SELEX {SELEX} | R Documentation

## SELEX Package

### Description

Functions to assist in discovering transcription factor DNA binding specificities from SELEX-seq experimental data according to the Slattery et al. paper. For a more comprehensive example, please look at the vignette. Sample data used in the Slattery, et. al. is stored in the `extdata` folder for the package, and can be accessed using either the base R function `system.file` or the package function `selex.exampledata`.

Functions available:

`selex.affinities` | Construct a K-mer affinity table
`selex.config` | Set SELEX system parameters
`selex.counts` | Construct or retrieve a K-mer count table
`selex.countSummary` | Summarize available K-mer count tables
`selex.defineSample` | Define annotation for an individual sample
`selex.exampledata` | Extract example data files
`selex.fastqPSFM` | Construct a diagnostic PSFM for a FASTQ file
`selex.getAttributes` | Display sample handle attributes
`selex.getRound0` | Obtain round zero sample handle
`selex.getSeqfilter` | Display sequence filter attributes
`selex.infogain` | Compute or retrieve information gain between rounds
`selex.infogainSummary` | Summarize available information gain values
`selex.jvmStatus` | Display current JVM memory usage
`selex.kmax` | Calculate kmax for a dataset
`selex.kmerPSFM` | Construct a PSFM from a K-mer table
`selex.loadAnnotation` | Load a sample annotation file
`selex.mm` | Build or retrieve a Markov model
`selex.mmProb` | Compute prior probability of sequence using Markov model
`selex.mmSummary` | Summarize Markov model properties
`selex.revcomp` | Create forward-reverse complement data pairs
`selex.run` | Run a standard SELEX analysis
`selex.sample` | Create a sample handle
`selex.sampleSummary` | Show samples visible to the current SELEX session
`selex.saveAnnotation` | Save sample annotations to file
`selex.seqfilter` | Create a sequence filter
`selex.setwd` | Set or change the working directory
`selex.split` | Randomly split a dataset
`selex.summary` | Display all count table, Markov model, and information gain summaries

### Details

Package: | SELEX
Type: | Package
Version: | .99
Date: | 2014-11-3
License: | GPL

### Author(s)

Chaitanya Rastogi, Dahong Liu, and Harmen Bussemaker
Maintainer: Harmen Bussemaker hjb2004@columbia.edu

### References

Slattery, M., Riley, T.R., Liu, P., Abe, N., Gomez-Alcala, P., Dror, I., Zhou, T., Rohs, R., Honig, B., Bussemaker, H.J.,and Mann, R.S. (2011) Cofactor binding evokes latent differences in DNA binding specificity between Hox proteins. Cell 147:1270–1282.

Riley, T.R., Slattery, M., Abe, N., Rastogi, C., Liu, D., Mann, R.S., and Bussemaker, H.J. (2014) SELEX-seq: a method for characterizing the complete repertoire of binding site preferences for transcription factor complexes. Methods Mol. Biol. 1196:255–278.

### Examples

    #Initialize the SELEX package
    #options(java.parameters="-Xmx1500M")
    #library(SELEX)

    # Configure the current session
    workDir = file.path(".", "SELEX_workspace")
    selex.config(workingDir=workDir,verbose=FALSE, maxThreadNumber= 4)

    # Extract sample data from package, including XML database
    sampleFiles = selex.exampledata(workDir)

    # Load & display all sample files using XML database
    selex.loadAnnotation(sampleFiles[3])
    selex.sampleSummary()

    # Create sample handles
    r0 = selex.sample(seqName="R0.libraries", sampleName="R0.barcodeGC", round=0)
    r2 = selex.sample(seqName='R2.libraries', sampleName='ExdHox.R2', round=2)

    # Split the r0 sample into testing and training sets
    r0.split = selex.split(sample=r0)
    r0.split

    # Display all currently loaded samples
    selex.sampleSummary()

    # Find kmax on the test dataset
    k = selex.kmax(sample=r0.split$test)

    # Build the Markov model on the training dataset
    mm = selex.mm(sample=r0.split$train, order=NA, crossValidationSample=r0.split$test)
    # See Markov model R^2 values
    selex.mmSummary()

    # Kmer counting with an offset
    t1 =  selex.counts(sample=r2, k=2, offset=14, markovModel=NULL)
    # Kmer counting with a Markov model (produces expected counts)
    t2 =  selex.counts(sample=r2, k=4, markovModel=mm)
    # Display all available kmer statistics
    selex.countSummary()

    # Calculate information gain
    ig =  selex.infogain(sample=r2, k=8, mm)
    # View information gain results
    selex.infogainSummary()

    # Perform the default analysis
    selex.run(trainingSample=r0.split$train, crossValidationSample=r0.split$test,
      infoGainSample=r2)

    # View all stats
    selex.summary()

[Package SELEX version 1.16.0 Index]