Docking Analysis in DOCK3.8: Difference between revisions
Line 10: | Line 10: | ||
The scripts are located @ analysis/top_poses in the DOCK repository. | The scripts are located @ analysis/top_poses in the DOCK repository. | ||
=== Python 3.8 === | === Python 3.8+ === | ||
== Conda Environment == | |||
The simplest way to source python 3.8+ is to just install via conda. | |||
<nowiki> | |||
conda create -n py38 python>=3.8</nowiki> | |||
No other packages are required! | |||
== Manual Install == | |||
You need to include a link to a python3.8 executable in the top_poses directory for run_top_poses.bash and run_top_poses_mr.bash to work. This needs to be a link, you cannot copy the executable- it expects to be installed in particular directory. There are no pip requirements, just a blank python 3.8 install. | You need to include a link to a python3.8 executable in the top_poses directory for run_top_poses.bash and run_top_poses_mr.bash to work. This needs to be a link, you cannot copy the executable- it expects to be installed in particular directory. There are no pip requirements, just a blank python 3.8 install. |
Revision as of 20:59, 2 March 2023
Location of new scripts/Install Instructions
You can retrieve these scripts from the DOCK 3.8 repository @ https://github.com/docking-org/DOCK/tree/dev
git clone https://github.com/docking-org/DOCK.git cd DOCK git checkout dev
The scripts are located @ analysis/top_poses in the DOCK repository.
Python 3.8+
Conda Environment
The simplest way to source python 3.8+ is to just install via conda.
conda create -n py38 python>=3.8
No other packages are required!
Manual Install
You need to include a link to a python3.8 executable in the top_poses directory for run_top_poses.bash and run_top_poses_mr.bash to work. This needs to be a link, you cannot copy the executable- it expects to be installed in particular directory. There are no pip requirements, just a blank python 3.8 install.
On Wynton you can use the version installed @ /wynton/group/bks/soft/python-versions/python-3.8-install
If you want to install python3.8 on your own, try the following:
wget https://www.python.org/ftp/python/3.8.8/Python-3.8.8.tgz # MY_SOFT is the directory you want to install to tar -C $MY_SOFT -xzf Python-3.8.8.tgz pushd $MY_SOFT/Python-3.8.8 ./configure --prefix=$MY_SOFT make && make install popd # add the new python 3.8 executable to your path to use export PATH=$PATH:$MY_SOFT/python-3.8-install/bin # optional: clean up the configuration files # rm -r $MY_SOFT/Python-3.8.8.tgz # rm Python-3.8.8.tgz
Scripts Description
top_poses.py
Description
Main pose retrieval algorithm, runs on multiple processes.
Input can be a directory or a file. If input is a directory, the script will use a recursive find command to locate all test.mol2.gz* files residing in the directory structure.
If input is a file, each line in the file should map to a valid pose file, e.g:
/wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0000/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0001/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0002/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0003/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0004/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0005/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0006/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0007/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0008/test.mol2.gz /wynton/group/bks/work/yingyang/5HT-1d/04_LSD/run_dock_es1.5_ld0.3/docked_chunks/chunk0009/test.mol2.gz
Output prefix is where the top N poses will be written out when the script has finished. e.g /scratch/top_poses.mol2.gz, as well as a human-readable .scores file.
Usage
usage: top_poses.py [-h] [-n NPOSES] [-o OUTPREFIX] [-j NPROCESSES] [--id-file INPUT_ID_FILE] [--verbose] [--quiet] [--log-interval LOG_INTERVAL] dockresults_path Retrieve the top N poses from docking results positional arguments: dockresults_path Can be either a directory containing docking results, or a file where each line points to a docking results file. optional arguments: -h, --help show this help message and exit -n NPOSES How many top poses to retrieve, default of 150000 -o OUTPREFIX Output file prefix. Each run will produce two files, a mol2.gz containing pose data, and a .scores file containing relevant score information. Default is "top_poses" -j NPROCESSES How many processes should be dedicated to this run, default is 2. If your files are spread across multiple disks, increasing this number will improve performance. --id-file INPUT_ID_FILE Only retrieve poses matching ids specified in an external file. --verbose write verbose logs to stdout --quiet write minimum logs to stdout --log-interval LOG_INTERVAL number of poses between log statements. Ignored if --quiet enabled
Note on Parallel Processing
By default, this script allocates two extra threads (-j 2) to read in files. This ensures that the main thread can sort poses uninterrupted, while the others take care of the grunt work of reading and annotating files. Increasing the number of reader threads beyond two does not guarantee an improvement in performance, but depending on the filesystem(s) your docking poses live on, they could. For example, on Wynton it can be helpful to allocate up to 8 extra threads for reading files, due to the way the filesystem works on Wynton. On the BKS cluster, increasing the number of reader threads beyond two will have a negligible (or even negative) impact, unless your files happen to be striped across multiple servers.
run_top_poses.bash
Description
Wrapper script for top_poses.py, can be used to submit individual pose jobs. Will run with 7 cores allocated.
Anything in args will be passed to top_poses.py on top of any arguments generated by the script. Valid additional arguments would be -n {X} or --id-file {X}.
Usage
run_top_poses.bash <input> <output> <args>
Typical qsub usage
qsub -wd $PWD run_top_poses.bash <input> <output> <args>
run_top_poses_mr.bash
Description
Map-reduce script to submit a number of analysis jobs and combine their results. The preferred method of running large analysis workloads.
Input field is evaluated the same as in top_poses.py.
Staging directory should be an NFS directory writable by your user. This is where input/output will be stored by the script.
Final output will show up in <staging directory>/output_final.poses.mol2.gz
Batch size refers to how many poses files will be evaluated by each job, the default is 1000, though you may want to modify this depending on the properties of your poses files/how many there are.
Only works on sge for right now. Tested on Wynton.
Anything in args will be passed to top_poses.py on top of any arguments generated by the script. Valid additional arguments would be -n {X} or --id-file {X}.
Usage
run_top_poses_mr.bash <input> <staging directory> <args>
Checking Logs
After your jobs have finished, check the logs to see if anything went wrong.
<staging directory>/logs
If everything went smoothly, there should be an output file corresponding to each input file, there should be nothing in the .err logs, and each .out log should end with a string of text that looks like this:
received all input! joining threads... done processing! writing out... 299900 / 300000
If you find an output file that doesn't end like this, you may wish to re-attempt that particular job.
If you submitted with run_top_poses_mr.bash, all you need to do is to run it again with the same parameters as before. The script detects existing output and will only re-submit as necessary. This will also update the output_final.poses.mol2.gz file.
You may also see a message that looks like this:
short timeout reached while retrieving pose... trying again! curr=...
This just indicates slowness in the file reading, and is common to see at the beginning of a log or when the filesystem is under high load.