Cross Species Structural, Post translational modifications and Functional protein predictions workflow
Content summary:
- General Info
- Installation and Setup
- Running CWL workflows
- Prediction Modules Description
- Python API
- Known issues
- References
This project is a scalable workflow that receives either the protein FASTA file or a list of Uniprot IDs and runs a series of structural and phenotype related predictors, generating a knowledge dataset that will facilitate further exploration and comparisons according to the following categories of features: secondary structure, solvent accessibility, disordered regions, PTS modifications (phosphorylation, glycosylation, lipid modification, sumoylation, etc).
Currently there are 7 main modules that deal with different types of predictions :
- A. Structural related
- B. Phosphorylation
- C. Glycosylaytion
- D. Acetylation
- E. Sumoylation
- F. Lipid modification
- G. Cellular localisation
This repo intends to create an easy, user accesible and open-source tool for running a series of third party predictions software. Provided are :
- Dockerfiles for easy installing existing prediction software.
- Python API for facilitating parsing and organising each predictor's output data.
- CWL pipelines that facilitates large protein sequences sets prediction jobs submissions and parallelisation.
The project is under active development and it was tested so far only on native Ubuntu 18 & 20 .
To run CWL workflows you can use any CWL runner of your choice. By default (and also for the ease of debugging) we used CWLtool [AC 2017].
- Docker client:
- cwltool - click. It can be either downloaded or the user can just use the python environment.
- Python3.8 and above (Optional: for separately using the Python API outisde the CWL pipeline)
$ git clone https://github.com/eliza-m/CrossSpeciesWorkflow.git
Please set the CrossSpeciesWorkflow project home variable and add it to .bashrc :
$ export CSW_HOME=/path/to/CrossSpeciesWorkflow/project/home
$ cd CSW_HOME
Conda environment that has cwltool and python is also provided.
$ conda env create -f environment.yamlThe python code in this project is published as a conda package.
$ conda activate species_proteins
$ python setup.py installThis step can be skipped if you do not intend to use the Structural module. Only the structural predictors require downloading and setting up different protein databases. Links for download are provided bellow
- RaptorX
- Psipred & Disopred:
- UniRef50/UniRef90 FASTA format : http://uniprot.org/downloads.
Afterwards, for UniRef50/UniRef90 a blast database needs to be created (these 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 ncbi-blast+
# 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 uniref50.fastaFor easing the CWL pipeline submission, please create symbolic links with the downloaded & prepared databases:
$ ln -s /path/to/host/db/folder/uniprot20_2016_02 ${CWL_HOME}/databases/uniprot20_2016_02
$ ln -s /path/to/host/db/folder/uniref50 ${CWL_HOME}/databases/uniref50
The setup databases should contain:
$ ls ${CSW_HOME}/databases/uniprot20_2016_02
md5sum uniprot20_2016_02.cs219 uniprot20_2016_02_hhm_db.index
uniprot20_2016_02_a3m_db.index uniprot20_2016_02_cs219.ffdata uniprot20_2016_02_hhm.ffdata
uniprot20_2016_02_a3m.ffdata uniprot20_2016_02_cs219.ffindex uniprot20_2016_02_hhm.ffindex
uniprot20_2016_02_a3m.ffindex uniprot20_2016_02.cs219.sizes
$ ls ${CSW_HOME}/databases/uniref50
uniref50.fasta uniref50.fasta.02.pin uniref50.fasta.05.phr uniref50.fasta.07.psq
uniref50.fasta.00.phr uniref50.fasta.02.psq uniref50.fasta.05.pin uniref50.fasta.08.phr
uniref50.fasta.00.pin uniref50.fasta.03.phr uniref50.fasta.05.psq uniref50.fasta.08.pin
uniref50.fasta.00.psq uniref50.fasta.03.pin uniref50.fasta.06.phr uniref50.fasta.08.psq
uniref50.fasta.01.phr uniref50.fasta.03.psq uniref50.fasta.06.pin uniref50.fasta.pal
uniref50.fasta.01.pin uniref50.fasta.04.phr uniref50.fasta.06.psq uniref50.release_note.txt
uniref50.fasta.01.psq uniref50.fasta.04.pin uniref50.fasta.07.phr
uniref50.fasta.02.phr uniref50.fasta.04.psq uniref50.fasta.07.pin
CWL workflow scripts provide a easy on-step way to run a series of multi step operations in a scalable and parallelised fashion. Running such a workflow requires:
- a workflow manager (we use cwltool)
- a cwl script (found in
${CSW_HOME}/cwldirectory) that defines the workflow procedure, paramaters and how inputs and outputs of different steps relate to each other. These are scripts do not need editing when using different input data or parameters. - input file - YML or JSON (examples can be found in
${CSW_HOME}/tests/cwl/test_modules/) that define where the input files are, values of mandatory / optional parameters.
The CWL workflow scripts are provided in ${CSW_HOME}/cwl directory, for each prediction type module, but also for an overall pipeline covering all modules.
Individual modules short names are :
- struct - Structural related
- phos - Phosphorylation
- glyc - Glycosylaytion
- acet - Acetylation
- sumo - Sumoylation
- loc - Cellular localisation
- lipid -Lipid modification
While the following refer to grouped predictions modules :
- ptm - All Post Translation Modification modules (acet + glyc + phos + sumo + lipid)
- all - All modules
Specific modules can be run individually using designated CWL workflow scripts found in their corresponding modules folder. Provided are tools for different input types such as:
- single protein FASTA : 1prot_$module_only_fasta.cwl
- single protein ID : 1prot_$module_only_id.cwl
- multi protein ID array : Nprot_$module_only_id.cwl
The disctinction between single & multi protein mode is that in multi protein mode, sequences are aligned using Clustal Omega in the final output layout.
Example YML input files are provided within ${CSW_HOME}/tests/cwl/test_modules/ for each input type (single protein FASTA or ID or a list of IDs).
Usage example for a specific module only for a list of protein IDs would be :
### INDIVIDUAL MODULES
# Structural
cwltool --no-match-user --no-read-only --outdir [path/to/dir] ${CSW_HOME}/cwl/structural/Nprot_struct_only_id.cwl [YML/JSON file]
# Acetylation
cwltool --no-match-user --no-read-only --outdir [path/to/dir] ${CSW_HOME}/cwl/acetylation/Nprot_acet_only_id.cwl [YML/JSON file]
# Glycosylation
cwltool --no-match-user --no-read-only --outdir [path/to/dir] ${CSW_HOME}/cwl/glycosylation/Nprot_glyc_only_id.cwl [YML/JSON file]
# Phosphorylation
cwltool --no-match-user --no-read-only --outdir [path/to/dir] ${CSW_HOME}/cwl/phosphorylation/Nprot_phos_only_id.cwl [YML/JSONfile]
# Lipid modification
cwltool --no-match-user --no-read-only --outdir [path/to/dir] ${CSW_HOME}/cwl/lipid/Nprot_lipid_only_id.cwl [YML/JSON file]
# Sumoylation
cwltool --no-match-user --no-read-only --outdir [path/to/dir] ${CSW_HOME}/cwl/sumoylation/Nprot_sumo_only_id.cwl [YML/JSON file]
# Localisation
cwltool --no-match-user --no-read-only --outdir [path/to/dir] ${CSW_HOME}/cwl/localisation/Nprot_loc_only_id.cwl [YML/JSON file]
### GROUPED MODULES
# PTM predictions
cwltool --no-match-user --no-read-only --outdir [path/to/dir] ${CSW_HOME}/cwl/Nprot_ptm_id.cwl [YML/JSON file]
# ALL
cwltool --no-match-user --no-read-only --outdir [path/to/dir] ${CSW_HOME}/cwl/Nprot_all_id.cwl [YML/JSON file]
All tests can be run simultaneously by running ${CSW_HOME}/tests/cwl/test_modules/test_all_module.sh.
Output samples for each cwl workflow are found in in module folder in expected_ourput directories.
$ cd ${CSW_HOME}/tests/cwl/test_modules/
$ bash test_all_module.sh
Current outputs are module based and consists of:
- a directory containing all raw predictions outputs named
$protname_$module_preds - a summary file in
tsvformat named$module_results.tsvthat contains all predictions output parsed and organised in a comparative manner.
A HTML/JSON output format is currently under development.
The summary tsv file for single protein format has a 3-row header. First 2 columns contain resids and amino acids. Next columns (3 to end) contain predictions ouput. the subheaders refer to :
- 1st - prediction method name
- 2nd - prediction type (such as STY-phosphorylation, Nter-acetylation, K-acetylation)
- 3rd - predictor specific details or conditions (such as a particular enzyme, or number of classes the prediction refers to). These are predictor specific, so the user needs to be aquinted with each individual prediction methods particularities and how data should be interpreted.
Example - acetylation module :
resid aa netacet gpspail gpspail gpspail gpspail gpspail gpspail gpspail
N-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet
CREBBP EP300 HAT1 KAT2A KAT2B KAT5 KAT8
1 M - - - - - - - -
2 T 0.482 - - - - - - -
3 E - - - - - - - -
4 Q - - - - - - - -
5 M - - - - - - - -
...
The summary tsv file for multi protein format has a 4-row header. First column contain alignment numbering Next N columns contain amino acids of the N sequences predicted. Column N+2 named 'Dif' shows positions where sequence differences exist in alignment (marked with * symbol). Next columns (from N+3 to end) contain predictions ouput. the subheaders refer to :
- 1st - predictor method name
- 2nd - prediction type (such as STY-phosphorylation, Nter-acetylation, K-acetylation)
- 3rd - predictor specific details or conditions (such as a particular enzyme, or number of classes the prediction refers to). These are predictor specific, so the user needs to be aquinted with each individual prediction methods particularities and how data should be interpreted.
- 4th - Protname / ProtID to which the prediction refers to.
Example - acetylation module:
alnid P63244 O42248 O18640 P38011 Dif netacet netacet netacet netacet gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail gpspail
N-acet N-acet N-acet N-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet K-acet
CREBBP CREBBP CREBBP CREBBP EP300 EP300 EP300 EP300 HAT1 HAT1 HAT1 HAT1 KAT2A KAT2A KAT2A KAT2A KAT2B KAT2B KAT2B KAT2B KAT5 KAT5 KAT5 KAT5 KAT8 KAT8 KAT8 KAT8
P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011
1 - - - M * - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
2 - - - A * - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
3 M M M S * - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
4 T T S N * 0.482 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
5 E E E E - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Example - structural module:
alnid P63244 O42248 O18640 P38011 Dif raptorx raptorx raptorx raptorx psipred psipred psipred psipred raptorx raptorx raptorx raptorx raptorx raptorx raptorx raptorx raptorx raptorx raptorx raptorx disopred disopred disopred disopred
SS SS SS SS SS SS SS SS SS SS SS SS ACC ACC ACC ACC DIS DIS DIS DIS DIS DIS DIS DIS
3-class 3-class 3-class 3-class 3-class 3-class 3-class 3-class 8-class 8-class 8-class 8-class 3-class 3-class 3-class 3-class 2-class 2-class 2-class 2-class 2-class 2-class 2-class 2-class
P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011 P63244 O42248 O18640 P38011
1 - - - M * C C C C C C C C C C C C E E E E D D D D - - - D
2 - - - A * C C C C C C C C C C C C E E E E D D D D - - - D
3 M M M S * C C C C C C C C C C C C E E E E D D D D D D D D
4 T T S N * C C C C C C C C C C C C E E E E D D D D - - - -
5 E E E E C C C C C C C C C C C C E E E E D D D - - - - -
6 Q Q T V * E C C E C C C C E C C E E E E M - D - - - - - -
7 M M L L * E E E E E E C E E E E E M M M M - - - - - - - -
...
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:
Predictions methods included:
- Secondary structure (SS) predictions (SS3 & SS8 - 3 and 8 classes classification) [WLLX 2016], [WPMX 2016], [WSX 2016]
- Relative solvent accesibility (RSA) 3 class classification
- Disorder prediction - AUCpreD [WMX 2016], [WSX 2016]
A2. Psipred - from UCL Bioinformatics group
Docker image contains PSIPRED Protein Secondary Structure Predictor v4.0 (github repo) [BJ 2019], [J 1999].
Predicts secondary structure (SS) predictions - 3 class prediction
A3. Disopred - from UCL Bioinformatics group
Docker image contains DISOPRED Disorder Predictor v3.1 (github repo) [JC 2014]
Predicts intrinsically disordered regions - 2 class prediction
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].
We integrated in the main worklow only the online submission mode.
However, also provided are a Dockerfile, as well python parsers and cwl tools to run NetPhos locally. Please see CONTAINERS.md for details and examples.
B2. NetPhospan v1.0
Predicts phophorylation sites from a set of 120 human kinase [FN 2018].
We integrated in the main worklow only the online submission mode with generic type mode, due to the large number of available kinases models.
However, also provided are a Dockerfile, as well python parsers and cwl tools to run NetPhosPan locally. Please see CONTAINERS.md for details and examples.
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 (used in CWL wf)
Predicts phophorylation sites at S/T/Y residues, in both generic or kinases specific mode (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.
Integrated in the main workflow are currently only generic type predictions at S, T and Y residues.
C1. NetNGlyc v1.0
Predicts N-Glycosylation sites in human proteins [GJB 2004]. CLI user guide
C2. NGlyDE
Predicts N-Glycosylation sites [PS 2019].
C3. NetOGlyc v4.0
Predicts O-GalNAc (mucin type) glycosylation sites in mammalian proteins. [SC 2013]:
C4. ISOGlyP
Predicts isoform specific mucin-type o-glycosylation sites [ML 2020]. Github repo - https://github.com/jonmohl/ISOGlyP
C5. NetCGlyc v1.0
Predicts tryptophan C-mannosylation sites in mammalian proteins [J 2007].
C6. GlycoMine
Predicts C-, N- and O-linked glycosylation sites. [LS 2015].
Only for ISOGlyP, the main workflow uses the docker images, while the rest of the predictions are submitted online.
However, for NetNGlyc, NetCGlyc and NetOGlyc, also provided are a Dockerfile, as well python parsers and cwl tools to run them locally. Please see CONTAINERS.md for details and examples.
D1. NetAcet v1.0
Predicts substrates Nε-lysine acetylation sites. [KB 2005].
D2. GPSpail
Predicts N-Glycosylation sites [DX 2016].
Only online submitted predictions are currently available in the main workflow
E1. SUMOgo
Predicts sumoylation (SUMO) lysine sites. [CC 2018].
E2. GPSpail
Predicts sumoylation sites (SUMO) and SUMO-interaction Motifs (SIM) [ZR 2014].
Only online submitted predictions are currently available in the main workflow
F1. GPSlipid
Predicts lipid modification sites [XR 2016]
Included are specific lipif PTMs predictors refering to:
- N-Myristoylation
- S-Palmitoylation
- S-Farnesylation
- S-Geranylgeranylation
Only online submitted predictions are currently available in the main workflow
G1. TMHMM v2.0
Predicts transmembrane helices and orientation [KS 2001]
G2. TMpred
Predicts transmembrane helices and orientation [HS 1993]
Only online submitted predictions are currently available in the main workflow
for TMHMM, also provided are a Dockerfile, as well python parsers and cwl tools to run them locally. Please see CONTAINERS.md for details and examples.
The Python API is used directly by the CWL workflow. While completely optional, users can find convenient to use the Python API directly for particular purposes, such as the ones described bellow.
A simplist selection of the cwl tool can be performed directly from Python API. However it offers a very limited number of options, therefore we recommend using directly CWLtool or other workflow runner
# Full pipeline & one protein example
$ python species_proteins/workflow/run.py pipeline --cwlinput tests/cwl/test_modules/1prot_id.yml --mode single
--module all --outdir [path/to/dir]
# Acetylation module & multi protein example
$ python species_proteins/workflow/run.py pipeline --cwlinput tests/cwl/test_modules/Nprot_id.yml --mode multi
--module acet --outdir [path/to/dir] ``` For other options, see help menu:
$ python species_proteins/workflow/run.py pipeline --help
Usage: run.py pipeline [OPTIONS]
Simple wrapper for choosing which CWL workflow to run. For more options
uses directly cwltool or a different workflow runner.
Options:
--cwlinput TEXT YML or JSON input file with Uniprot IDs. See provided
examples in /tests folder [required]
--mode TEXT "single" or "multi" protein analysis [required]
--module TEXT Prediction module - accepted values:
- all : All modules
- ptm : All Post Translation modifications ( glyc + acet + phos + sumo + lipid)
- struct : Structural module
- glyc : Glycosylation module
- phos : Phosphorylation module
- acet : Acetylation module
- lipid : Lipid modification module
- sumo : Sumoylation module
- loc : Cellular localisation module [required]
--outdir TEXT Output directory. Default: current directory.
--args TEXT Argumments to be passed to cwltool. [default: --no-match-
user --no-read-only]
--parallel Run in parallel. NOT recommended due to HTTP response
errors !!! [default: False]
--help Show this message and exit.
Let's assume that you have already performed an "all" module prediction pipeline for your proteins and you want to generate a formatted output only for a specific sequence and only for glycosylation related predictors.
$ python species_proteins/workflow/run.py format-output --format single --module glyc
--inputfolder [path/to/predictions/dir] --protname YourID
As a input folder you can provide directly the overall prediction folder (that containes multiple proteins predictions) and the provided ID will be searched for (prediction output naming is $protID.predictor.*), but if you provide directly the folder of the desired protein the --protname argument is no longer needed.
For other options, see help menu:
$ python species_proteins/workflow/run.py format-output --h
elp
Usage: run.py format-output [OPTIONS]
Generates a vertical formatted layout of all predicted outputs. If 'multi'
protein format is selected, sequences are shown aligned.
Options:
--format TEXT "single" or "multi" protein layout [required]
--module TEXT Prediction module - accepted values:
- all : All modules
- ptm : All Post Translation modifications ( glyc + acet + phos + sumo + lipid)
- struct : Structural module
- glyc : Glycosylation module
- phos : Phosphorylation module
- acet : Acetylation module
- lipid : Lipid modification module
- sumo : Sumoylation module
- loc : Cellular localisation module [required]
--inputfolder TEXT Input folder where all prediction results are stored
[required]
--output TEXT Output formatted file [required]
--signif Print only significant predicted sites. It applies only
for PTM predictors (significance thresholds are method
specific.) [default: False]
--protname TEXT Only for single protein format, when within the specified input
folder there are multiple files with the same extension, a basename of the protein should be provided.
Example: protname.predictor.out; Default: null
--alnfile TEXT Required for "multi" protein layout.
--help Show this message and exit.
Some of the predictors can be used only on their webserver. We provide a easy to use tool to directly submit a prediction job on predictor's webserver via POST & GET.
Usage example:
$ python species_proteins/workflow/run.py submit-online --input MyPROT.fasta --predictor gpslipid --output MyPROT.gpslipid.html
Some online predictors accept multiFASTA input, some do not...
Other options:
$ python species_proteins/workflow/run.py submit-online --h
elp
Usage: run.py submit-online [OPTIONS]
Submit online jobs for a given predictor
Options:
--input TEXT Input FASTA file [required]
--output TEXT Output filename [required]
--predictor TEXT Online predictor to submit sequence to. Predictors list per categories :
- Glycosilation: 'netcglyc', 'netnglyc', 'netoglyc', 'glycomine', 'nglyde'
- Acetylation: 'netacet', 'gpspail'
- Phosphorylation: 'netphos', 'netphospan'
- Lipid modification: 'gpslipid'
- Sumoylation: 'gpssumo', 'sumogo'
- Cellular localisation: 'tmhmm', 'tmpred' [required]
--type TEXT Additional arguments to be passed; predictor specific
(glycomine: "N" or "C" or "O" glicosylation)
--help Show this message and exit.
IMPORTANT !!!:
Some online predictors forms do not handle well commonly used FASTA headers, therefore we recommend using as header directly the protein name (no spaces) or ID. Example: >LEUK_RAT or '>P12345'.
Also, in order to easily use the provided parsers for each predictor, we recommend to use prediction output filenames that follow the rule $protname.$predictor.* Examples: 'LEUK_RAT.netnglyc.html', 'P12345.nglyde.out', etc.
To ease FASTA files retrieval and manipulation, you can use the provided get-fasta CLI function. An example of retrieving sequences of a protein ID and trimming the header youd be:
$ python species_proteins/workflow/run.py get-fasta --uniprot P12345 --trimheader
Other options
$ python species_proteins/workflow/run.py get-fasta --help
Usage: run.py get-fasta [OPTIONS]
Retrieves fasta file from UniprotKB ID
Options:
--uniprot TEXT Uniprot ID to fetch [required]
--filename TEXT Filename to save. Default $id.fasta
--trimheader Trimm header to contain only id [default: False]
--mode TEXT Write("w") or append("a") mode [default: w]
--help Show this message and exit.
Found in ${CSW_HOME}/species_proteins the API is structured on a module basis. Each module folder contains:
- Predictor classes
$predictor_data.pythat deal with input preparation, raw outputs parsing and, for a series of predictors, online job submission. - Module classes
$module_pred.py(inherit Module_pred base class) deal with :- raw output paths retrieval
- organizing multiple predictors results
- printing results in comparative layout.
CWL workflow related :
- some code duplication due to CWL v1.0 limitations. CWL v1.2 (currently under development) will support IF operator and we will address this when v1.2 becomes stable.
- The cwltool flags
--no-match-userand--no-read-onlyare necessary due to permissions issues (because some of the predictors require generating or editing files in specific locations)
Output related:
- right now only
tsvoutputs are supported, html/json versions are in development.
Contains all reference found within the repo.
Amstutz, P, Crusoe, MR, Singh, M, Kumar, K, Chilton, J, [Unknown], B, Soiland-Reyes, S, Chapman, B, Kotliar, M, Leehr, D, Carrasco, G, Kartashov, A, Tijanic, N, Ménager, H, Safont, PR, Porter, JJ, Molenaar, G, Yuen, D, Barrera, A, Ivkovic, S, Spangler, R, [Unknown], P, Tanjo, T, Vandewege, W, Randall, JC, Kern, J, Bradley, J, Li, J, der Zwaan, JV & Connelly, A, common-workflow-language/cwltool, 2017, Software, GitHub, GitHub. https://github.com/common-workflow-language/cwltool/releases/tag/1.0.20170828135420
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