cleanUpdTSeq-package {cleanUpdTSeq} | R Documentation

## This package classifies putative polyadenylation sites.

### Description

3'ends of transcripts have generally been poorly annotated. With the advent of deep sequencing, many methods have been developed to identify 3'ends. The majority of these methods use an oligodT primer which can bind to internal adenine-rich sequences, and lead to artifactual identification of polyadenylation sites. Heuristic filtering methods rely on a certain number of As downstream of a putative polyadenylation site to classify the site as true or oligodT primed. This package provides a robust method to classify putative polyadenylation sites using a Naive Bayes classifier.

### Details

Package: | cleanUpdTSeq
Type: | Package
Version: | 1.0
Date: | 2013-07-22
License: | GPL-2

### Author(s)

Sarah Sheppard, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Sarah Sheppard <Sarah.Sheppard@umassmed.edu>, Jianhong Ou <Jianhong.Ou@umassmed.edu>, Lihua Julie Zhu <Julie.Zhu@umassmed.edu>

### References

1. Meyer, D., et al., e1071: Misc Functions of the Department of Statistics (e1071), TU Wien. 2012.

2. Pages, H., BSgenome: Infrastructure for Biostrings-based genome data packages.

3. Sarah Sheppard, Nathan D. Lawson, and Lihua Julie Zhu. 2013. Accurate identification of polyadenylation sites from 3' end deep sequencing using a na\"ive Bayes classifier. Bioinformatics. Under revision

### Examples

    #read in a test set
    #### first install the package using the following command
    #### BiocManager::install("cleanUpdTSeq")
    if (interactive())
    {
    	library(cleanUpdTSeq)
    	testFile = system.file("extdata", "test.bed", package="cleanUpdTSeq")
    	testSet = read.table(testFile, sep = "\t", header = TRUE)

    	#convert the test set to GRanges with upstream and downstream sequence information
    	peaks = BED2GRangesSeq(testSet,upstream.seq.ind = 7, downstream.seq.ind = 8, withSeq=TRUE)
    	#build the feature vector for the test set with sequence information
    	testSet.NaiveBayes = buildFeatureVector(peaks,BSgenomeName = Drerio, upstream = 40,
    	 downstream = 30, wordSize = 6, alphabet=c("ACGT"),
    	 sampleType = "unknown",replaceNAdistance = 30,
    	method = "NaiveBayes", ZeroBasedIndex = 1, fetchSeq = FALSE)

    	#convert the test set to GRanges without upstream and downstream sequence information
            peaks = BED2GRangesSeq(testSet,withSeq=FALSE)

    	#build the feature vector for the test set without sequence information
    	testSet.NaiveBayes = buildFeatureVector(peaks,BSgenomeName = Drerio, upstream = 40,
             downstream = 30, wordSize = 6, alphabet=c("ACGT"),
             sampleType = "unknown",replaceNAdistance = 30,
            method = "NaiveBayes", ZeroBasedIndex = 1, fetchSeq = TRUE)

    	#predict the test set
    	data(data.NaiveBayes)
    	predictTestSet(data.NaiveBayes$Negative, data.NaiveBayes$Positive, testSet.NaiveBayes,
    	outputFile = "test-predNaiveBayes.tsv", assignmentCutoff = 0.5)
    }

[Package cleanUpdTSeq version 1.20.1 Index]