PeCorA is a package that contains a number of functions to detect discordant peptide quantities in shotgun proteomics sata by Peptide Correlation Analysis. The package also contains published proteomics dataset processed with processing tools such as MaxQuant.

Once installed, load the package by writing in the console

library(PeCorA)

Currently, there are three datasets available in PeCorA.

Data available in the package is loaded into the R session using the load function; for instance, to get a recent large-scale analysis of COVID19 severity from Overmyer et al 2020:

data(peptides_data_filtered)

To get more information about a dataset, see its manual page.

?peptides_data_filtered

PeCorA requires a filename.csv file containing table in long format of peptides, their quantities, and the proteins they belong to. This file must at least contain the following columns (check spelling and letter case):

“Condition” - group labels of the conditions. Can be more than 2 but must be at least 2. “Peptide.Modified.Sequence” - peptide sequence including any modifications “BioReplicate” - numbering for biological replicates “Protein” - protein membership for each peptide

You may need to transform your data into PeCorA-ready format. For example ransform peptides.txt output of MaxQuant into t use function import_LFQ_PeCorA.

The main function of the package is called PeCorA, which fits a linear model with interaction between peptides and biological treatment groups.

If you have any questions or suggestions please contact us:

Maria Dermit : maria.dermit at qmul.ac.uk

Jesse Meyer: jesmeyer at mcw.edu

Please see the original paper