While the main workflow relies only on ready to run containers (available on quay.io), we provide additional Dockerfiles that can be built and used locally.
Please note that building these containers IS NOT REQUIRED for the current CWL pipeline, but could be optionally added if one wants to. Also, several 3rd party predictors require registering on their website prior downloading the software.
DTU predictors are licensed only for for academic and non-profit usage and in order to download the software it is required to register on their website and accept the license agreement prior receiving via email a download link. Please register and download the bellow predictors (linux version as the dockerfile image is ubuntu based) from the Download link
- Phosphorylation module:
- NetPhos
- NetPhosPan
- Glycosylation module:
- NetNGlyc
- NetOGlyc
- NetCGlyc
- SignalP
- Localisation module:
- Tmhmm2
Please note that some of the predictors (see above) require registering on their website in order to download the source code. Also make sure that you have enough disk space available at the location were the docker image is being stored.
# DTU predictors need registering before downloading the software !!!
# Please provide the path to the folder where DTU predictors source packages are being stored.
# Phosphorylation module
export netphospan_SOURCE=/path/to/netphospan/source
export netphos_SOURCE=/path/to/netphos/source
# Glycosylation module
export netcglyc_SOURCE=/path/to/netcglyc/source
export netoglyc_SOURCE=/path/to/netoglyc/source
export netnglyc_SOURCE=/path/to/netnglyc/source
export sinalp_SOURCE=/path/to/sinalp/source
# Localisation module
export tmhmm_SOURCE=/path/to/tmhmm/sourceYou can build only the docker images of the predictors you are interested in (please see bellow), or you can build all docker images by using the following bash script:
bash ${CSW_HOME}/bin/build_all_docker_images.shSome of the individual predictors require downloading and setting up different protein databases. Details of each predictors requirements and usage are shown in each predictors sections bellow :
- RaptorX (see A1)
- Psipred (see A2)
- Disopred (see A3)
For testing that all the docker images work as expected, please set the following variables.
# input folder where FASTA file is located
export inputFolder=${CSW_HOME}/tests/bash/docker_tests/input
# protein name root ( in our example the FASTA file is 1pazA.fasta )
export prot="1pazA"
# output folder
export outputFolder=${CSW_HOME}/tests/bash/docker_tests/output
# CPU threads and maximum RAM (GB) to be used
export CPUnum=10
export maxRAM=16
# location of the protein database to be use for generatig sequence profiles
export RaptorxDBfolder=/path/to/uniprot20_2016_02
# for Psipred & DisoPred the recommended db is Uniref90 or Uniref50
export DBfolder=/path/to/uniref50
export DBname=uniref50.fastaAlter setting the variables, you cand run the all modules test :
bash ${CSW_HOME}/tests/bash/docker_tests/test_ALL_modules.shIf you are interested in a particular module only, separate tests are provided in ${CSW_HOME}/tests/bash/docker_tests/
In each predictor folder - ${outputFolder}/structural/scratch1d/expected_output/ samples of how the output should look are provided.
A1. RaptorX Protein Structure Property Prediction - from Xu group
The RaptorX-Property repo linked to the journal paper has been upgraded and split into 2 packages:
Docker image contains:
- Secondary structure (SS) predictions (SS3 & SS8 - 3 and 8 classes classification) [WLLX 2016], [WPMX 2016], [WSX 2016]
- Relative solvent accesibility (RSA)
- Disorder prediction - AUCpreD [WMX 2016], [WSX 2016]
- Transmembrane topology - TopoPred (TM2 & TM8 - 2 and 8 classes classification) [WG 2019]
Build docker image
cd ${CSW_HOME}/dockerfiles/structural/raptorx
docker build -t raptorx -f raptorx_property_cpu.dockerfile .Get protein sequence database, according to the sequence profile generator sofware you want to use (installed in the docker image):
-
hhblits (default):
-
jackhmm :
- uniref50 : ftp://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref50/uniref50.fasta.gz
- uniref90 : ftp://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref90/uniref90.fasta.gz
-
buildali2 : This should be mounted (or using simlinks) in "databases/nr_databases" (must contain nr90 and nr70). http://raptorx.uchicago.edu/download/
Usage example using bash :
For RaptorX the recommended db is uniprot20_2016_02 or uniclust30. For now, only the usage of uniprot20_2016_02 and hhsuite3 was tested. A sample of what the expected output should consist of is located in ${outputFolder}/structural/raptorx/expected_output/.
# location of the protein database to be use for generatig sequence profiles
export RaptorxDBfolder=/storage1/eliza/protDBs/uniprot20_2016_02
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/structural/raptorx:/output \
-v ${RaptorxDBfolder}:/home/TGT_Package/databases/uniprot20_2016_02 \
-e prot \
-e CPUnum \
-e maxRAM \
-it raptorx:latest bash -c '\
TGT_Package/A3M_TGT_Gen.sh -i /input/${prot}.fasta -h hhsuite3 -d uniprot20_2016_02 -c ${CPUnum} -m ${maxRAM} -o /output/; \
Predict_Property/Predict_Property.sh -i /output/${prot}.tgt -o /output/; 'A2. SCRATCH-1D Protein Predictor v1.2 & DisPRO1.0 - from Baldi group
Links:
Docker image contains 2 packages:
- SCRATCH-1D v1.2 [MB 2014], [CRSB 2005] :
- Secondary structure predictions (SSpro3 & SSpro8 - 3 and 8 classes classification)
- Relative solvent accesibility (ACCpro).
- DISpro1.0 Disorder prediction [CSB 2005]
Build docker image
cd ${CSW_HOME}/dockerfiles/structural/scratch1d
docker build -t scratch1d -f scratch1d.dockerfile .Usage example using bash. A sample of what the expected output should consist of is located in ${outputFolder}/structural/scratch1d/expected_output/.
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/structural/scratch1d:/output \
-e prot \
-e CPUnum \
-it scratch1d:latest bash -c '\
SCRATCH-1D_1.2/bin/run_SCRATCH-1D_predictors.sh /input/${prot}.fasta /output/${prot} ${CPUnum} ;'
A3. Psipred - from UCL Bioinformatics group
Docker image contains:
- PSIPRED Protein Secondary Structure Predictor v4.0 (github repo) [BJ 2019], [J 1999].
Build docker image:
cd ${CSW_HOME}/dockerfiles/structural/psipred
docker build -t psipred -f psipred.dockerfile .This docker image will use BLAST+ for building the sequence profile. Therefore we need to download and setup a sequence database. Psipred recomands the usage of UniRef90. For more details visit their documentation (link above). Download UniRef50/UniRef90 in fasta format from uniprot.org/downloads.
Afterwards a blast database needs to be created (this steps need to be done only once, afterwards the database can be used or moved anywhere):
# if you do not habe BLAST+ installed run:
sudo apt-get install
# go to the place where Uniref fasta file is being stored (change the path bellow accordingly):
cd /Place/where/UnirefX.fasta/file/is/stored
# create database (this might take a while from several minutes to one hour)
makeblastdb -dbtype prot -in uniref90.fastaAfter the BLAST+ database has been generated, from now on we can use the docker image anytime. Let's see an usage example using bash and also test that everything works as expected :
Let's set some custom variables:
The path to the folder where uniref is being stored (change it according to you case):
export DBfolder=/path/to/protDBs/uniref50
export DBname=uniref50.fasta Let's run Psipred ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow ). Sample output for comparison is located in ${outputFolder}/structural/psipred/expected_output/
docker run \
-v ${outputFolder}/structural/psipred:/output \
-v ${inputFolder}:/input \
-v ${DBfolder}:/home/database \
-e DBfolder=/home/database \
-e DBname \
-e prot \
-e CPUnum \
-it psipred:latest \
bash -c '\
cp /input/${prot}.fasta /output/ && cd /output/ ; \
$psipredplus /output/${prot}.fasta;'A4. Disopred - from UCL Bioinformatics group
Docker image contains:
- DISOPRED Disorder Predictor v3.1 (github repo) [JC 2014]
Build docker image:
cd ${CSW_HOME}/dockerfiles/structural/disopred
docker build -t disopred -f disopred .dockerfile .This docker image will use BLAST+ for building the sequence profile. Therefore we need to download and setup a sequence database. Please proceed as specified for Psipred (just above)
Let's run disopred ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow ). Sample output for comparison is located in ${outputFolder}/structural/disopred/expected_output/
docker run \
-v ${outputFolder}/structural/disopred:/output \
-v ${inputFolder}:/input \
-v ${DBfolder}:/home/database \
-e DBfolder=/home/database \
-e DBname \
-e prot \
-e CPUnum \
-it disopred:latest \
bash -c '\
cp /input/${prot}.fasta /output/ && cd /output/ ; \
$disopredplus /output/${prot}.fasta;'### A5. [SPOT-1D predictors](https://sparks-lab.org/server/spot-1d/) - from Sparks Lab SPOT-1D [\[HZ 2019\]](#hz-2019) :exclamation: On progress
B1. NetPhos v3.1
Predicts serine, threonine or tyrosine phosphorylation sites in eukaryotic proteins, either generic or kinase specific (17 kinases) [BGB 1999], [BB 2004].
As all DTU predictors license is for academic and non-profit usage only, in order to download the software it is required to register on their website and accept the license agreement prior accessing the download page. Please register and download the above predictors (linux version as the dockerfile image is ubuntu based) from the Download link
After you complete the license agreement and download the software, you can proceed building the docker image:
cd ${CSW_HOME}/dockerfiles/phosphorylation/netphos-3.1
cp ${netphos_SOURCE}/netphos-3.1* ${CSW_HOME}/dockerfiles/phosphorylation/netphos-3.1/
docker build -t netphos-3.1 -f netphos-3.1.dockerfile . Let's run NetPhos3.1 ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow )
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/phosphorylation/netphos:/output \
-e prot \
-it netphos-3.1:latest \
bash -c '\
/home/netphos-3.1/ape-1.0/ape /input/${prot}.fasta > /output/${prot}.netphos.out; 'B2. NetPhospan v1.0
Predicts phophorylation sites from a set of 120 human kinase [FN 2018].
As all DTU predictors license is for academic and non-profit usage only, in order to download the software it is required to register on their website and accept the license agreement prior accessing the download page. Please register and download the above predictors (linux version as the dockerfile image is ubuntu based) from the Download link
After you complete the license agreement and download the software, you can proceed building the docker image:
cd ${CSW_HOME}/dockerfiles/phosphorylation/netphospan-1.0
cp ${netphospan_SOURCE}/netphospan-1.0* ${CSW_HOME}/dockerfiles/phosphorylation/netphospan-1.0/
docker build -t netphospan-1.0 -f netphospan-1.0.dockerfile . Let's run now NetPhospan for this protein example, using either the generic predictor (-generic flag), or a kinase specific model (-a kinasename). For a list of supported kinase models please see the documentation for this predictor :
# General predictor :
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/phosphorylation/netphospan:/output \
-e prot=$prot \
-it netphospan-1.0:latest \
bash -c '\
netphospan-1.0.Linux/netphospan -f /input/${prot}.fasta -generic > /output/${prot}.generic.netphospan.out;'
# Kinase specific :
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/phosphorylation/netphospan:/output \
-e prot=$prot \
-it netphospan-1.0:latest \
bash -c '\
netphospan-1.0.Linux/netphospan -f /input/${prot}.fasta -a PKACA > /output/${prot}.PKACA.netphospan.out;'MusiteDeep Phosphorylation (github repo) predicts general and/or kinase specific phosphorylation sites [WX 2017].
There are 2 available dokerfiles:
- MusiteDeep using Keras1 and Theano CPU-based
- MusiteDeep using Keras2 and Tensorflow CPU-based - which is much faster than Theano's version
According to your choice, build the selected docker image:
cd ${CSW_HOME}/dockerfiles/phosphorylation/musitedeep
docker build -t musitedeep_keras2_tensorflow_cpu -f musitedeep_keras2_tensorflow_cpu.dockerfile .
docker build -t musitedeep_keras1_theano_cpu -f musitedeep_keras1_theano_cpu.dockerfile .Let's see an usage example using bash and also test that everything works as expected. We will use the variables set above.
MusiteDeep contains a generic phosphorylation predictor, as well as trained kinase specific models (only for 'CDK','PKA','CK2', 'MAPK', 'PKC' kinases). Additionally it provides some custom models and the possibility to train custom models based on users data. Please see their documentation for detailed usage info.
Using MusiteDeep v1.0 (theano) :
Either the general predictor :
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/phosphorylation/musitedeep_keras1_theano:/output \
-e prot \
-it musitedeep_keras1_theano_cpu:latest \
bash -c '\
python predict.py -input /input/${prot}.fasta -output /output/ \
-predict-type general -residue-types S,T,Y ;'Or kinase specific:
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/phosphorylation/musitedeep_keras1_theano:/output \
-e prot \
-it musitedeep_keras1_theano_cpu:latest \
bash -c '\
python predict.py -input /input/${prot}.fasta -output /output/ \
-predict-type kinase -kinase CDK ;'Similarly for MusiteDeep v2.0 (tensorflow), only the image name needs to be change and the usage is almost equivalent :
For the general predictor :
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/phosphorylation/musitedeep_keras2_tensorflow:/output \
-e prot \
-it musitedeep_keras2_tensorflow_cpu:latest \
bash -c '\
python predict.py -input /input/${prot}.fasta -output /output/ \
-predict-type general -residue-types S,T,Y ;'Or Kinase specific :
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/phosphorylation/musitedeep_keras2_tensorflow:/output \
-e prot \
-it musitedeep_keras2_tensorflow_cpu:latest \
bash -c '\
python predict_batch.py -input /input/${prot}.fasta -output /output/ \
-predict-type kinase -kinase CDK ;'C1. NetNGlyc v1.0
Predicts N-Glycosylation sites in human proteins [GJB 2004]. CLI user guide
As all DTU predictors license is for academic and non-profit usage only, in order to download the software it is required to register on their website and accept the license agreement prior accessing the download page. Please register and download the above predictors (linux version as the dockerfile image is ubuntu based) from the Download link
After you complete the license agreement and download the software, you can proceed building the docker image:
cd ${CSW_HOME}/dockerfiles/phosphorylation/netnglyc-1.0d
cp ${netnglyc_SOURCE}/netnglyc-1* ${CSW_HOME}/dockerfiles/phosphorylation/netnglyc-1.0d/
cp ${signalp_SOURCE}/signalp* ${CSW_HOME}/dockerfiles/phosphorylation/netnglyc-1.0d/
docker build -t netnglyc-1.0d -f netnglyc-1.0d.dockerfile . Let's run NetNglyc ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow )
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/glycosylation/netnglyc:/output \
-e prot \
-it netnglyc-1.0d:latest \
bash -c '\
netNglyc /input/${prot}.fasta > /output/${prot}.netnglyc.out; '
C2. NetOGlyc v4.0
Predicts O-GalNAc (mucin type) glycosylation sites in mammalian proteins. [SC 2013]:
As all DTU predictors license is for academic and non-profit usage only, in order to download the software it is required to register on their website and accept the license agreement prior accessing the download page. Please register and download the above predictors (linux version as the dockerfile image is ubuntu based) from the Download link
After you complete the license agreement and download the software, you can proceed building the docker image:
cd ${CSW_HOME}/dockerfiles/phosphorylation/netoglyc-3.1
cp ${netoglyc_SOURCE}/netoglyc-3.1* ${CSW_HOME}/dockerfiles/phosphorylation/netoglyc-3.1/
cp ${signalp_SOURCE}/signalp* ${CSW_HOME}/dockerfiles/phosphorylation/netoglyc-3.1/
docker build -t netoglyc-3.1 -f netoglyc-3.1.dockerfile .Let's run NetOglyc ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow )
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/glycosylation/netoglyc:/output \
-e prot \
-it netoglyc-3.1:latest \
bash -c '\
netOglyc /input/${prot}.fasta > /output/${prot}.netoglyc.out; 'C3. NetCGlyc v1.0
Predicts tryptophan C-mannosylation sites in mammalian proteins [J 2007].
As all DTU predictors license is for academic and non-profit usage only, in order to download the software it is required to register on their website and accept the license agreement prior accessing the download page. Please register and download the above predictors (linux version as the dockerfile image is ubuntu based) from the Download link
After you complete the license agreement and download the software, you can proceed building the docker image:
cd ${CSW_HOME}/dockerfiles/phosphorylation/netcglyc-1.0c
cp ${netcglyc_SOURCE}/netcglyc-1* ${CSW_HOME}/dockerfiles/phosphorylation/netcglyc-1.0c/
docker build -t netcglyc-1.0c -f netcglyc-1.0c.dockerfile . Let's run NetCglyc ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow )
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/glycosylation/netcglyc:/output \
-e prot \
-it netcglyc-1.0c:latest \
bash -c '\
netCglyc /input/${prot}.fasta > /output/${prot}.netcglyc.out; '
C4. ISOGlyP
Predicts isoform specific mucin-type o-glycosylation sites [ML 2020]. Github repo - https://github.com/jonmohl/ISOGlyP
Building the docker image:
cd ${CSW_HOME}/dockerfiles/phosphorylation/isoglyp
docker build -t isoglyp -f isoglyp.dockerfile . Let's run ISOGlyP ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow )
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/glycosylation/isoglyp:/output \
-e prot \
-it isoglyp \
bash -c '\
isoglypCL.py -p /home/ISOGlyP/isoPara.txt -f /input/${prot}.fasta ; \
mv isoglyp-predictions.csv /output/${prot}.isoglyp.out; '❗ Only online predictors are currently used
❗ Only online predictors are currently used
❗ Only online predictors are currently used
G1. TMP-SSurface
Predicts RSA for transmembrane proteins using deep learning [LW 2019] Github repo - https://github.com/Liuzhe30/TMP-SSurface-2.0
Building the docker image:
cd ${CSW_HOME}/dockerfiles/localisation/tmp_ssurface
docker build -t tmp_ssurface -f tmp_ssurface.dockerfile .
Let's run TMP-SSurface ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow )
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/localisation/tmp_ssurface:/output \
-e prot \
-it tmp_ssurface:latest \
bash -c '\
python3 run.py -f /input/${prot}.fasta -p /input/pssm/ -o /output/ ;'
G2. TMHMM v2.0
Predicts transmembrane helices [KS 2001]
As all DTU predictors license is for academic and non-profit usage only, in order to download the software it is required to register on their website and accept the license agreement prior accessing the download page. Please register and download the above predictors (linux version as the dockerfile image is ubuntu based) from the Download link
After you complete the license agreement and download the software, you can proceed building the docker image:
cd ${CSW_HOME}/dockerfiles/localisation/tmhmm2
cp ${tmhmm_SOURCE}/tmhmm-2.0c* ${CSW_HOME}/dockerfiles/localisation/tmhmm2/
docker build -t tmhmm2 -f tmhmm2.dockerfile .
Let's run TMHMM ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow )
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/localisation/tmhmm2:/output \
-e prot \
-it tmhmm2:latest \
bash -c '\
tmhmm /input/${prot}.fasta > /output/${prot}.tmhmm2.out; \
cp TMHMM_*/* /output/;'
G3. MEMSAT-SVM
Predicts transmembrane regions and cellular localisation [NJ 2009] Github repo - https://github.com/psipred/MemSatSVM
Building the docker image:
cd ${CSW_HOME}/dockerfiles/localisation/memsatsvm
docker build -t memsatsvm -f memsatsvm.dockerfile .
Let's run MEMSAT-SVM ( no need to change anything as the variables used are being set above - just copy paste the whole command bellow )
docker run \
-v ${inputFolder}:/input \
-v ${outputFolder}/localisation/memsatsvm:/output \
-e prot \
-it memsatsvm:latest \
bash -c '\
./run_memsat-svm.pl -p 1 -g 0 -mtx 1 /input/pssm_mtx/${prot}.mtx -j /output/'
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