https://github.com/gersteinlab/bay2ctrls
https://github.com/gersteinlab/bay2ctrls
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/gersteinlab/bay2ctrls
- Owner: gersteinlab
- License: other
- Created: 2024-12-27T07:00:33.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-27T07:01:08.000Z (5 months ago)
- Last Synced: 2025-01-20T12:08:00.642Z (4 months ago)
- Language: R
- Size: 15.6 KB
- Stars: 0
- Watchers: 10
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Bay2Ctrls: Bayesian model to integrate two types of ChIP-seq controls for binding site detection
Two types of controls were widely used for ChIP-seq: DNA input controls and mock IP controls. This software was developed to combine both for better binding site detection.
This R package is based on SPP, and the wrapper script (run_Bay2Ctrls.R) for the R package is adapted from the ENCODE ChIP-seq pipeline.
### DATA INPUT
The software accecpt four files as input:
The first set is the DNA input controlled IP experiment. This DNA control (di) and the IP (i) are the results from conventional ChIP-seq.
The second set is the mock IP experiment. This includes the mock IP (m) and its corresponding DNA input (dm).
### DATA OUTPUT
Binding peaks are in the narrow peak file format
### DATA DOWNLOAD
Download the test data [here](http://archive2.gersteinlab.org/proj/MockOrNot/Bay2Ctrls/test_data/)
All raw and processed data are available [here](http://archive2.gersteinlab.org/proj/MockOrNot/Data/)
### DEPENDENCE
- [SPP](https://cran.r-project.org/web/packages/spp/index.html)
- [caTools](https://cran.r-project.org/web/packages/caTools/index.html)
Tested with
- R 3.6.1
- SPP 1.16.0
- caTools 1.18.0
Please note that you might encounter a lot of issues if you want to install SPP from source. We recommend you install it via R `install.package("spp")`.
### INSTALLATION
`devtools::install_github("gersteinlab/Bay2Ctrls")`
### USAGE
Run the wrapper script **run_Bay2Ctrls.R** as the following:
`Rscript run_Bay2Ctrls.R -ip= -mock= -input4ip= -input4mock= [Bay2Ctrls_parameters, ...] [SPP_parameters, ...]`
##### Example
`Rscript run_Bay2Ctrls.R -ip=test_data/a.rep0.tagAlign.gz -mock=test_data/EMb1.rep0.tagAlign.gz -input4ip=test_data/ap.rep0.tagAlign.gz -input4mock=test_data/EMb1p.rep0.tagAlign.gz -npeak=30000 -x=-500:85 -s=0:5:1200 -odir=./ -filtchr='.*_[CD].*' -savr -savp -rf -out=test.cc -npeak=30000 -totReads=10000000 -mcstep=1000000`
You might also use the R package Bay2Ctrls in your scripts. See man pages for individual functions(TODO).
##### Paramters specific for Bay2Ctrls
-ip a *.tagAlign.gz file containing mapped reads from the IP experiment (i)
-mock a *.tagAlign.gz file containing mapped reads from the mock IP experiment (m)
-input4ip a *.tagAlign.gz file containing mapped reads from the DNA input control for the IP experiment (di)
-input4mock a *.tagAlign.gz file containing mapped reads from the DNA input control for the mock IP experiment (dm)
-totReads scaling the numbers of reads of the four experiments to the same level, e.g. -totReads=10000000
-mcstep simulation steps for the Bayesian model, e.g. -mcstep=1000000
##### Intrinsic parameters of SPP
-npeak the number of total narrow peaks to be output, e.g. -npeak=30000
-x avoid phantom peaks, e.g. -x=-500:85
-s genomic distance for signal correlation, e.g. -s=0:5:1200
-out output log file
-rf remove previous results
-odir output directory
-p number of processors to be used
More options for output format please refer to the spp R package [github](https://github.com/hms-dbmi/spp) or [CRAN](https://cran.r-project.org/web/packages/spp/index.html)