protr

Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) <DOI:10.1093/bioinformatics/btv042> (PDF).

Paper citation

Formatted citation:

Nan Xiao, Dong-Sheng Cao, Min-Feng Zhu, Qing-Song Xu (2015). protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics, 31(11), 1857--1859.

BibTeX entry:

@article{Xiao2015,
  author  = {Xiao, Nan and Cao, Dong-Sheng and Zhu, Min-Feng and Xu, Qing-Song.},
  title   = {protr/{ProtrWeb}: {R} package and web server for generating various numerical representation schemes of protein sequences},
  journal = {Bioinformatics},
  year    = {2015},
  volume  = {31},
  number  = {11},
  pages   = {1857--1859},
  doi     = {10.1093/bioinformatics/btv042}
}

Installation

To install protr from CRAN:

install.packages("protr")

Or try the latest version on GitHub:

remotes::install_github("nanxstats/protr")

Browse the package vignette for a quick-start.

Shiny app

ProtrWeb, the Shiny web application built on protr, can be accessed from http://protr.org.

ProtrWeb is a user-friendly web application for computing the protein sequence descriptors (features) presented in the protr package.

List of supported descriptors

Commonly used descriptors

Amino acid composition descriptors
- Amino acid composition
- Dipeptide composition
- Tripeptide composition
Autocorrelation descriptors
- Normalized Moreau-Broto autocorrelation
- Moran autocorrelation
- Geary autocorrelation
CTD descriptors
- Composition
- Transition
- Distribution
Conjoint Triad descriptors
Quasi-sequence-order descriptors
- Sequence-order-coupling number
- Quasi-sequence-order descriptors
Pseudo amino acid composition (PseAAC)
- Pseudo amino acid composition
- Amphiphilic pseudo amino acid composition
Profile-based descriptors
- Profile-based descriptors derived by PSSM (Position-Specific Scoring Matrix)

Proteochemometric (PCM) modeling descriptors

Scales-based descriptors derived by principal components analysis
- Scales-based descriptors derived by amino acid properties (AAindex)
- Scales-based descriptors derived by 20+ classes of 2D and 3D molecular descriptors (Topological, WHIM, VHSE, etc.)
- Scales-based descriptors derived by factor analysis
- Scales-based descriptors derived by multidimensional scaling
- BLOSUM and PAM matrix-derived descriptors

Similarity computation

Local and global pairwise sequence alignment for protein sequences:

Between two protein sequences
Parallelized pairwise similarity calculation with a list of protein sequences

GO semantic similarity measures:

Between two groups of GO terms / two Entrez Gene IDs
Parallelized pairwise similarity calculation with a list of GO terms / Entrez Gene IDs

Miscellaneous tools and datasets

Retrieve protein sequences from UniProt
Read protein sequences in FASTA format
Read protein sequences in PDB format
Sanity check of the amino acid types appeared in the protein sequences
Protein sequence segmentation
Auto cross covariance (ACC) for generating scales-based descriptors of the same length
20+ pre-computed 2D and 3D descriptor sets for the 20 amino acids to use with the scales-based descriptors
BLOSUM and PAM matrices for the 20 amino acids
Meta information of the 20 amino acids

Contribute

To contribute to this project, please take a look at the Contributing Guidelines first. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

seb-mueller/protr