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: lwzhanghz@163.com)
This script was used for produce genomic profiles in wiggle/bedgraph format using the input bam file.
Python module: pysam, numpy
python script.py [options] arg1 arg2
-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.
python slidingCountFromBam.py -i input.bam -w 50000 -t 1000 -f bg -o output.bedgraph
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.
- 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.
- 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
- Run script to calculate S50 score for all genomic bins.
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.
python computeS50.py -i S1.bedgraph,S2.bedgraph,S3.bedgraph,S4.bedgraph -t bedgraph -o S50.bedgraph
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.
- Region files can be prepared similarly as that of computeS50.py or a set of pre-predicted peak regions in bed format.
- Using intersectBed to intersectBed to retrieve read count for region/peaks of interest.
- 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
python F-score.py 17321232,12987432 untreated.bed treated.bed my.F-score