | Boston Outbreak | A cohort of 63 samples from a real outbreak with three introductions, Illumina platform, metagenomic approach | To understand the features of virus transmission during real outbreak setting, metagenomic sequencing | sars-cov-2-SNF-A.tsv | NA |Lemieux et al. | 2 | CoronaHiT rapid | A cohort of 39 samples prepared by different wet-lab approaches and sequenced at two platforms (Illumina vs MinIon) with MinIon running for 18 hrs, amplicon-based approach | To verify that a bioinformatics pipeline finds virtually no differences between platforms of the same genome, outbreak setting |sars-cov-2-coronahit-rapid.tsv | ARTICV3|Baker et al. | 3 | CoronaHiT routine | A cohort of 69 samples prepared by different wet-lab approaches and sequenced at two platforms (Illumina vs MinIon) with MinIon running for 30 hrs, amplicon-based approach | To verify that a bioinformatics pipeline finds virtually no differences between platforms of the same genome, routinue surveillance |sars-cov-2-coronahit-routine.tsv | ARTICV3|Baker et al. | 4 | VOI/VOC lineages | A cohort of 16 samples from 10 representative CDC defined VOI/VOC lineages as of 06/15/2021, Illumina platform, amplicon-based approach | To benchmark lineage-calling bioinformatics pipeline especially for VOI/VOCs, bioinformatics pipeline validation |sars-cov-2-voivoc.tsv | ARTICV3 | This study | 5 | Non-VOI/VOC lineages | A cohort of 39 samples from representative non VOI/VOC lineages as of 05/30/2021, Illumina platform, amplicon-based approach | To benchmark lineage-calling pipeline nonspecific to VOI/VOCs, bioinformatics pipeline validation |sars-cov-2-nonvoivoc.tsv | ARTICV3: 34, ARTICV1: 2, RandomPrimer-SSIVNexteraXT: 2, NA: 1|This study | 6 | Failed QC | A cohort of 24 samples failed basic QC metrics, covering 8 possible failure scenarios, Illumina platform, amplicon-based approach | To serve as controls to test bioinformatics quality control cutoffs |sars-cov-2-failedQC.tsv | ARTIC_V3: 5, CDC in house multiplex PCR primers (Paden et al.): 19|This study

Installation & Usage

Other installation methods

Some methods of installation are maintained by the community. Although we do not have direct control over them, we would like to list them for convenience.

Visit INSTALL.md for these methods.

From Source Code

Grab the latest stable release under the releases tab. If you are feeling adventurous, use git clone! Include the scripts directory in your path. For example, if you downloaded this project into your local bin directory:

$ export PATH=$PATH:$HOME/bin/datasets/scripts

Additionally, ensure that you have the NCBI API key. This key associates your edirect requests with your username. Without it, edirect requests might be buggy. After obtaining an NCBI API key, add it to your environment with

export NCBI_API_KEY=unique_api_key_goes_here

where unique_api_key_goes_here is a unique hexadecimal number with characters from 0-9 and a-f. You should also set your email address in the EMAIL environment variable as edirect tries to guess it, which is an error prone process. Add this variable to your environment with

export EMAIL=my@email.address

using your own email address instead of my@email.address.

Dependencies

In addition to the installation above, please install the following.

1. edirect (see section on edirect below)
2. sra-toolkit, built from source: https://github.com/ncbi/sra-tools/wiki/Building-and-Installing-from-Source
3. Perl 5.12.0
4. Make
5. wget - Brew users: brew install wget
6. sha256sum - Linux-based OSs should have this already; Other users should see the relevant installation section below.

Installing edirect

Modified instructions from https://www.ncbi.nlm.nih.gov/books/NBK179288/

sh -c "$(curl -fsSL ftp://ftp.ncbi.nlm.nih.gov/entrez/entrezdirect/install-edirect.sh)"

NOTE: edirect needs an NCBI API key. Instructions can be found at https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities

Installing sha256sum

If you do not have sha256sum (e.g., if you are on MacOS), then try to make the shell function and export it.

function sha256sum() { shasum -a 256 "$@" ; }
export -f sha256sum

This shell function will need to be defined in the current session. To make it permanent for future sessions, add it to $HOME/.bashrc.

Downloading a dataset

To run, you need a dataset in tsv format. Here is the usage statement:

Usage: GenFSGopher.pl -o outdir spreadsheet.dataset.tsv
PARAM        DEFAULT  DESCRIPTION
--outdir     <req'd>  The output directory
--compressed          Compress files after finishing hashsum verification
--format     tsv      The input format. Default: tsv. No other format
                      is accepted at this time.
--layout     onedir   onedir   - Everything goes into one directory
                      byrun    - Each genome run gets its separate directory
                      byformat - Fastq files to one dir, assembly to another, etc
                      cfsan    - Reference and samples in separate directories with
                                 each sample in a separate subdirectory
--shuffled   <NONE>   Output the reads as interleaved instead of individual
                      forward and reverse files.
--norun      <NONE>   Do not run anything; just create a Makefile.
--numcpus    1        How many jobs to run at once. Be careful of disk I/O.
--citation            Print the recommended citation for this script and exit
--version             Print the version and exit
--help                Print the usage statement and die

Using a dataset

There is a field intendedUse which suggests how a particular dataset might be used. For example, Epi-validated outbreak datasets might be used with a SNP-based or MLST-based workflow. As the number of different values for intendedUse increases, other use-cases will be available. Otherwise, how you use a dataset is up to you!

Creating your own dataset

To create your own dataset and to make it compatible with the existing script(s) here, please follow these instructions. These instructions are subject to change.

Start by creating a new Excel spreadsheet with only one tab. Please delete any extraneous tabs to avoid confusion. Then view the specification.

Citation

If this project has helped you, please cite both this website and the publication:

Xiaoli L, Hagey JV, et al. "Benchmark datasets for SARS-CoV-2 surveillance bioinformatics." PeerJ 10 (2022): e13821.
DOI: 10.7717/peerj.13821

Notices and Disclaimers

Public Domain

This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.

License

Unless otherwise specified, the repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.

This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version.

This source code in this repository is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the Apache Software License for more details.

You should have received a copy of the Apache Software License along with this program. If not, see http://www.apache.org/licenses/LICENSE-2.0.html

Any source code forked from other open source projects will inherit its license.

Privacy

This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the Disclaimer and Code of Conduct.For more information about CDC's privacy policy, please visit http://www.cdc.gov/other/privacy.html.

Contributing

Anyone is encouraged to contribute to the repository by forking and submitting a pull request. (If you are new to GitHub, you might start with a basic tutorial.) By contributing to this project, you grant a world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license to all users under the terms of the Apache Software License v2 or later.

All comments, messages, pull requests, and other submissions received through CDC including this GitHub page may be subject to applicable federal law, including but not limited to the Federal Records Act, and may be archived. Learn more at http://www.cdc.gov/other/privacy.html.

More specific instructions can be found at CONTRIBUTING.md.

Records

This repository is not a source of government records, but is a copy to increase collaboration and collaborative potential. All government records will be published through the CDC web site.