BackCLIP

The PAR-CLIP protocol derives a transcriptome wide set of binding sites for RNA Binding Proteins. However, non-specific RNA background remains. We propose a tool, BackCLIP, to identify the presence of common RNA background in a PAR-CLIP dataset. We built a common background set where each element has a score that reflects its presence in several PAR-CLIP datasets. We present a tool that uses this score to identify the amount of common backgrounds present in a PAR-CLIP dataset, and we provide the user the option to use or remove it.

This Git contains the software code and output results from [P.H Reyes-Herrera, C.A Speck-Hernandez, C.A. Sierra, and S. Herrera.(2015) BackCLIP: a tool to identify common background presence in PAR-CLIP datasets. Bioinformatics.] (http://bioinformatics.oxfordjournals.org/content/early/2015/07/29/bioinformatics.btv442.abstract)

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Content

• Supplementary data
• Requirements
• Install
• Usage
• License
• Extra

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Supplementary data

• Details for Background Initial Set and Additional Results Supplementary_Information_BACKCLIP_v2.doc
• CommonBackground: BackgroundTraining_19datasets.bed (Common background set used in the paper), Background_49datsets.bed (Common Background built on 49 datasets).The Common Background set built with a large and diverse datasets results in a robust data set.

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Requirements

• Python 2.7 and above
• Python Packages (To install plese (1) download requirements.txt and (2) use the command pip install -r /path/to/requirements.txt )
• BedTools

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Install

Download python scritp (scr/backclip_v2.1.py)

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Usage

• backclip_v2.1.py. This python program identifies the presence of common background in a PAR-CLIP dataset. As a result provides:

  • confidence interval for proportion of sites in the intersection with a score higher than the threshold
  • histogram score distribution in the intersection file
  • common background in a dataset (bed file)
  • dataset without the sites in the intersection that have a score higher than the threshold

• The input argument is:

  • parameters file name (see src/parameters_v1.0): this file contains the following parameters :
    • alpha=significance level (default value 0.01)
    • threshold=to define if the amount of common background is significant (default (maximum score)/2)
    • histogram=give as output histogram of the intersection scores (default false)
    • remove = give as output a modified dataset. We removed sites from the intersection (score > threshold)
    • fbackground=(.BED)file background and corresponding scores (see src/example/GLOBALBACKGROUNDGROUPS.bed.sorted.delete.min10. Nevertheless, the complete version of the background is CommonBackground/Background_49datsets.bed)
    • fclusters=(.BED)file with clusters detected from PAR-CLIP dataset (see src/example/QKI_SRR048972_Bowtie_Score_Cleanmin10_Prueba1.bed)
    • filename=in case histogram is true, the name of the output file (default fileclusters.histbackground)
    • namebed=name bed with set of common background found in fileclusters

  To run, just write on shell

  python backclip_v2.1.py parametersfilename

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Extra

• ClusterDetection (CD_Bg.jar ). This program obtains a set of clusters from a sam format file. Usage: java -jar CD_Bg.jar Setupini
• count_motif_occurrences.py. This program finds the number of motif occurrences in a bed format file. Usage: python count_motif_occurrences.py motif filebed outfile

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License

Created by Paula H. Reyes-Herrera, Cesar A. Speck Hernandez, Carlos Sierra, Santiago Herrera on 5 February 2015 Copyright (c) 2015 Paula H. Reyes-Herrera, Cesar A. Speck Hernandez, Carlos Sierra, Santiago Herrera. All rights reserved.

BackCLIP is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by # the Free Software Foundation, version 2.