Code for sequencing data analysis of DNA replication

Major codes for paper "H2A.Z Facilitates Licensing and Activation of Early Replication" are deposited here. There might be some error during running them, please let me know if needed (E-mail: [email protected])

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slidingCountFromBam.py

Description:

This script was used for produce genomic profiles in wiggle/bedgraph format using the input bam file.

Prerequisite:

Python module: pysam, numpy

Usage:

python script.py [options] arg1 arg2

Options:

-h, --help show this help message and exit
-i INBAM, --input-bam=INBAM
   The inputted bam file.
-w SLIDEWINDOW, --sliding-window=SLIDEWINDOW
   The width (bp) of sliding window during tag counting
   (DEFAULT: 10000).
-t INTERVALSTEP, --interval-step=INTERVALSTEP
   Step length (bp) for each counting (DEFAULT: 1000).
-f OUTFMT, --output-format=OUTFMT
   bg:bedgraph, wig: wiggle.
-o OUTFILE, --output-wig=OUTFILE
   Specify the file in which the result should be stored.

Example:

  python slidingCountFromBam.py -i input.bam -w 50000 -t 1000 -f bg -o output.bedgraph

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computeS50.py

Description:

H2A.Z Facilitates Licensing and Activation of Early Replication. S50 algorithm was used to describe the DNA replication-timing of genomic regions of interest using reli-seq data Gaetano Ivan Dellino and et. al.

Input files are a series of read per million (RPM), or other algorithm, normalized bedgraph/wiggle formatted files of time-course repli-seq data, such as S1,S2,S3 and et.al.

1. Genomic bins of input files should be generated with the same parameters using slidingCountFromBam.py to ensure the consistency of genomic bin positions between files.
   
2. Using intersectBed to retrieve read count for each genomic bin at each time point and then normalized using RPM-like method. An example of files is shown below:
   

chr start end normValue
chr1 1 5000 0.36
chr1 5001 10000 0.37
chr1 10001 15000 0.34

3. Run script to calculate S50 score for all genomic bins.
   

Usage:

python script.py [options] arg1 arg2

Options:
-h, --help show this help message and exit
-i INFILES, --input-files=INFILES
   The inputted wig/bedgraph files separated by comma.
-t FILETYPE, --file-type=FILETYPE
   Format of input files and output file, wiggle or
   bedgraph.(Default: bedgraph)
-o OUTFILE, --output-wig=OUTFILE
   Specify the file in which the result should be stored.

Example:

  python computeS50.py -i S1.bedgraph,S2.bedgraph,S3.bedgraph,S4.bedgraph -t bedgraph -o S50.bedgraph

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F-score.py

Description:

F-score (firing score) was used to assess the effective firing efficiency of genomic regions using nascent strand (NS)-seq data, each sample contains two parallel data, RNase untreated and treated sequencing data.

Usage:

1. Region files can be prepared similarly as that of computeS50.py or a set of pre-predicted peak regions in bed format.
   
2. Using intersectBed to intersectBed to retrieve read count for region/peaks of interest.
   
3. Run script to compute F-score:
     python F-score.py total_reads_of_untreated,total_reads_of_treated untreated.bed treated.bed output.F-score
   

Example:

 python F-score.py 17321232,12987432 untreated.bed treated.bed my.F-score