Papers: 10.1007/s11306-015-0879-3

https://doi.org/10.1007/s11306-015-0879-3

Data standards can boost metabolomics research, and if there is a will, there is a way

Cited by: 89
Author(s): Philippe Rocca-Serra, Reza M. Salek, Masanori Arita, Elon Correa, Saravanan Dayalan, Alejandra González-Beltrán, Timothy M. D. Ebbels, Royston Goodacre, Janna Hastings, Kenneth Haug, Albert Koulman, Macha Nikolski, Matej Orešič, Susanna-Assunta Sansone, Daniel Schober, James M. Smith, Christoph Steinbeck, Mark R. Viant, Steffen Neumann
Published: almost 11 years ago

Software Mentions 5

bioconductor: ArrayExpress
Access the ArrayExpress Collection at EMBL-EBI Biostudies and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet
Papers that mentioned: 1,962
Very Likely Science (83)
bioconductor: RMassBank
Workflow to process tandem MS files and build MassBank records
Papers that mentioned: 10
Very Likely Science (90)
bioconductor: xcms
LC-MS and GC-MS Data Analysis
Papers that mentioned: 68
Very Likely Science (100)
pypi: workbench
A scalable framework for security research and development teams.
Papers that mentioned: 139
Very Likely Science (75)
pypi: xcms
calculated extended contact mode score provided the query and template protein-ligand structures
Papers that mentioned: 68
Likely Science (55)