https://github.com/whitead/peplib

Peptide library methods
https://github.com/whitead/peplib

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Peptide library methods

• Host: GitHub
• URL: https://github.com/whitead/peplib
• Owner: whitead
• Created: 2012-04-02T15:58:28.000Z (almost 13 years ago)
• Default Branch: master
• Last Pushed: 2015-10-01T12:26:48.000Z (over 9 years ago)
• Last Synced: 2024-10-16T02:59:08.306Z (3 months ago)
• Language: R
• Homepage:
• Size: 833 KB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Peptide Library Analysis Methods

=========================

This package provides a variety of methods for dealing with analysis

of peptide library data, including clustering, motif finding, and QSAR

model fitting. It is for the R programming language and is described

in a [recent paper](http://pubs.acs.org/doi/full/10.1021/ci300484q).

Installing From Source (preferred)

----------

To install the latest version from the source here, use:

    wget https://github.com/whitead/peplib/archive/master.zip

    unzip master.zip && rm master.zip

    R CMD build peplib-master

    sudo R CMD INSTALL peplib_*.tar.gz

Installing From CRAN

------------------

To install from CRAN, type the following command from 

an R session

    install.packages("peplib")

Documentation

--------------------

Check out the `tutorial.pdf` file. It contains

many more details than the brief information below and detailed

tutorials.

Loading Sequences

--------------------

The easiest way to load sequences is to use the `read.sequences` method.

    seq <- read.sequences("seqfile.txt")

where `seqfile.txt` looks like:

    FDDSDF

    FDSA

    GGHIT

For most of the methods, it's recommended to have the same length for all sequences.

Calculating Peptide Descriptors

----------------------------

To calculate descriptors on your sequences, use:

    seq.desc <- simpleDescriptors(seq)

That will calculate about 10 descriptors. To calculate a few hundred, type

    seq.desc <- descriptors(seq)

These descriptors are all relative to glycine. So, for example,

molecular weight is not the actual molecular weight but the difference

between a given amino acid and glycine. 

Plotting Sequences

--------------------

One nice feature of peplib is the ability to plot sequences with a

combination of a finding the substitution distance between sequences

and then projecting that distance matrix to 2 dimensions. This may be

done like so:

    plot(seq)

    

This method also clusters your sequences assuming that there are 3 clusters. That may be changed by adding one argument:

    plot(seq, 1)

    plot(seq, 5)