GitHub repo template to promote reproducible clinical metabolomics study

Checklist

• Deposit data to a public repository
• Present metadata clearly
• Share workflow components information with a version control system
• Use open-source and downloadable software
• Use virtual machine or software container
• Document runtime hardware information
• Semantic annotation for workflow components
• Use workflow automation or literate programming

Computational resource information (with examples)

• Public repository used to deposit data: MetaboLights
• Unique identifier of data deposited to a public repository: MTBLS1
• Template used to present metadata in the manuscript: Cell STAR★Methods
• Version control system used to share computational resource information: GitHub
• Used software names along with versions (if applicable) and URL used in the analysis: ProteoWizard-msConvert (https://proteowizard.sourceforge.io/tools.shtml), MS-DIAL v4.46 (http://prime.psc.riken.jp/compms/msdial/main.html),
• URL of shared project file: https://s3.console.aws.amazon.com/s3/buckets/example
• URL of shared virtual machine or software container used for the analysis: https://hub.docker.com/_/python
• Model of runtime CPU: Intel Core i7
• Number of runtime CPU: 6
• Model runtime GPU: NVDIA GeForce GTX 980 Ti
• Number of runtime GPU: 2
• Ontology used for semantic annotation of the analysis workflow: EDAM Ontology
• Tool used for workflow automation or literate programming: Nextflow v20.07.1 (https://www.nextflow.io/index.html)
• Order of running workflow components (specify input and output of each step):
  1. Feed input data (.RAW format) to ProteoWizard-msConvert to convert data from to .mzML format, store the output to data/ folder
  2. Feed input data from last step to Nextflow pipeline by typing the following command, output will be stored to results/ folder:

  nextflow main.nf
  

  3. ...