GROOLS Application

GROOLS is a powerful reasoner representing knowledge as graph and dealing with uncertainties and contradictions for predictions and expectations. This application is a standalone tool that illustrates a possible usage of GROOLS API.

How to cite

Method
Application

Biological application

This GROOLS application is a bioinformatics software that helps biologists in the evaluation of genome functional annotation through biological processes like metabolic pathways.

Two different resources are used to represent biological knowledge:

• Genome Properties
• UniPathway

Results

As a test case, the reasoner was launched on 14 prokaryotic genomes/proteomes using:

• UniPathway or Genome Properties as prior-knowledge to represent biological processes like metabolic pathways
• and protein annotations from MicroScope and UniProt.

Results of this test case are available here.

Installation

GROOLS application requires:

• java 1.8 or later
• dot from Graphviz

From release

Download and unzip GROOLS.zip file for the last realease

Build from source

git clone https://github.com/Grools/grools-application
pushd grools-application
bash build.sh
popd

The executable jar file will be located in grools-application/build/libs/ directory

Java library dependencies that are downloaded and installed

• grools-reasoner
• bio-scribe
• grools-genome-properties
• grools-obo
• grools-reporter

Usage

Usage from shell script

Three shell scripts are available to grab annotations from MicroScope or UniProt using UniPathway or Genome Properties as prior-knowledge, respectively.

UniProt TIGRFAM/PFAM predictions with Genome Properties

./scripts/uniprot_genpropToGrools.sh

UniProt annotations with UniPathway

./scripts/uniprot_upaToGrools.sh

MicroScope annotations with UniPathway

./scripts/microscope_upaToGrools.sh  

Usage from jar file

The application can be launched directly with the jar file, as follow:

java -jar  build/libs/grools-application-1.0.0.jar [-u/-g] observations.csv results_dir/

This application requires three parameters:

• file of observations (predictions and expectations) in GROOLS CSV format
• a directory to save results
• the option -g or -u to choose between Genome Properties (-g) or UniPathway (-u) as a resource of prior-knowledge.

GROOLS CSV file format

This file format allows user to declare observations that will be propagated on the prior-knowledge graph. The header of the CSV file should be:

Name;EvidenceFor;Type;isPresent;Source;Label;Description

Fields

• Name: is a unique identifier
• EvidenceFor: is the name of the related prior-knowledge
• Type: CURATION,EXPERIMENTATION,COMPUTATION
• isPresent: T or F (True/False)
• Source: is the origin of the given observation (e.g. UniProt, MicroScope, BIOLOG)
• Label: is a short description of the observation
• Description: is a complete description of the observation

Examples

Files containing Acinetobacter baylyi ADP1 observations for:

• UniProt (PFAM and TIGRFAM) and Biolog results related to Genome Properties (link)
• MicroScope (EC number, RHEA and MetaCyc reactions) and Biolog results related to UniPathway (link)

Tutorial using UniPathway as Prior-Knowledge graph

examples Directory contains a set of data to learn how to use GROOLS application:

examples/
├── biolog
│   ├── AbaylyiADP1
│   │   └── PM1_A_101117.tab
│   └── pk_observation_mapper
│       ├── biolog_plates_cells_name_evidencesForGenProp_description.tsv
│       └── biolog_plates_cells_name_evidencesForUPA_description.tsv
├── expectation_genprop_aa.csv
└── expectation_upa_aa.csv
└── organismName_sequencesId.csv

Describe expectations using organism background knowkedge

You may indicate a list of pathways that are expected according to organism background knowkedge. As example, a proptotroph organism should have all amino acid biosynthesis pathways: see file expectation_upa_aa.csv

$ cat examples/expectation_upa_aa.csv
Accession;Name
UPA00031;L-histidine biosynthesis
UPA00035;L-tryptophan biosynthesis
UPA00047;L-isoleucine biosynthesis
...

This file should be converted in GROOLS csv file format (see section GROOLS CSV file format) using:

$ ./scripts/expectations_list_to_grools_csv.sh examples/expectation_upa_aa.csv examples/expectation_upa_aa.grools.csv

This command generates a file named expectation_upa_aa.grools.csv:

"Name";"EvidenceFor";"Type";"isPresent";"Source";"Label";"Description"
"Exp_UPA00031";"UPA00031";"EXPERIMENTATION";"T";"";"Exp_UPA00031";"L-histidine biosynthesis"
"Exp_UPA00035";"UPA00035";"EXPERIMENTATION";"T";"";"Exp_UPA00035";"L-tryptophan biosynthesis"
"Exp_UPA00047";"UPA00047";"EXPERIMENTATION";"T";"";"Exp_UPA00047";"L-isoleucine biosynthesis"
...

🔶 Indeed you can directly write a such file.

Expectations from Biolog experimentations

Biolog results are representated with numerical values. These quantitative growth phenotypes data should be first discretized in three states FALSE (no growth), TRUE (growth), NA (growth maybe). For that, you may use the omp R package with "grofit" aggregation method and weak discretization (-a, -w and -z options of run_opm.R program).

See examples/biolog/AbaylyiADP1/PM1_A_101117.tab file as example of omp output:

"File"	"Plate_Type"	"Position"	"Setup_Time"	"Well"	"mu"	"lambda"	"A"	"AUC"	"mu_CI95_low"	"lambda_CI95_low"	"A_CI95_low"	"AUC_CI95_low"	"mu_CI95_high"	"lambda_CI95_high"	"A_CI95_high"	"AUC_CI95_high"	"Aggr_software"	"Aggr_version"	"Aggr_method"	"Discretized"	"Disc_software"	"Disc_version"	"Disc_method"
".//01_Abaylyi_PM1_A_101117.csv"	"PM01"	"15-A"	"Nov 17 2010 5:28 PM"	"A01"	11.0239490374402	0.101998280074103	50.0586437522741	4437.423792864	10.8538314459345	-51.4322872382767	50.2978431840183	4398.07750163227	16.5264325297181	85.9780629028097	53.0550158798986	4463.39457157138	"opm"	"1.3.51"	"grofit"	FALSE	"opm"	"1.3.51"	"kmeans"
".//01_Abaylyi_PM1_A_101117.csv"	"PM01"	"15-A"	"Nov 17 2010 5:28 PM"	"A02"	6.47772286994336	-1.17233134884521	50.2660273428748	4294.08252281865	5.46716638240377	-34.66207762553	50.201817207401	4251.13805006296	14.7111400864039	119.317927831985	54.03143182126	4316.55817358922	"opm"	"1.3.51"	"grofit"	FALSE	"opm"	"1.3.51"	"kmeans"
".//01_Abaylyi_PM1_A_101117.csv"	"PM01"	"15-A"	"Nov 17 2010 5:28 PM"	"A03"	5.4787776379894	-0.0367891891096806	29.6073109885561	2566.50462868851	3.31739323527425	-17.3502549023257	29.7878444150652	2537.08932176093	17.0850517869014	148.128379766773	34.4615301314378	2582.13004990163	"opm"	"1.3.51"	"grofit"	FALSE	"opm"	"1.3.51"	"kmeans"
...

To convert omp files in GROOLS csv file format, you may use biologToGroolsExpectations.py script as follow:

$ ./scripts/biologToGroolsExpectations.py examples/biolog/pk_observation_mapper/biolog_plates_cells_name_evidencesForUPA_description.tsv biolog_list_of_expectations_upa.csv  examples/biolog/AbaylyiADP1/*.tab

This command generates the biolog_list_of_expectations_upa.csv file

"Name";"EvidenceFor";"Type";"isPresent";"Source";"Label";"Description"
"PM01_A02_1";"UPA00463";"EXPERIMENTATION";"F";"BIOLOG experimentation: 101117";"L-arabinose degradation";"No growth with L-Arabinose as carbon source"
"PM01_A08_1";"UPA00532";"EXPERIMENTATION";"T";"BIOLOG experimentation: 101117";"L-proline degradation";"Growth with L-Proline as carbon source"
"PM01_A09_1";"UPA00043";"EXPERIMENTATION";"T";"BIOLOG experimentation: 101117";"D-alanine degradation";"Growth with D-Alanine as carbon source"
"PM01_B02_1";"UPA00812";"EXPERIMENTATION";"F";"BIOLOG experimentation: 101117";"D-sorbitol degradation";"No growth with D-Sorbitol as carbon source"
...

To merge expectation file

$ cp biolog_list_of_expectations_upa.csv complete_list_of_expecations_upa.csv
$ tail -n +2 examples/expectation_upa_aa.grools.csv  >> complete_list_of_expecations_upa.csv

Run GROOLS application

Using UniProt annotations

The proteom id of A. baylyi ADP1 is UP000000430 in UniProt.

Normal reasoning mode:

$ ./scripts/uniprot_upaToGrools.sh -o res/uniprot_upa_normal UP000000430 complete_list_of_expecations_upa.csv

Specific reasoning mode:

$ ./scripts/uniprot_upaToGrools.sh -s -o res/uniprot_upa_normal UP000000430 complete_list_of_expecations_upa.csv

Results are viewable with a browser, open: res/uniprot_upa_normal/index.html or res/uniprot_upa_specific/index.html

Using MicroScope annotations

The sequence id of A. baylyi ADP1 is 36 in MiscroScope platform.

The correspondence table Organism <-> Sequence ID is available into the csv file: organismName_sequencesId.csv .

Normal reasoning mode:

$ ./scripts/microscope_upaToGrools.sh -o res/microscope_upa_normal 36 complete_list_of_expecations_upa.csv

Specific reasoning mode:

$ ./scripts/microscope_upaToGrools.sh -s -o res/microscope_upa_specific 36 complete_list_of_expecations_upa.csv

Results are viewable with a browser, open: res/microscope_upa_normal/index.html or res/microscope_upa_specific/index.html