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proteoQC is an R package for proteomics data quality assessment. This package creates an HTML format QC report for MS/MS-based proteomics data. The report is intended to allow the user to quickly assess the quality of proteomics data. The official page is the Bioconductor landing page (release or devel versions).


To install proteoQC

```{r install, eval = FALSE} library("BiocInstaller") biocLite("proteoQC")

If you need the github version (not recommended unless you know what
you are doing)

```{r installgh, eval = FALSE}


To cite the proteoQC package in publications, please use:

Wen B and Gatto L (2017). proteoQC: An R package for proteomics data quality control. R package version 1.15.0,

List of citations

proteoQC has been cited in the following manuscripts: 1. Gatto, Laurent, et al. "Visualization of proteomics data using R and Bioconductor." Proteomics 15.8 (2015): 1375-1389. 2. Bittremieux, Wout, et al. "Computational quality control tools for mass spectrometry proteomics." Proteomics 17.3-4 (2017). 3. Samandi, Sondos, et al. "Deep transcriptome annotation enables the discovery and functional characterization of cryptic small proteins." eLife 6 (2017): e27860. 4. Belghit I, Lock E J, Fumière O, et al. Species-Specific Discrimination of Insect Meals for Aquafeeds by Direct Comparison of Tandem Mass Spectra. Animals, 2019, 9(5): 222. 5. Walzer M., Vizcaíno J.A. (2020) Review of Issues and Solutions to Data Analysis Reproducibility and Data Quality in Clinical Proteomics. In: Matthiesen R. (eds) Mass Spectrometry Data Analysis in Proteomics. Methods in Molecular Biology, vol 2051. Humana, New York, NY


Contributions to the package are more than welcome.

wenbostar/proteoQC documentation built on Nov. 20, 2020, 10:15 a.m.