Noble-Lab/crema

RESOURCE

Abstract

Confidence Estimation for Mass Spectrometry Proteomics. Crema is an open-source Python tool that estimates the false discovery rate (FDR) of a set of peptide-spectrum matches that result from proteomics data analysis. Crema implements standard FDR estimation methods that are widely used within the field. This package is meant to be simple, easy to use, stand-along, and easy to maintain.
Developers:
See, Donavan [1] Lin, Andy [2] Fondrie, Will [3] Just, Seth [4] Noble, William [1]
  1. University of Washington
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  3. Talus Bioscience
  4. Seer, Inc
Release Date:
2023-05-22
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Licenses:
Apache License 2.0
Sponsoring Org.:
Code ID:
107336
Site Accession Number:
Battelle IPID 32721-E
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Country of Origin:
United States

RESOURCE

Citation Formats

See, Donavan, Lin, Andy, Fondrie, Will, Just, Seth, and Noble, William. Noble-Lab/crema. Computer Software. https://github.com/Noble-Lab/crema. USDOE. 22 May. 2023. Web. doi:10.11578/dc.20230522.3.
See, Donavan, Lin, Andy, Fondrie, Will, Just, Seth, & Noble, William. (2023, May 22). Noble-Lab/crema. [Computer software]. https://github.com/Noble-Lab/crema. https://doi.org/10.11578/dc.20230522.3.
See, Donavan, Lin, Andy, Fondrie, Will, Just, Seth, and Noble, William. "Noble-Lab/crema." Computer software. May 22, 2023. https://github.com/Noble-Lab/crema. https://doi.org/10.11578/dc.20230522.3.
@misc{ doecode_107336,
title = {Noble-Lab/crema},
author = {See, Donavan and Lin, Andy and Fondrie, Will and Just, Seth and Noble, William},
abstractNote = {Confidence Estimation for Mass Spectrometry Proteomics. Crema is an open-source Python tool that estimates the false discovery rate (FDR) of a set of peptide-spectrum matches that result from proteomics data analysis. Crema implements standard FDR estimation methods that are widely used within the field. This package is meant to be simple, easy to use, stand-along, and easy to maintain.},
doi = {10.11578/dc.20230522.3},
url = {https://doi.org/10.11578/dc.20230522.3},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230522.3}},
year = {2023},
month = {may}
}