README.md

In missuse/ragp: Mining for Hydroxyproline rich glycoprotein sequences

ragp - hydroxyproline aware filtering of hydroxyproline rich glycoprotein sequences

ragp is an R package primarily designed for mining and analysis of plant hydroxyproline rich glycoproteins. It incorporates a novel concept with an additional analysis layer where the probability of proline hydroxylation is estimated by a machine learning model. Only proteins predicted to contain hydroxyprolines are further analysed for HRGP characteristic motifs and features. ragp can also be used for protein annotation by obtaining predictions for several protein features based on sequence (secretory signals, transmembrane regions, domains, glycosylphosphatidylinositol attachment sites and disordered regions). Additionally ragp provides tools for visualization of the mentioned attributes.

Short example:

library(ragp)
ids <- c("Q9FLL2", #several uniprot accessions
         "Q9LS14",
         "Q9S7I8",
         "Q9M2Z2",
         "Q9FIN5")

seqs <- unlist(protr::getUniProt(ids)) #download sequences 

p1 <- plot_prot(seqs, #plot sequence features
                ids,
                hyp = FALSE, #do not plot hydroxyprolines
                ag = FALSE, #do not plot ag spans
                domain = "hmm") #annotate domains according to Pfam
p1

Installation

You can install ragp from github with:

# install.packages("remotes") #if not present
# install.packages("git2r") #if not present
remotes::install_github("missuse/ragp")

Or alternatively to build vignettes use:

# install.packages("remotes") 
# install.packages("git2r") 
remotes::install_git("https://github.com/missuse/ragp",
                     build_vignettes = FALSE)

Vignettes can be viewed by:

browseVignettes("ragp")

Tutorials

Tutorials on usage of ragp functions with examples on how to combine them into meaningful HRGP filtering and analysis pipelines are available at: https://missuse.github.io/ragp/

Bug reports

If you encounter undesired behavior in ragp functions or you have ideas how to improve them please open an issue at: https://github.com/missuse/ragp/issues

Citation

If you find ragp useful in your own research please cite our Glycobiology paper.

Milan B Dragićević, Danijela M Paunović, Milica D Bogdanović, Slađana I Todorović, Ana D Simonović (2020) ragp: Pipeline for mining of plant hydroxyproline-rich glycoproteins with implementation in R, Glycobiology 30(1) 19–35, https://doi.org/10.1093/glycob/cwz072

You can get citation info via citation("ragp") or by copying the following BibTex entry:

@article{10.1093/glycob/cwz072,
    author = {Dragićević, Milan B and Paunović, Danijela M and Bogdanović, Milica D and Todorović, Slađana I and Simonović, Ana D},
    title = "{ragp: Pipeline for mining of plant hydroxyproline-rich glycoproteins with implementation in R}",
    journal = "{Glycobiology}",
    issn = "{1460-2423}",
    publisher = "{Oxford University Press}",
    year = "{2020}",
    volume = "{30}",  
    number = "{1}",
    pages = "{19–35}",
    url = "{https://doi.org/10.1093/glycob/cwz072}",
    doi = "{10.1093/glycob/cwz072}",
    eprint = "{https://academic.oup.com/glycob/article-pdf/30/1/19/5567434/cwz072.pdf}"
}

Acknowledgments

This software was developed with funding from the Ministry of Education, Science and Technological Development of the Republic of Serbia (Projects TR31019 and OI173024).

missuse/ragp documentation built on Jan. 4, 2022, 10:49 a.m.