ChromTime is written in Python 2.7 and C and can be run on Linux and MacOS.
You need to install Numpy and SciPy before you can run ChromTime. You can use the following URL or your favorite package manager:
After you installed Numpy and SciPy, you have to compile the C code with gcc or a similar C compiler. You can do this by simply typing make in
the ChromTime directory. If you are using a C compiler that is different from gcc, you should edit the provided Makefile accordingly.
After the compilation has finished, you should be able to run ChromTime by typing:
$ python ChromTime.py
usage: ChromTime.py [-h] [-a ALIGNED_FNAMES [ALIGNED_FNAMES ...]]
[-c CONTROL_FNAMES [CONTROL_FNAMES ...]] [-i ORDER_FNAME]
[-m {punctate,narrow,broad}] [-g GENOME] [-o DIRECTORY]
[-p PREFIX] [-b INT] [-t INT] [-s SHIFT]
[-q FDR_FOR_DECODING] [--q-value-seed Q_VALUE_SEED]
[--p-value-extend P_VALUE_EXTEND]
[--min-expected-reads MIN_EXPECTED_READS]
[--min-gap MIN_GAP] [--merge-peaks]
[--min-dynamic-prior MIN_DYNAMIC_PRIOR]
[--model-file FILE] [--data-file FILE] [--skip-training]
[-n INT] [--output-signal-files]
ChromTime: Modeling Spatio-temporal Dynamics of Chromatin Marks
optional arguments:
-h, --help show this help message and exit
Input data from command line:
-a ALIGNED_FNAMES [ALIGNED_FNAMES ...], --aligned-reads ALIGNED_FNAMES [ALIGNED_FNAMES ...]
BED files with aligned reads for each time point in
the correct order
-c CONTROL_FNAMES [CONTROL_FNAMES ...], --control-reads CONTROL_FNAMES [CONTROL_FNAMES ...]
BED files with aligned reads for control (input) for
each time point in the correct order
Input data from order file:
-i ORDER_FNAME, --input-order-file ORDER_FNAME
A tab-separated file with paths to files with
foreground and control aligned reads - one line per
time point, in the right order.
Options:
-m {punctate,narrow,broad}, --mode {punctate,narrow,broad}
punctate: equivalent to "-b 200 --min-gap 600 --min-
dynamic-prior 0.05", narrow (default): equivalent to
"-b 200 --min-gap 600 --min-dynamic-prior 0", broad:
equivalent to "-b 500 --min-gap 1500 --merge-peaks
--min-dynamic-prior 0"
-g GENOME, --genome GENOME
Genome. One of: [hg18, hg19, mm10, mm9, zv9] or path
to a file with chromosome sizes one per line
-o DIRECTORY, --output-dir DIRECTORY
Output directory
-p PREFIX, --prefix PREFIX
prefix for the output files
-b INT, --bin-size INT
Bin size in base pairs (Default: 200)
-t INT, --threads INT
Number of threads to use (Default: 1)
-s SHIFT, --shift SHIFT
Number of bases to shift each read (Default: 100)
-q FDR_FOR_DECODING, --q-value FDR_FOR_DECODING
False discovery rate (Q-value) for calling peaks at
each time point (Default: 0.05)
--q-value-seed Q_VALUE_SEED
FDR threshold to call significant bins (Default: 0.05)
--p-value-extend P_VALUE_EXTEND
FDR threshold to call significant bins (Default: 0.15)
--min-expected-reads MIN_EXPECTED_READS
Minimum expected reads per bin for the background
component (Default: 1)
--min-gap MIN_GAP Minimum gap between significant regions before they
are joined (Default: 600)
--merge-peaks Merge significant peaks across time points instead of
splitting them (Default: False)
--min-dynamic-prior MIN_DYNAMIC_PRIOR
Minimum prior probability for each dynamic at each
time point (Default: 0.0)
--model-file FILE Pickled model to load
--data-file FILE Pickled data to load
--skip-training Skip EM training (Default: False)
-n INT, --n-training-examples INT
Number of training examples to use. (Default: 10000)
--output-signal-files
Output signal files for each time point in wiggle
format (Default: False)
Sample input data taken from Zhang et al. 2012
for H3K4me2 on chr19 and the corresponding ChromTime output can be found in example/. The ChromTime output can be also viewed in the UCSC Genome Browser
if you click here.
ChromTime takes as input aligned reads from ChIP-seq experiments in BED format, one per time point. In addition, you can specify aligned reads from control experiments (Input, IgG, etc) to be used as background. There are two ways specify the input fo ChromTime:
-
Specify a space-separated list of files for each time point with the
-aand-coptions. The files should be in the right order according to the time course. For example:python ~/software/ChromTime/ChromTime.py -a data/FLDN1_H3K4me2.merged.chr19.bed.gz data/FLDN2a_H3K4me2.merged.chr19.bed.gz data/FLDN2b_H3K4me2.merged.chr19.bed.gz data/ThyDN3_H3K4me2.merged.chr19.bed.gz data/ThyDP_H3K4me2.merged.chr19.bed.gz -c data/FLDN1_Input_rep1.chr19.bed.gz data/FLDN2a_Input_rep1.chr19.bed.gz data/FLDN2b_Input_rep1.chr19.bed.gz data/ThyDN3_Input_rep1.chr19.bed.gz data/ThyDP_Input_rep1.chr19.bed.gz -o t_cell_development.H3K4ME2 -p t_cell_development.H3K4ME2 -t 4 -g mm9 -
Specify a tab-separated text file with the path to ChIP-seq and background reads for each time point with the
-ioption. The file names should come in the correct order according the time course (i.e. line 1 corresponds to time point 1, line 2 to time point 2 and so on) Paths can be either relative or absolute. For example:python ~/software/ChromTime/ChromTime.py -i input_order -o t_cell_development.H3K4ME2 -p t_cell_development.H3K4ME2 -t 4 -g mm9
Where input_order contains
data/FLDN1_H3K4me2.merged.chr19.bed.gz data/FLDN1_Input_rep1.chr19.bed.gz
data/FLDN2a_H3K4me2.merged.chr19.bed.gz data/FLDN2a_Input_rep1.chr19.bed.gz
data/FLDN2b_H3K4me2.merged.chr19.bed.gz data/FLDN2b_Input_rep1.chr19.bed.gz
data/ThyDN3_H3K4me2.merged.chr19.bed.gz data/ThyDN3_Input_rep1.chr19.bed.gz
data/ThyDP_H3K4me2.merged.chr19.bed.gz data/ThyDP_Input_rep1.chr19.bed.gz
All output files will be stored in the directory specified by the -o option. Each file will be prefixed with the label given by the -p option, if it is specified.
Here is an example output generated from the above order file:
Predictions for each time point
t_cell_development.H3K4ME2.FLDN1_H3K4me2.merged.chr19.ChromTime_timepoint_predictions.bed.gz
t_cell_development.H3K4ME2.FLDN2a_H3K4me2.merged.chr19.ChromTime_timepoint_predictions.bed.gz
t_cell_development.H3K4ME2.FLDN2b_H3K4me2.merged.chr19.ChromTime_timepoint_predictions.bed.gz
t_cell_development.H3K4ME2.ThyDN3_H3K4me2.merged.chr19.ChromTime_timepoint_predictions.bed.gz
t_cell_development.H3K4ME2.ThyDP_H3K4me2.merged.chr19.ChromTime_timepoint_predictions.bed.gz
The .ChromTime_timepoint_predictions.bed.gz files contain the predicted boundaries in gzipped BED format for each peak at each time point.
They have the following format:
chromosome <tab> predicted start <tab> predicted end <tab> region id # predicted class <tab> 1000 <tab> . <tab> color
For example:
chr19 4865000 4865800 chr19-100-1#S-C-S-S/S-C-S-S 1000 . 4865000 4865800 90,90,90
chr19 36907400 36911400 chr19-1000-1#S-S-S-S/S-S-C-S 1000 . 36907400 36911400 90,90,90
chr19 36978600 36979400 chr19-1001-1#S-S-S-S/S-S-S-S 1000 . 36978600 36979400 90,90,90
Full output from ChromTime that contains extra information like posterior probabilities
t_cell_development.H3K4ME2.ChromTime_full_output.bed.gz
Learned model and intermediate data stored in pickle format
t_cell_development.H3K4ME2.data.pickle
t_cell_development.H3K4ME2.model.pickle
Initial block boundaries and significant bins for each time point
t_cell_development.H3K4ME2_FLDN1_H3K4me2.merged.chr19.block_boundaries.bed.gz
t_cell_development.H3K4ME2_FLDN1_H3K4me2.merged.chr19.significant_bins.bed.gz
t_cell_development.H3K4ME2_FLDN2a_H3K4me2.merged.chr19.significant_bins.bed.gz
t_cell_development.H3K4ME2_FLDN2b_H3K4me2.merged.chr19.significant_bins.bed.gz
t_cell_development.H3K4ME2_ThyDN3_H3K4me2.merged.chr19.significant_bins.bed.gz
t_cell_development.H3K4ME2_ThyDP_H3K4me2.merged.chr19.significant_bins.bed.gz
Observed and expected read counts and RPKM values in wiggle format for each time point
t_cell_development.H3K4ME2_FLDN1_H3K4me2.merged.chr19.READ_COUNTS.wig.gz
t_cell_development.H3K4ME2_FLDN1_H3K4me2.merged.chr19.EXPECTED.wig.gz
t_cell_development.H3K4ME2_FLDN1_H3K4me2.merged.chr19.RPKM.wig.gz
t_cell_development.H3K4ME2_FLDN2a_H3K4me2.merged.chr19.READ_COUNTS.wig.gz
t_cell_development.H3K4ME2_FLDN2a_H3K4me2.merged.chr19.EXPECTED.wig.gz
t_cell_development.H3K4ME2_FLDN2a_H3K4me2.merged.chr19.RPKM.wig.gz
t_cell_development.H3K4ME2_FLDN2b_H3K4me2.merged.chr19.READ_COUNTS.wig.gz
t_cell_development.H3K4ME2_FLDN2b_H3K4me2.merged.chr19.EXPECTED.wig.gz
t_cell_development.H3K4ME2_FLDN2b_H3K4me2.merged.chr19.RPKM.wig.gz
t_cell_development.H3K4ME2_ThyDN3_H3K4me2.merged.chr19.READ_COUNTS.wig.gz
t_cell_development.H3K4ME2_ThyDN3_H3K4me2.merged.chr19.EXPECTED.wig.gz
t_cell_development.H3K4ME2_ThyDN3_H3K4me2.merged.chr19.RPKM.wig.gz
t_cell_development.H3K4ME2_ThyDP_H3K4me2.merged.chr19.READ_COUNTS.wig.gz
t_cell_development.H3K4ME2_ThyDP_H3K4me2.merged.chr19.EXPECTED.wig.gz
t_cell_development.H3K4ME2_ThyDP_H3K4me2.merged.chr19.RPKM.wig.gz
ChromTime log file
t_cell_development.H3K4ME2.log
Fiziev P, Ernst J. ChromTime: modeling spatio-temporal dynamics of chromatin marks. Genome Biology, 19:109, 2018.