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

Formatted citation:

Nan Xiao, Dong-Sheng Cao, Min-Feng Zhu, and 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},
  issn = {1367-4803},
  url = {http://bioinformatics.oxfordjournals.org/content/31/11/1857}
}

To install protr from CRAN:

install.packages("protr")

Or try the latest version on GitHub:

# install.packages("devtools")
devtools::install_github("nanxstats/protr")

Browse the package vignette for a quick-start.

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.

• 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)

• 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

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

• 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

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.