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Benchmark datasets for WGS analysis
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README.md Sha256sum instructions Aug 23, 2017

README.md

datasets

Benchmark datasets for WGS analysis.

Installation

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

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/

mkdir -p ~/bin
cd ~/bin
perl -MNet::FTP -e \
  '$ftp = new Net::FTP("ftp.ncbi.nlm.nih.gov", Passive => 1);
   $ftp->login; $ftp->binary;
   $ftp->get("/entrez/entrezdirect/edirect.tar.gz");'
gunzip -c edirect.tar.gz | tar xf -
rm edirect.tar.gz
export PATH=$PATH:$HOME/bin/edirect
./edirect/setup.sh

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.

For the impatient

We have included a script that downloads all datasets, runs the CFSAN SNP Pipeline, infers a phylogeny, and compares the tree against the suggested tree. All example commands are present in the shell script for your manual inspection.

$ bash EXAMPLES/downloadAll.sh

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
--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.

  1. Create a new Excel spreadsheet with only one tab. Please delete any extraneous tabs to avoid confusion.
  2. The first part describes the dataset. This is given as a two-column key/value format. The keys are case-insensitive, but the values are case-sensitive. The order of rows is unimportant.
  3. Organism. Usually genus and species, but there is no hard rule at this time.
  4. Outbreak. This is usually an outbreak code but can be some other descriptor of the dataset.
  5. pmid. Any publications associated with this dataset should be listed as pubmed IDs.
  6. tree. This is a URL to the newick-formatted tree. This tree serves as a guide to future analyses.
  7. source. Where did this dataset come from?
  8. intendedUsge. How do you think others will use this dataset?
  9. Blank row - separates the two parts of the dataset
  10. Header row with these names (case-insensitive): biosample_acc, strain, genbankAssembly, SRArun_acc, outbreak, dataSetName, suggestedReference, sha256sumAssembly, sha256sumRead1, sha256sumRead2
  11. Accessions to the genomes for download. Each row represents a genome and must have the following fields. Use a dash (-) for any missing data.
  12. biosample_acc - The BioSample accession
  13. strain - Its genome name
  14. genbankAssembly - GenBank accession number
  15. SRArun_acc - SRR accession number
  16. outbreak - The name of the outbreak clade. Usually named after an outbreak code. If not part of an important clade, the field can be filled in using 'outgroup'
  17. dataSetName - this should be redundant with the outbreak field in the first part of the spreadsheet
  18. suggestedReference - The suggested reference genome for analysis, e.g., SNP analysis.
  19. sha256sumAssembly - A checksum for the GenBank file
  20. sha256sumRead1 - A checksum for the first read from the SRR accession
  21. sha256sumRead2 - A checksum for the second read from the SRR accession
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