CITATION.cff

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: Optimization of metabolomic data processing using NOREVA
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Jianbo
    family-names: Fu
    affiliation: Zhejiang University
  - given-names: Ying
    family-names: Zhang
    affiliation: Zhejiang University
  - given-names: Yunxia
    family-names: Wang
    affiliation: Zhejiang University
  - given-names: Hongning
    family-names: Zhang
    affiliation: Zhejiang University
    orcid: 'https://orcid.org/0000-0002-7818-7915'
identifiers:
  - type: doi
    value: 10.1038/s41596-021-00636-9
repository-code: 'https://github.com/idrblab/NOREVA'
url: 'https://idrblab.org/noreva/'
abstract: >-
  A typical output of a metabolomic experiment is a peak
  table corresponding to the intensity of measured signals.
  Peak table processing, an essential procedure in
  metabolomics, is characterized by its study dependency and
  combinatorial diversity. While various methods and tools
  have been developed to facilitate metabolomic data
  processing, it is challenging to determine which
  processing workflow will give good performance for a
  specific metabolomic study. NOREVA, an out-of-the-box
  protocol, was therefore developed to meet this challenge.
  First, the peak table is subjected to many processing
  workflows that consist of three to five defined
  calculations in combinatorially determined sequences.
  Second, the results of each workflow are judged against
  objective performance criteria. Third, various benchmarks
  are analyzed to highlight the uniqueness of this newly
  developed protocol in (1) evaluating the processing
  performance based on multiple criteria, (2) optimizing
  data processing by scanning thousands of workflows, and
  (3) allowing data processing for time-course and
  multiclass metabolomics. This protocol is implemented in
  an R package for convenient accessibility and to protect
  users’ data privacy. Preliminary experience in R language
  would facilitate the usage of this protocol, and the
  execution time may vary from several minutes to a couple
  of hours depending on the size of the analyzed data.
keywords:
  - metabolomic
  - data process
license: GPL-3.0
commit: 7136b345eb40e18726d93757522473f7d56d01e9
version: 2.1.1
date-released: '2021-10-14'