Overall, ‘idpr’ aims to integrate tools for the computational analysis
of intrinsically disordered proteins within R. This package is used to
identify known characteristics of IDPs within a sequence of interest
with easily reported and dynamic results. Additionally, this package
also includes tools for IDP-based sequence analysis to be used in
conjunction with other R packages. See our recently published
peer-reviewed publication in PLOS ONE
(https://doi.org/10.1371/journal.pone.0266929)
Please Refer to idpr-vignette.Rmd file for a detailed introduction to
theidpr package.
You can install the stable release version version from
Bioconductor with:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("idpr")
Additionally, you can install the development version from
Bioconductor
with:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
Or you can install the most recent development version from
GitHub with:
# install.packages("devtools") #if not already installed
devtools::install_github("wmm27/idpr")
Example
This is a basic example to quickly profile your protein of interest:
citation("idpr")
#>
#> To cite idpr in publications, use the citation below and other
#> function-specific sources found in the idpr package documentation:
#>
#> McFadden, W. M., and Yanowitz, J. L. (2022). idpr: A package for
#> profiling and analyzing Intrinsically Disordered Proteins in R. PLOS
#> ONE, 17(4), e0266929. doi:10.1371/journal.pone.0266929
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R},
#> author = {William M McFadden and Judith L Yanowitz},
#> journal = {PLOS ONE},
#> publisher = {Public Library of Science},
#> year = {2022},
#> volume = {17},
#> number = {4},
#> pages = {e0266929},
#> doi = {10.1371/journal.pone.0266929},
#> url = {https://bioconductor.org/packages/idpr/},
#> }
Function citations
Bálint Mészáros, Gábor Erdős, Zsuzsanna Dosztányi, IUPred2A:
context-dependent prediction of protein disorder as a function of
redox state and protein binding, Nucleic Acids Research, Volume 46,
Issue W1, 2 July 2018, Pages W329–W337,
https://doi.org/10.1093/nar/gky384
Erdős, G., & Dosztányi, Z. (2020). Analyzing protein disorder with
IUPred2A. Current Protocols in Bioinformatics, 70, e99.
https://doi.org/10.1002/cpbi.99
Kyte, J., & Doolittle, R. F. (1982). A simple method for displaying
the hydropathic character of a protein. Journal of molecular
biology, 157(1), 105-132.
Nelson, D. L., & Cox, M. M. (2017). Lehninger Principles of
Biochemistry (Seventh ed.). New York, NY: W. H. Freeman and Company.
Prilusky, J., Felder, C. E., et al. (2005). FoldIndex: a simple tool
to predict whether a given protein sequence is intrinsically
unfolded. Bioinformatics, 21(16), 3435-3438.
Uversky, V. N. (2016). Paradoxes and wonders of intrinsic disorder:
Complexity of simplicity. Intrinsically Disordered Proteins, 4(1),
e1135015. https://doi.org/10.1080/21690707.2015.1135015
Uversky, V. N. (2013). A decade and a half of protein intrinsic
disorder: Biology still waits for physics. Protein Science, 22(6),
693-724. doi:10.1002/pro.2261
Uversky, V. N., Gillespie, J. R., & Fink, A. L. (2000). Why are
“natively unfolded” proteins unstructured under physiologic
conditions?. Proteins: structure, function, and bioinformatics,
41(3), 415-427.
https://doi.org/10.1002/1097-0134(20001115)41:3<415::AID-PROT130>3.0.CO;2-7
Additional Information
Sys.time()
#> [1] "2022-04-25 12:20:36 EDT"
Sys.Date()
#> [1] "2022-04-25"
R.version
#> _
#> platform x86_64-apple-darwin17.0
#> arch x86_64
#> os darwin17.0
#> system x86_64, darwin17.0
#> status
#> major 4
#> minor 1.3
#> year 2022
#> month 03
#> day 10
#> svn rev 81868
#> language R
#> version.string R version 4.1.3 (2022-03-10)
#> nickname One Push-Up
idpr
Overall, ‘idpr’ aims to integrate tools for the computational analysis of intrinsically disordered proteins within R. This package is used to identify known characteristics of IDPs within a sequence of interest with easily reported and dynamic results. Additionally, this package also includes tools for IDP-based sequence analysis to be used in conjunction with other R packages. See our recently published peer-reviewed publication in PLOS ONE (https://doi.org/10.1371/journal.pone.0266929)
Please Refer to idpr-vignette.Rmd file for a detailed introduction to the idpr package.
Links to the vignettes found at the Bioconductor landing page (here) or
Installation
You can install the stable release version version from Bioconductor with:
Additionally, you can install the development version from Bioconductor with:
Or you can install the most recent development version from GitHub with:
Example
This is a basic example to quickly profile your protein of interest:
Please Refer to idpr-vignette.Rmd file for a detailed introduction to the idpr package. Link to the Vignette (here)
Appendix
For use and details on ‘idpr’, see our peer-reviewed article published in PLOS ONE (https://doi.org/10.1371/journal.pone.0266929)
Package citation
Function citations
Additional Information