sneumann

#!/bin/bash

chmo2definedterm () {
  CURIE=`echo "$1" | sed -e 's/.*\(CHMO:[0-9]*\).*/\1/'`
  wget -q -O- "https://service.tib.eu/ts4tib/api/ontologies/chmo/terms?obo_id=$CURIE" |\
  jq '{ measurementTechnique: { "@type": "DefinedTerm", "@id": ._embedded.terms[0].iri, "termCode": ._embedded.terms[0].obo_id, "name": ._embedded.terms[0].label, "url": ("https://terminology.nfdi4chem.de/ts/ontologies/"+._embedded.terms[0].ontology_name+"/terms?iri="+._embedded.terms[0].iri), "inDefinedTermSet": { "@type": "DefinedTermSet", "@id": ._embedded.terms[0].ontology_iri, "name": ._embedded.terms[0].ontology_name, } } }' |\
    grep -v '^[{}]$'
  echo ","
}

sneumann

#!/bin/bash

BASEURL=https://www.chemotion-repository.net/api/v1/public/metadata/publications
LIMIT=500

MAXPARALLEL=25
MAXRECORDS=99999999
#MAXRECORDS=2000

# There are currently ~6000 Samples, ~25000 Containers and ~5000 Reactions

sneumann

#!/bin/bash

BASEURL=https://www.chemotion-repository.net/api/v1/public/metadata/publications
LIMIT=500

MAXPARALLEL=25
MAXRECORDS=99999999
MAXRECORDS=2000

# There are currently ~6000 Samples, ~25000 Containers and ~5000 Reactions

sneumann

{
    "@context": "https://schema.org",
    "@type": "Study",
    "@id": "https://dx.doi.org/10.14272/reaction/SA-FUHFF-UHFFFADPSC-UWHQATUHQV-UHFFFADPSC-NUHFF-NUHFF-NUHFF-ZZZ",
    "identifier": "CCR-33542",
    "dct:conformsTo": {
        "@type": "CreativeWork",
        "@id": "https://bioschemas.org/types/Study/0.3-DRAFT"
    },
    "url": "https://www.chemotion-repository.net/inchikey/reaction/SA-FUHFF-UHFFFADPSC-UWHQATUHQV-UHFFFADPSC-NUHFF-NUHFF-NUHFF-ZZZ",

sneumann

#!/usr/bin/env bash

export BACKEND=http://localhost:8081/oai-backend

jq -c '.[]' DataDump-IPB.jsonld | \
while read i; do

IDENTIFIER=`echo "$i" | jq '.identifier'`

(

sneumann

## 1) Get paginated list of Molecules from Chemotion
## 2) extract DOI and internal chemotion ID for each molecule with `jq`
## 3) Query each molecule and convert with `jq` to (bio)schemas
## 4) Embedd the JSON in a CDATA in a minimalistic XML
## 5) Pipe the XML to the FIZ-OAI backend running at $BACKEND, e.g. localhost

export BACKEND=http://localhost:8081/oai-backend

for I in 1 2 3 4 5 6 7 8 9 10; do
wget -O- "https://www.chemotion-repository.net/api/v1/public/molecules.json?page=$I&per_page=100" |\

sneumann

library(rmzTabM)       ## https://lifs-tools.github.io/rmzTab-m/ 
library(metabolighteR) ## https://aberhrml.github.io/metabolighteR/

convertMzTab2MAF <- function(mzTabfile, MAFfile) {

  mzTabTable <- readMzTab(mzTabfile)

  ## To turn these tables into objects, use the R6 class constructor method `new()`:
  mzTabObject <- MzTab$new()$fromDataFrame(mzTabTable)
  metadata <- mzTabObject$metadata

sneumann

#!/bin/bash
wget -O- https://www.chemotion-repository.net/api/v1/public/molecule.json?id=6338 |\
jq "$(cat <<EOF
{ "@context": "https://schema.org/", 
  "@type": "MolecularEntity", 
  "dct:conformsTo": { "http://purl.org/dc/terms/conformsTo": {
    "@id": "https://bioschemas.org/profiles/MolecularEntity/0.5-RELEASE", 
    "@type": "CreativeWork" }
  }, 
  "@id": .molecule.tag.taggable_data.chemotion.doi, 

sneumann

install.packages("microbenchmark")
install.packages("plyr")

install.packages("rcdk")
install.packages("OrgMassSpecR")
install.packages("CHNOSZ")

BiocManager::install("Rdisop")
BiocManager::install("MetaboCoreUtils")

sneumann

import pykube
from os import environ as os_environ

# pykube imports:
try:
    from pykube.config import KubeConfig
    from pykube.http import HTTPClient
    from pykube.objects import (
        Job,
        Pod