ChromHMM can segment genomes into different chromatin states by modeling re-occuring combinatorial and spatial pattern of various histone modifications with a multivariate Hidden Markov Model. The resulting segmentations can be used to annotate genomes. Bed files for immediate visualization in genome browsers are generated.

ChromHMM automatically computes state enrichments for functional and annotation datasets (TSSs, exons, ...) which facilitates the biological characterization of each state.

On Biowulf, ChromHMM can be run in two ways. The full path to the jarfile can be called like this:

java -mx4000M -Djava.awt.headless=true -jar $CHROMHMM_HOME/ChromHMM.jar [ command ] [ options ]
java -mx4000M -Djava.awt.headless=true -jar $CHROMHMM_JAR [ command ] [ options ]

The amount of memory is assigned with -mx[num], where 4000 MB is allocated. The second option -Djava.awt.headless=true is required, unless an X11 display is available. See here for more information about X11 display.

An easier way is to use the wrapper script ChromHMM.sh. This wrapper script includes an additional option to set the amount of memory:

ChromHMM.sh --memory 8g [ command ] [ options ]

By default, ChromHMM uses 4gb of memory. To allocate a different amount of memory, for example 20gb, include --memory 20g on the commandline.

References:

• Jason Ernst and Manolis Kellis. ChromHMM: automating chromatin-state discovery and characterization. Nature Methods 2012(9): 215. Pubmed  |  PMC |  Journal

• Home page
• Manual

• Module Name: ChromHMM (see the modules page for more information)
• ChromHMM LernModel can use more than one CPU. Make sure to match the number of cpus requested with the number used with the -p option.
• Module sets the $CHROMHMM_HOME and $CHROMHMM_JAR environment variables
• Example files in $CHROMHMM_TEST_DATA

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$
[user@biowulf]$ sinteractive --mem=8g --cpus-per-task=4 --gres=lscratch:10
salloc.exe: Pending job allocation 46116226
salloc.exe: job 46116226 queued and waiting for resources
salloc.exe: job 46116226 has been allocated resources
salloc.exe: Granted job allocation 46116226
salloc.exe: Waiting for resource configuration
salloc.exe: Nodes cn3144 are ready for job

[user@cn3144]$ cd /lscratch/$SLURM_JOB_ID
[user@cn3144]$ module load ChromHMM
[user@cn3144]$ cp -R $CHROMHMM_TEST_DATA/SAMPLEDATA_HG18 .
[user@cn3144]$ # train a model with 8 States; leave some buffer between memory given to JVM and
               # allocated memory
[user@cn3144]$ ChromHMM.sh --memory 7g LearnModel -p $SLURM_CPUS_PER_TASK SAMPLEDATA_HG18 out 8 hg18
...
[user@cn3144]$ cp -r out /data/user/where/you/want/your/chromhmm/results

[user@cn3144]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf]$

Create a batch input file (e.g. ChromHMM.sh), which uses the input file 'ChromHMM.in'. For example:

#! /bin/bash

function fail() {
    echo "$@" >&2
    exit 1
}
module load ChromHMM/1.20 || fail "could not load module ChromHMM"
cd /lscratch/$SLURM_JOB_ID || fail "could not use lscratch"
cp -R $CHROMHMM_TEST_DATA/SAMPLEDATA_HG18 .
ChromHMM.sh --memory 8g LearnModel -p ${SLURM_CPUS_PER_TASK} \
  SAMPLEDATA_HG18 OUTPUTSAMPLE 10 hg18

Submit this job using the Slurm sbatch command.

sbatch --cpus-per-task=8 --mem=9g ChromHMM.sh

Create a swarmfile (e.g. ChromHMM.swarm). For example:

ChromHMM.sh --memory 8g LearnModel -p ${SLURM_CPUS_PER_TASK} \
  SAMPLEDATA_HG18 OUTPUTSAMPLE8 8 hg18
ChromHMM.sh --memory 8g LearnModel -p ${SLURM_CPUS_PER_TASK} \
  SAMPLEDATA_HG18 OUTPUTSAMPLE10 10 hg18
ChromHMM.sh --memory 8g LearnModel -p ${SLURM_CPUS_PER_TASK} \
  SAMPLEDATA_HG18 OUTPUTSAMPLE12 12 hg18

Submit this job using the swarm command.

swarm -f ChromHMM.swarm -g 9 -t 4 --module ChromHMM/1.20