Cargando…
Assessment of label-free quantification and missing value imputation for proteomics in non-human primates
BACKGROUND: Reliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data normalization and imputation. In non-human primate...
Autores principales: | Hamid, Zeeshan, Zimmerman, Kip D., Guillen-Ahlers, Hector, Li, Cun, Nathanielsz, Peter, Cox, Laura A., Olivier, Michael |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9264528/ https://www.ncbi.nlm.nih.gov/pubmed/35804317 http://dx.doi.org/10.1186/s12864-022-08723-1 |
Ejemplares similares
-
Optimization of Imputation Strategies for High-Resolution Gas Chromatography–Mass Spectrometry (HR GC–MS) Metabolomics Data
por: Ampong, Isaac, et al.
Publicado: (2022) -
A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation
por: Välikangas, Tommi, et al.
Publicado: (2017) -
A comparative study of evaluating missing value imputation methods in label-free proteomics
por: Jin, Liang, et al.
Publicado: (2021) -
Moderate maternal nutrient reduction in pregnancy alters fatty acid oxidation and RNA splicing in the nonhuman primate fetal liver
por: Zimmerman, Kip D., et al.
Publicado: (2023) -
Normalization and missing value imputation for label-free LC-MS analysis
por: Karpievitch, Yuliya V, et al.
Publicado: (2012)