Difference between revisions of "MCross"

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=Introduction=
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mCross is a computational tool to perform de novo motif discovery for RNA-binding proteins using CLIP data. mCross jointly models the sequence specificity and protein-RNA crosslinking position in the RBP binding motif \by leveraging the crosslink sites mapped at the single-nucleotide resolution by crosslinking induced mutation site (CIMS) and truncation site (CITS) analysis. 
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More details about this work can be found in the following paper:
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<pre>
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Feng et al. (2019), Modeling the in vivo specificity of RNA-binding proteins by precisely registering protein-RNA crosslink sites. Mol Cell. 74:1189-1204.E6.
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</pre>
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=Versions=
 +
*v1.0.0 ( 05-07-2021 )
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**The initial public release
 +
 +
=Software installation=
 +
==Prerequisites==
 +
This software is implemented with Perl and R. We have tested the software on RedHat Linux, although it is expected to work on most Unix-like systems, including Mac OS X.  The package requires the following packages to be installed:
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* R (version 3.0.0 and higher).
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* R packages: '''gplots''', '''motifStack''', '''ggplot2''', '''gridExtra''',  '''cowplot''',and '''getopt'''.
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==mCross installation and preparation==
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=Usage=
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You can run the following command to show descriptions of arguments, input and output format.
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<pre>
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mCross.pl [options] <seq_file> <out_file or out_file_stem>
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</pre>
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Arguments:
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{|class="wikitable" width="55%" style="border:1px solid"
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!'''Argument'''!!'''Description'''
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|-
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| -l||sequence extension around crosslink site
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|-
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|  --seed|| top_nmer_file
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|-
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| --bg|| if top_nmer not provided, fg and bg file are used to get the list
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|-
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| -p|| pad the seed motif on both sides
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|-
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|  -m|| number of mismatches allowed in the core motif
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|-
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|  -N|| max number of seed words to search
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|-
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|  --cluster-seeds|| cluster seed word
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|-
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|  --xl-model|| crosslink model (1=simple(default), 2=nucleotide-specific)
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|-
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|  --score-method||  ([log])/sqrt
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|-
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|  --prefix||  prefix of the motif name
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|-
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|  --single-output-file||  write all motifs to a single file
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|-
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| -c, cache dir||path to write temporary file
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|-
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| -v, verbose||verbose mode
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|}
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mCross takes the sequences around CIMS/CITS sites as input and generate the binding motifs for each input. Please note that mCross can either accept the top n mer file or background sequence fasta file to get the top n mer list.  The top n mer file is generated by counting the occourrance of n mer in the sequence around peak region or CIMS/CITS region. The top n mer file can be generated combined with our CTK toolkit(http://zhanglab.c2b2.columbia.edu/index.php/CTK_Documentation).
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==Peak calling and CIMS/CITS analysis==
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 +
Please check http://zhanglab.c2b2.columbia.edu/index.php/ECLIP_data_analysis_using_CTK for details.
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==Get enriched top n mer from sequences around peak/CIMS/CITS regions==
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1. Extract fasta sequences from the beds files representing the peak or CIMS/CITS region. Typically, we extend 50bp both upstream and downstream around peak center or 10bp around CIMS/CITS sites.
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2. Generate background sequences. We take -550 to -450 upstream of the peak center and 450 to 550 downstream of the peak center as background sequences.
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3. Calculate the enrichment score of n mer. We set n equal to 7 as an example in the following command.
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<pre>
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word_enrich.pl -w 7 -test binom -v Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.normsk.fa  Rbfox_R2.tag.uniq.peak.sig.PH10.center.bg.100.normsk.fa Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.w7.txt
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</pre>
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4. Generate top n mer file as input of mCross.
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<pre>
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gen_word_enrich_matrix.pl  peak.conf  Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.w7.zcore.mat.txt
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</pre>
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This script take a configuration file which includes two columns separated by tab as input:
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<pre>
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Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.w7.txt \tab Rbfox_peak
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</pre>
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5. Extract the top n mer list.
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<pre>
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Rscript topword.R Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.w7.zcore.mat.txt Rbfox_peak_top7mer
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</pre>
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==Run mCross based on top n mer and CIMS/CITS sequences==
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With the top n mer files ready, mCross can either take CIMS or CITS sequence as input. Here we use CITS sequence as an example:
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<pre>
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mCross.pl -l 10 -p 2 -N 1 -m 1 --cluster-seeds --seed Rbfox_peak_top7mer/top.Rbfox_peak.txt --prefix Rbfox Rbfox_R2.tag.uniq.rgb.clean.CITS.s30.singleton.21.normsk.fa Rbfox_peakvsCITS
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</pre>
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mCross will output a list of discovered motifs represented by TRANSFAC format in the output folder.
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=mCrossDB=
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==Web interface==
 
==Web interface==
  
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==Download==
 
==Download==
 
A list of position frequency matrices for 112 unique RBPs derived from eCLIP data: [http://zhanglab.c2b2.columbia.edu/data/mCross/eCLIP_mCross_PWM.tgz download here (199 kb)].
 
A list of position frequency matrices for 112 unique RBPs derived from eCLIP data: [http://zhanglab.c2b2.columbia.edu/data/mCross/eCLIP_mCross_PWM.tgz download here (199 kb)].
 
 
==Citation==
 
Feng et al. (2018), Modeling the in vivo specificity of RNA-binding proteins by precisely registering protein-RNA crosslink sites.  in submission.
 

Latest revision as of 12:06, 27 February 2024

Introduction

mCross is a computational tool to perform de novo motif discovery for RNA-binding proteins using CLIP data. mCross jointly models the sequence specificity and protein-RNA crosslinking position in the RBP binding motif \by leveraging the crosslink sites mapped at the single-nucleotide resolution by crosslinking induced mutation site (CIMS) and truncation site (CITS) analysis.

More details about this work can be found in the following paper:

Feng et al. (2019), Modeling the in vivo specificity of RNA-binding proteins by precisely registering protein-RNA crosslink sites. Mol Cell. 74:1189-1204.E6.

Versions

  • v1.0.0 ( 05-07-2021 )
    • The initial public release

Software installation

Prerequisites

This software is implemented with Perl and R. We have tested the software on RedHat Linux, although it is expected to work on most Unix-like systems, including Mac OS X. The package requires the following packages to be installed:

  • R (version 3.0.0 and higher).
  • R packages: gplots, motifStack, ggplot2, gridExtra, cowplot,and getopt.

mCross installation and preparation

Usage

You can run the following command to show descriptions of arguments, input and output format.

mCross.pl [options] <seq_file> <out_file or out_file_stem>

Arguments:

Argument Description
-l sequence extension around crosslink site
--seed top_nmer_file
--bg if top_nmer not provided, fg and bg file are used to get the list
-p pad the seed motif on both sides
-m number of mismatches allowed in the core motif
-N max number of seed words to search
--cluster-seeds cluster seed word
--xl-model crosslink model (1=simple(default), 2=nucleotide-specific)
--score-method ([log])/sqrt
--prefix prefix of the motif name
--single-output-file write all motifs to a single file
-c, cache dir path to write temporary file
-v, verbose verbose mode


mCross takes the sequences around CIMS/CITS sites as input and generate the binding motifs for each input. Please note that mCross can either accept the top n mer file or background sequence fasta file to get the top n mer list. The top n mer file is generated by counting the occourrance of n mer in the sequence around peak region or CIMS/CITS region. The top n mer file can be generated combined with our CTK toolkit(http://zhanglab.c2b2.columbia.edu/index.php/CTK_Documentation).


Peak calling and CIMS/CITS analysis

Please check http://zhanglab.c2b2.columbia.edu/index.php/ECLIP_data_analysis_using_CTK for details.

Get enriched top n mer from sequences around peak/CIMS/CITS regions

1. Extract fasta sequences from the beds files representing the peak or CIMS/CITS region. Typically, we extend 50bp both upstream and downstream around peak center or 10bp around CIMS/CITS sites.

2. Generate background sequences. We take -550 to -450 upstream of the peak center and 450 to 550 downstream of the peak center as background sequences.

3. Calculate the enrichment score of n mer. We set n equal to 7 as an example in the following command.

word_enrich.pl -w 7 -test binom -v Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.normsk.fa  Rbfox_R2.tag.uniq.peak.sig.PH10.center.bg.100.normsk.fa Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.w7.txt

4. Generate top n mer file as input of mCross.

gen_word_enrich_matrix.pl  peak.conf  Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.w7.zcore.mat.txt

This script take a configuration file which includes two columns separated by tab as input:

Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.w7.txt \tab Rbfox_peak

5. Extract the top n mer list.

Rscript topword.R Rbfox_R2.tag.uniq.peak.sig.PH10.center.100.w7.zcore.mat.txt Rbfox_peak_top7mer

Run mCross based on top n mer and CIMS/CITS sequences

With the top n mer files ready, mCross can either take CIMS or CITS sequence as input. Here we use CITS sequence as an example:

mCross.pl -l 10 -p 2 -N 1 -m 1 --cluster-seeds --seed Rbfox_peak_top7mer/top.Rbfox_peak.txt --prefix Rbfox Rbfox_R2.tag.uniq.rgb.clean.CITS.s30.singleton.21.normsk.fa Rbfox_peakvsCITS

mCross will output a list of discovered motifs represented by TRANSFAC format in the output folder.

mCrossDB

Web interface

Access mCrossBase>>>.


Download

A list of position frequency matrices for 112 unique RBPs derived from eCLIP data: download here (199 kb).