Toy and benchmark datasets
We wish to put together a set of data that, after assembly, the results can be compared back. These results can include actual assemblies or profiling such as MLST. There are several advantages including
- Workshops and trainings
- Proficiency testing
- Certifications
- Bioinformatics workflow development
- Baseline comparison between bioinformatics pipelines
We wish to put together a set of data that is fast to assemble. There are several advantages including
- Fast to test new bioinformatics workflows
- Fast to teach new people ONT assembly
- Fast to teach people bioinformatics workflows in a workshop setting
This first table lists datasets curated from the Enteric Diseases Laboratory Branch at CDC
| Dataset | Name | Description | Intended Use | tsv name | Reference |
|---|---|---|---|---|---|
| 1 | Staphylococcus aureus | hybrid Nanopore R9.4.1 and Illumina near-reference quality assembled Staphylococcus aureus whole genomes isolated from sinus swabs from Chronic Rhinosinusitis Patients, along with their plasmids. | Fast assembly of bacterial genomes with AMR | toy-bacteria-saureus.tsv | PRJNA914892 |
| 2 | Salmonella AMR | A set of Salmonella enterica genomes sequenced by ONT from three different publications. | Assembly and then confirmation of AMR results | Salmonella-AMR.tsv | PMID36036604, PMID35727013, PMID35416692 |
| 3 | Campylobacter - PulseNet | A set of Campylobacter outbreaks with allele calls from BioNumerics | Test whole genome MLST caller; outbreak clustering | campylobacter-pulsenet.tsv | PMID37133905 |
| 4 | Metagenomics - two outbreaks | A set of metagenomic reads belonging to either an Alabama outbreak or a Colorado outbreak in the US. | Test pipeline on metagenomics outbreak datasets for clustering or pathogen detection | coal-metagenomics.tsv | PMID27881416 |
| 5 | Salmonella - PulseNet | A set of Salmonella outbreaks with allele calls from BioNumerics | Test whole genome MLST caller; outbreak clustering | salm-cgMLST.tsv | PMID37808298 |
| 6 | Escherichia - PulseNet | A set of Escherichia and Shigella outbreaks with allele calls from BioNumerics | Test whole genome MLST caller; outbreak clustering | stec-outbreak.tsv |
Collaboration on the Genomics for Food and Feed Safety (Gen-FS)
If using these datasets, please cite Timme et al 2017.
| Dataset | Name | Description | Intended Use | tsv name | Reference |
|---|---|---|---|---|---|
| 1 | Stone Fruit Food recall | An outbreak of L. monocytogenes | Outbreak analysis | Listeria_monocytogenes_1408MLGX6-3WGS.tsv | PMID27694232 |
| 2 | Spicy Tuna outbreak | An outbreak of S. enterica | Outbreak analysis | Salmonella_enterica_1203NYJAP-1.tsv | PMID25995194 |
| 3 | Simulated outbreak | A simulated outbreak of S. enterica | Outbreak analysis | Salmonella_enterica_1203NYJAP-1.simulated.tsv | Timme et al 2017 |
| 4 | Raw Milk outbreak | C. jejuni outbreak | Outbreak analysis | Campylobacter_jejuni_0810PADBR-1.tsv | http://www.outbreakdatabase.com/details/hendricks-farm-and-dairy-raw-milk-2008 |
| 5 | Sprouts Outbreak | E. coli outbreak | Outbreak analysis | Escherichia_coli_1405WAEXK-1.tsv | http://www.cdc.gov/ecoli/2014/o121-05-14/index.html |
From https://github.com/CDCgov/datasets-sars-cov-2/
If you use these datasets, please cite Xiaoli et al.
| 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 | ARTIC_V3 | 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-a.tsv, sars-cov-2-coronahit-routine-b.tsv | ARTIC_V3 | 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 | ARTIC_V3 | Xiaoli et al |
| 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 | ARTIC_V3: 34, ARTIC_V1: 2, RandomPrimer-SSIV_NexteraXT: 2, NA: 1 | Xiaoli et al |
| 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 | Xiaoli et al |
Global Microbial Identifier
| Dataset | Name | Description | Intended Use | tsv name | Reference |
|---|---|---|---|---|---|
| 1 | Ahrenfeldt E. coli | An evolution experiment | phylogenomic pipeline validation | e.coli-Ahrenfeldt-dataset.tsv | PMC5217230 |
| 2 | Legionella outbreak | Philadelphia 1976 Legionaire's outbreak | Outbreak dataset | PA_76_benchMarkInfo.tsv | DOI/10.1371 |
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.
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 protected]
using your own email address instead of [email protected].
In addition to the installation above, please install the following.
- edirect (see section on edirect below)
- sra-toolkit, built from source: https://github.com/ncbi/sra-tools/wiki/Building-and-Installing-from-Source
- Perl 5.12.0
- Make
- wget - Brew users:
brew install wget - sha256sum - Linux-based OSs should have this already; Other users should see the relevant installation section below.
Modified instructions from https://www.ncbi.nlm.nih.gov/books/NBK179288/
sh -c "$(curl -fsSL ftp://ftp.ncbi.nlm.nih.gov/entrez/entrezdirect/install-edirect.sh)"
NOTE: edirect needs an NCBI API key. Instructions can be found at https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities
If you do not have sha256sum (e.g., if you are on MacOS), then try to make the shell function and export it.
function sha256sum() { shasum -a 256 "$@" ; }
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.
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
We are making the new method for downloading a dataset out of Make.
To run this workflow and download the data, make a blank directory and copy over two files like so.
# Change these variables to your liking
OUTDIR="toy-dataset.out"
DATASET="dataset/toy-bacteria-saureus.tsv"
NUMCPUS=4
mkdir $OUTDIR
cp -v $DATASET $OUTDIR/in.tsv
cp -v scripts/Makefile.template $OUTDIR/Makefile
make -j $NUMCPUS -C $OUTDIR allThis is an approximate flowchart of how the Makefile works
graph BT;
prefetch[prefetch.done]
sha256sumLog((sha256sum.log))
sha256sumLogBak((sha256sum.log.bak))
fastqDump{{fastq-dump}}
edirect{{edirect}}
IN([in.tsv])
SRA((SRA file))
R1((R1))
R2((R2))
R1hashsum((R1.sha256))
R2hashsum((R2.sha256))
fasta((fasta\nassembly))
fastahashsum((fasta.sha256))
sha256sum{sha256sum}
tree((tree.dnd))
IN --> |prefetch| SRA
SRA --> prefetch
prefetch -.-> R1
R1 -.-> R2
R1 -.-> R1hashsum
R2 -.-> R2hashsum
IN --> R1hashsum
IN --> R2hashsum
IN --> fastahashsum
IN --> tree
SRA --> fastqDump
fastqDump --> R1
fastqDump --> R2
edirect --> fasta
fasta -.-> fastahashsum
fastahashsum --> sha256sum
R1hashsum --> sha256sum
R2hashsum --> sha256sum
sha256sum --> |success| sha256sumLog
sha256sum --> |fail| sha256sumLogBak
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!
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.
If this project has helped you, please cite both this website and the relevant study(ies) in the table(s) above.
The original publication can be found in
Timme, Ruth E., et al. "Benchmark datasets for phylogenomic pipeline validation, applications for foodborne pathogen surveillance." PeerJ 5 (2017): e3893.
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