In vanbibn/mineDisProt: Mine/Extract data on protein disorder from DisProt (and other databases)

knitr::opts_chunk$set(
  collapse = FALSE,
  comment = "#>",
  fig.path = "README-"
)

mineDisProt

The package mineDisProt was developed to extract data from unstructured or semi-structured formats and compile into matrices or data frames more amenable to analysis of intrinsically disorder (ID).

In proteins, intrinsic disorder (ID) is a phenomenon that describes the lack of a stable, or ordered, tertiary structure while still maintaining physiologic functions. Informatics tools, such as those provided by PONDR](http://www.pondr.com/) and PONDR-FIT make it easy to analyze a protein sequence for intrinsic disorder; however, this task can become tedious if one has tens or even hundreds of sequences to analyze.

My goal in developing mineDisProt was to simplify the data collection process so you can focus on the analysis of your data set.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("vanbibn/mineDisProt")

Examples

First, we need to load the package.

library(mineDisProt)

extract_pondr()

This function extracts numerical data from text porduced by the VLXT, VL3, and VSL2 disorder predictors from the Predictor of Natural Disordered Regions PONDR. For each protien sequence, the data from the Raw Output of all three predictors were pasted into a .txt file.

pondr_data <- extract_pondr("inst/extdata/pondr_text/")

# here we will view the data from the VLXT predictor
pondr_data[,1:6]

* You may get warnings about "incomplete final line found [in the text file]."

extract_pondr.noVL3()

This is a version of extract_pondr(), except the raw data for the VL3 predictor is missing from the raw data text files.

extract_pondr.noVL3("inst/extdata/pondr_text_withoutVL3/")

extract_pondrFIT()

This function extracts the relevant data from the temporaty URL produced by analyzing a protein sequence in the PONDR-FIT protein disorder meta-predictor. It then calculates average and percent disorder scores from the per-residue scores. Before using this function, URLs should be collected and put in a .csv file with the UniProt ID in the first column and URL in the second.

extract_pondrFIT("inst/extdata/pondrfit-url.csv")

* You may get messages about "Parsed with column specification:".

extract_string()

Extract data on the quantity of protein interactions given by STRING with the minimum number of interactions at medium (0.4), high (0.7), and highest (0.9) confidence.

string_data <- extract_string("inst/extdata/string/")

# here are the interaction data for each protein at the 0.4 confidence level
string_data[,1:6]

* You may get some warnings like "NAs introduced by coercion" if your protein is missing data at higher confidence levels.

---

Note: In future versions of this package, I hope to fully automate the data collection process.

vanbibn/mineDisProt documentation built on April 30, 2020, 1:39 p.m.