README.md

met4j-toolbox

Met4j command-line toolbox for metabolic networks

Installation from source

git clone https://forgemia.inra.fr/metexplore/met4j.git;
cd met4j;
mvn clean install 

cd met4j-toolbox
mvn clean package

Download executable jar from gitlab registry

The executable jar is downloadable in the met4j gitlab registry.

Usage

The toolbox can be launched using

java -jar target/met4j-toolbox-<version>.jar

which will list all the contained applications that can be called using

java -cp target/met4j-toolbox-<version>.jar <Package>.<App name> -h

Log4j from jsbml can be very verbose. You can make it silent by adding this command :

java -Dlog4j.configuration= -cp target/met4j-toolbox-<version>.jar ...

From singularity

You need at least singularity v3.5.

singularity pull met4j-toolbox.sif oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:latest

If you want a specific version:

singularity pull met4j-toolbox.sif oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:x.y.z

If you want the last develop version:

singularity pull met4j-toolbox.sif oras://registry.forgemia.inra.fr/metexplore/met4j/met4j-singularity:develop

If you want to build by yourself the singularity image:

cd met4j-toolbox
mvn package
cd ../
singularity build met4j-toolbox.sif met4j.singularity

This will download a singularity container met4j-toolbox.sif that you can directly launch.

To list all the apps.

met4j-toolbox.sif 

To launch a specific app, prefix its name with the last component of its package name. For instance:

met4j-toolbox.sif convert.Tab2Sbml -h -in fic.tsv -sbml fic.sbml

By default, singularity does not see the directories that are not descendants of your home directory. To get the directories outside your home directory, you have to specify the SINGULARITY_BIND environment variable. At least, to get the data in the default reference directory, you have to specify: In bash:

export SINGULARITY_BIND=/db

In csh or in tcsh

setenv SINGULARITY_BIND /db

From docker

First install Docker.

Pull the latest met4j image:

sudo docker pull metexplore/met4j:latest

If you want a specific version:

sudo docker pull metexplore/met4j:x.y.z

If you want the develop version:

sudo docker pull metexplore/met4j:develop

If you want to build by yourself the docker image:

cd met4j-toolbox
mvn package
cd ../
sudo docker build -t metexplore/met4j:myversion .

To list all the apps:

sudo docker run metexplore/met4j:latest met4j.sh

Don't forget to map volumes when you want to process local files. Example:

sudo docker run -v /home/lcottret/work:/work \
 metexplore/met4j:latest met4j.sh convert.Sbml2Tab \
 -in /work/toy_model.xml -out /work/toy_model.tsv

If you change the working directory, you have to specify "sh /usr/bin/met4j.sh":

sudo docker run -w /work -v /home/lcottret/work:/work \
 metexplore/met4j:latest sh /usr/bin/met4j.sh convert.Sbml2Tab \
 -in toy_model.xml -out toy_model.tsv

Galaxy instance

Galaxy wrappers for met4j-toolbox apps are available in the Galaxy toolshed (master version) and in the Galaxy test toolsdhed (develop version). Wrappers launch the met4j singularity container, so the server where your Galaxy instance is hosted must have Singularity installed.

Features

Package fr.inrae.toulouse.metexplore.met4j_toolbox

GenerateGalaxyFiles

Create the galaxy file tree containing met4j-toolbox app wrappers more Create the galaxy file tree containing met4j-toolbox app wrappers Creates a directory for each app with inside the galaxy xml wrapper. -h : prints the help (default: false) -o VAL : output directory where the galaxy wrappers and the tool_conf.xml will be written (directory tools of the Galaxy directory -p [Docker | Singularity] : Package type (default: Singularity) -v VAL : Met4j version (default: MET4J_VERSION_TEST)

Package fr.inrae.toulouse.metexplore.met4j_toolbox.attributes
ExtractAnnotations	Extract databases' references from SBML annotations or notes. more Extract databases' references from SBML annotations or notes. The references are exported as a tabulated file with one column with the SBML compound, reaction or gene identifiers, and one column with the corresponding database identifier.The name of the targeted database need to be provided under the same form than the one used in the notes field or the identifiers.org uri. -db VAL : name of the referenced database to export annotations from, as listed in notes or identifiers.org base uri -export [METABOLITE | REACTION | GENE] : the type of entity to extract annotation, either metabolite, reaction, or gene -h : prints the help (default: false) -i VAL : input SBML file -o VAL : output file path -skip : Skip entities without the selected annotations, by default output them with NA value (default: false) -uniq : keep only one identifier if multiple are referenced for the same entity (default: false)
ExtractPathways	Extract pathway(s) from a SBML file and create a sub-network SBML file more Extract pathway(s) from a SBML file and create a sub-network SBML file -h : prints the help (default: false) -i VAL : input SBML file -o VAL : output SBML file -p VAL : pathway identifiers, separated by "+" sign if more than one
GetEntities	Parse a SBML file to return a list of entities composing the network: metabolites, reactions, genes and others. more Parse a SBML file to return a list of entities composing the network: metabolites, reactions, genes and others.The output file is a tabulated file with two columns, one with entity identifiers, and one with the entity type. If no entity type is selected, all of them are returned by default. Only identifiers are written, attributes can be extracted from dedicated apps or from the Sbml2Tab app. -c (--compartments) : Extract Compartments (default: false) -g (--genes) : Extract Genes (default: false) -h : prints the help (default: false) -i VAL : Input SBML file -m (--metabolites) : Extract Metabolites (default: false) -nt (--noTypeCol) : Do not write type column (default: false) -o VAL : Output file -p (--pathways) : Extract Pathways (default: false) -r (--reactions) : Extract Reactions (default: false)
GetGenesFromReactions	Get gene lists from a list of reactions and a SBML file. more Get associated gene list from a list of reactions and a SBML file. Parse SBML GPR annotations and output a tab-separated file with one row per gene, associated reaction identifiers from input file in first column, gene identifiers in second column. -col N : Column number in reaction file (first as 1) (default: 1) -h : prints the help (default: false) -header : Skip reaction file header (default: false) -i VAL : Input SBML file -o VAL : Output file -r VAL : Input Reaction file -sep VAL : Separator in reaction file (default: )
GetMetaboliteAttributes	Create a tabulated file with metabolite attributes from a SBML file more Create a tabulated file with metabolite attributes from a SBML file -h : prints the help (default: false) -i VAL : Input SBML file -o VAL : Output file
GetReactantsFromReactions	Get reactant lists from a list of reactions and a SBML file. more Get reactant lists from a list of reactions and a Sbml file. Output a tab-separated file with one row per reactant, reaction identifiers in first column, reactant identifiers in second column. It can provides substrates, products, or both (by default). In the case of reversible reactions, all reactants are considered as both substrates and products -col N : Column number in reaction file (first as 1) (default: 1) -h : prints the help (default: false) -header : Skip reaction file header (default: false) -i VAL : Input SBML file -o VAL : Output file -p (--products) : Extract products only (default: false) -r VAL : Input Reaction file -s (--substrates) : Extract substrates only (default: false) -sep VAL : Separator in reaction file (default: )
SetCharges	Set charge to metabolites in a SBML file from a tabulated file containing the metabolite ids and the charges more Set charge to metabolites in a SBML file from a tabulated file containing the metabolite ids and the charges The charge must be a number. The ids must correspond between the tabulated file and the SBML file. If prefix or suffix is different in the SBML file, use the -p or the -s options. The charge will be written in the SBML file in two locations:+ - in the reaction notes (e.g. charge: -1) - as fbc attribute (e.g. fbc:charge="1") References: Olivier et al.; SBML Level 3 Package: Flux Balance Constraints version 2; Journal of Integrative Bioinformatics; 2018 -c VAL : [#] Comment String in the tabulated file. The lines beginning by this string won't be read (default: #) -cc N : [2] number of the column where are the charges (default: 2) -ci N : [1] number of the column where are the metabolite ids (default: 1) -h : prints the help (default: false) -i VAL : Original SBML file -n N : [0] Number of lines to skip at the beginning of the tabulated file (default: 0) -o VAL : [out.sbml] SBML output file (default: out.sbml) -p : [deactivated] To match the objects in the sbml file, adds the prefix M_ to metabolite ids (default: false) -s : [deactivated] To match the objects in the sbml file, adds the suffix _comparmentID to metabolites (default: false) -tab VAL : Input Tabulated file
SetChemicalFormulas	Set Formula to network metabolites from a tabulated file containing the metabolite ids and the formulas more Set Formula to network metabolites from a tabulated file containing the metabolite ids and the formulas The ids must correspond between the tabulated file and the SBML file. If prefix or suffix is different in the SBML file, use the -p or the -s options. The formula will be written in the SBML file in two locations:+ - in the metabolite HTML notes (e.g. formula: C16H29O2) - as a fbc attribute (e.g. fbc:chemicalFormula="C16H29O2") References: Olivier et al.; SBML Level 3 Package: Flux Balance Constraints version 2; Journal of Integrative Bioinformatics; 2018 -c VAL : [#] Comment String in the tabulated file. The lines beginning by this string won't be read (default: #) -cf N : [2] number of the column where are the formulas (default: 2) -ci N : [1] number of the column where are the metabolite ids (default: 1) -h : prints the help (default: false) -i VAL : Original SBML file -n N : [0] Number of lines to skip at the beginning of the tabulated file (default: 0) -o VAL : [out.sbml] SBML output file (default: out.sbml) -p : [deactivated] To match the objects in the sbml file, adds the prefix M_ to metabolite ids (default: false) -s : [deactivated] To match the objects in the sbml file, adds the suffix _comparmentID to metabolites (default: false) -tab VAL : Input Tabulated file
SetEcNumbers	Set EC numbers to reactions in a SBML file from a tabulated file containing the reaction ids and the EC numbers more Set EC numbers to reactions in a SBML file from a tabulated file containing the reaction ids and the EC numbers The ids must correspond between the tabulated file and the SBML file. If prefix R_ is present in the ids in the SBML file and not in the tabulated file, use the -p option. The EC will be written in the SBML file in two locations: - in the reaction HTML notes (e.g. EC_NUMBER: 2.4.2.14) - as a reaction MIRIAM annotation (see https://pubmed.ncbi.nlm.nih.gov/16333295/) with ec-code identifiers link (https://registry.identifiers.org/registry/ec-code) References: Novère et al.; Minimum information requested in the annotation of biochemical models (MIRIAM); Nature Biotechnology; 2005 -c VAL : [#] Comment String in the tabulated file. The lines beginning by this string won't be read (default: #) -cec N : [2] number of the column where are the ecs (default: 2) -ci N : [1] number of the column where are the reaction ids (default: 1) -h : prints the help (default: false) -i VAL : Original SBML file -n N : [0] Number of lines to skip at the beginning of the tabulated file (default: 0) -o VAL : [out.sbml] SBML output file (default: out.sbml) -p : [deactivated] To match the objects in the sbml file, adds the prefix R_ to reactions (default: false) -tab VAL : Input Tabulated file
SetGprs	Create a new SBML file from an original sbml file and a tabulated file containing reaction ids and Gene association written in a cobra way more Create a new SBML file from an original sbml file and a tabulated file containing reaction ids and Gene association written in a cobra way The ids must correspond between the tabulated file and the SBML file. If prefix R_ is present in the ids in the SBML file and not in the tabulated file, use the -p option. GPR must be written in a cobra way in the tabulated file as described in Schellenberger et al 2011 Nature Protocols 6(9):1290-307 (The GPR will be written in the SBML file in two locations: - in the reaction html notes (GENE_ASSOCIATION: ( XC_0401 ) OR ( XC_3282 )) - as fbc gene product association (see FBC package specifications: https://doi.org/10.1515/jib-2017-0082) References: Schellenberger et al.; Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0; Nature Protocols; 2011 Olivier et al.; SBML Level 3 Package: Flux Balance Constraints version 2; Journal of Integrative Bioinformatics; 2018 -c VAL : [#] Comment String in the tabulated file. The lines beginning by this string won't be read (default: #) -cgpr N : [2] number of the column where are the gprs (default: 2) -ci N : [1] number of the column where are the reaction ids (default: 1) -h : prints the help (default: false) -i VAL : Original SBML file -n N : [0] Number of lines to skip at the beginning of the tabulated file (default: 0) -o VAL : [out.sbml] SBML output file (default: out.sbml) -p : [deactivated] To match the objects in the sbml file, adds the prefix R_ to reactions (default: false) -tab VAL : Input Tabulated file
SetIds	Set new ids to network objects in a SBML file from a tabulated file containing the old ids and the new ids more Set new ids to network objects in a SBML file from a tabulated file containing the old ids and the new ids The ids must correspond between the tabulated file and the SBML file. If prefix or suffix is different in the SBML file, use the -p or the -s options. -c VAL : [#] Comment String in the tabulated file. The lines beginning by this string won't be read (default: #) -ci N : [1] number of the column where are the object ids (default: 1) -cnew N : [2] number of the column where are the new ids (default: 2) -h : prints the help (default: false) -i VAL : Original SBML file -n N : [0] Number of lines to skip at the beginning of the tabulated file (default: 0) -o VAL : [out.sbml] SBML output file (default: out.sbml) -p : [deactivated] To match the objects in the sbml file, adds the prefix R_ to reactions and M_ to metabolites (default: false) -s : [deactivated] To match the objects in the sbml file, adds the suffix _comparmentID to metabolites (default: false) -t [REACTION | METABOLITE | GENE | : [REACTION] Object type in the column PROTEIN | PATHWAY | COMPARTMENT] id : REACTION;METABOLITE;GENE;PATHWAY (default: REACTION) -tab VAL : Input Tabulated file
SetNames	Set names to network objects in a SBML file from a tabulated file containing the object ids and the names more Set names to network objects in a SBML file from a tabulated file containing the object ids and the names The ids must correspond between the tabulated file and the SBML file. If prefix or suffix is different in the SBML file, use the -p or the -s options. -c VAL : [#] Comment String in the tabulated file. The lines beginning by this string won't be read (default: #) -ci N : [1] number of the column where are the object ids (default: 1) -cname N : [2] number of the column where are the names (default: 2) -h : prints the help (default: false) -i VAL : Original SBML file -n N : [0] Number of lines to skip at the beginning of the tabulated file (default: 0) -o VAL : [out.sbml] SBML output file (default: out.sbml) -p : [deactivated] To match the objects in the sbml file, adds the prefix R_ to reactions and M_ to metabolites (default: false) -s : [deactivated] To match the objects in the sbml file, adds the suffix _comparmentID to metabolites (default: false) -t [REACTION | METABOLITE | GENE | : [REACTION] Object type in the column PROTEIN | PATHWAY | COMPARTMENT] id : REACTION;METABOLITE;GENE;PATHWAY (default: REACTION) -tab VAL : Input Tabulated file
SetPathways	Set pathway to reactions in a network from a tabulated file containing the reaction ids and the pathways more Set pathway to reactions in a network from a tabulated file containing the reaction ids and the pathways The ids must correspond between the tabulated file and the SBML file. If prefix R_ is present in the ids in the SBML file and not in the tabulated file, use the -p option. Pathways will be written in the SBML file in two ways:- as reaction note (e.g. SUBSYSTEM: purine_biosynthesis)- as SBML group (see Group package specifications: https://pmc.ncbi.nlm.nih.gov/articles/PMC5451322/) References: Hucka et al.; SBML Level 3 package: Groups, Version 1 Release 1; Journal of Integrative Bioinformatics; 2016 -c VAL : [#] Comment String in the tabulated file. The lines beginning by this string won't be read (default: #) -ci N : [1] number of the column where are the reaction ids (default: 1) -cp N : [2] number of the column where are the pathways (default: 2) -h : prints the help (default: false) -i VAL : Original SBML file -n N : [0] Number of lines to skip at the beginning of the tabulated file (default: 0) -o VAL : [out.sbml] SBML output file (default: out.sbml) -p : [deactivated] To match the objects in the sbml file, adds the prefix R_ to reactions (default: false) -sep VAL : [|] Separator of pathways in the tabulated file (default: |) -tab VAL : Input Tabulated file
SetReferences	Add references to network objects in a SBML file from a tabulated file containing the metabolite ids and the references more Add references to network objects in a SBML file from a tabulated file containing the metabolite ids and the references Reference name given as parameter (-ref) must correspond to an existing id in the registry of identifiers.org (https://registry.identifiers.org/registry) The corresponding key:value pair will be written as metabolite or reaction MIRIAM annotation (see https://pubmed.ncbi.nlm.nih.gov/16333295/) References: Novère et al.; Minimum information requested in the annotation of biochemical models (MIRIAM); Nature Biotechnology; 2005 -c VAL : [#] Comment String in the tabulated file. The lines beginning by this string won't be read (default: #) -ci N : [1] number of the column where are the object ids (default: 1) -cr N : [2] number of the column where are the references (default: 2) -h : prints the help (default: false) -i VAL : Original SBML file -n N : [0] Number of lines to skip at the beginning of the tabulated file (default: 0) -o VAL : [out.sbml] SBML output file (default: out.sbml) -p : [deactivated] To match the objects in the sbml file, adds the prefix R_ to reactions and M_ to metabolites (default: false) -ref VAL : Name of the reference. Must exist in identifiers.org (https://registry.iden tifiers.org/registry) -s : [deactivated] To match the objects in the sbml file, adds the suffix _comparmentID to metabolites (default: false) -t [REACTION | METABOLITE | GENE | : [REACTION] Object type in the column PROTEIN | PATHWAY | COMPARTMENT] id : REACTION;METABOLITE;GENE;PATHWAY (default: REACTION) -tab VAL : Input Tabulated file

Package fr.inrae.toulouse.metexplore.met4j_toolbox.bigg

GetBiggModelProteome

Get proteome in fasta format of a model present in the BIGG database more Get proteome in fasta format of a model present in the BIGG database References: King et al.; BiGG Models: A platform for integrating, standardizing and sharing genome-scale models; Nucleic Acids Research; 2016 -h : prints the help (default: false) -m VAL : [ex: iMM904] id of the BIGG model -o VAL : [proteome.fas] path of the output file (default: proteome.fas)

Package fr.inrae.toulouse.metexplore.met4j_toolbox.convert
FbcToNotes	Convert FBC package annotations to sbml html notes more Convert FBC package annotations to sbml html notes (see https://www.degruyter.com/document/doi/10.1515/jib-2017-0082/html) References: Olivier et al.; SBML Level 3 Package: Flux Balance Constraints version 2; Journal of Integrative Bioinformatics; 2018 -h : prints the help (default: false) -i VAL : input SBML file -o VAL : output SBML file
Kegg2Sbml	Build a SBML file from KEGG organism-specific pathways. Uses Kegg API. more Build a SBML file from KEGG organism-specific pathways. Uses Kegg API. Errors returned by this program could be due to Kegg API dysfunctions or limitations. Try later if this problem occurs. -h : prints the help (default: false) -o VAL : [out.sbml] Out sbml file (default: out.sbml) -org VAL : [] Kegg org id. Must be 3 letters ( (default: )
Sbml2CarbonSkeletonNet	Create a carbon skeleton graph representation of a SBML file content, using GSAM atom-mapping file (see https://forgemia.inra.fr/metexplore/gsam) more Metabolic networks used for quantitative analysis often contain links that are irrelevant for graph-based structural analysis. For example, inclusion of side compounds or modelling artifacts such as 'biomass' nodes. Focusing on links between compounds that share parts of their carbon skeleton allows to avoid many transitions involving side compounds, and removes entities without defined chemical structure. This app produces a Carbon Skeleton Network relevant for graph-based analysis of metabolism, in GML or matrix format, from a SBML and an GSAM atom mapping file. GSAM (see https://forgemia.inra.fr/metexplore/gsam) performs atom mapping at genome-scale level using the Reaction Decoder Tool (https://github.com/asad/ReactionDecoder) and allows to compute the number of conserved atoms of a given type between reactants.This app also enables Markov-chain based analysis of metabolic networks by computing reaction-normalized transition probabilities on the Carbon Skeleton Network. -f (--format) [gml | tab | nodeList | : Format of the exported graphTabulated json | matrix] edge list by default (source id edge type target id). Other options include GML, JsonGraph, and tabulated node list (label node id node type). (default: tab) -fi (--fromIndexes) : Use GSAM output with carbon indexes (default: false) -g VAL : input GSAM file -h : prints the help (default: false) -i VAL : input SBML file -ks (--keepSingleCarbon) : keep edges involving single-carbon compounds, such as CO2 (requires formulas in SBML) (default: false) -main (--onlyMainTransition) : Compute RPAIRS-like tags and keep only main transitions for each reaction (default: false) -mc (--nocomp) : merge compartments (requires unique compound names that are consistent across compartments) (default: false) -me (--simple) : merge parallel edges to produce a simple graph (default: false) -o VAL : output file: path to the tabulated file where the resulting network will be exported -ri (--removeIsolatedNodes) : remove isolated nodes (default: false) -tp (--transitionproba) : set transition probability as weight (default: false) -un (--undirected) : create as undirected (default: false)
Sbml2CompoundGraph	Advanced creation of a compound graph representation of a SBML file content more Metabolic networks used for quantitative analysis often contain links that are irrelevant for graph-based structural analysis. For example, inclusion of side compounds or modelling artifacts such as 'biomass' nodes. While Carbon Skeleton Graph offer a relevant alternative topology for graph-based analysis, it requires compounds' structure information, usually not provided in model, and difficult to retrieve for model with sparse cross-reference annotations. In contrary to the Sbml2Graph app that performs a raw conversion of the SBML content, the present app propose a fine-tuned creation of compound graph from predefined list of side compounds and degree weighting to get relevant structure without structural data.This app also enables Markov-chain based analysis of metabolic networks by computing reaction-normalized transition probabilities on the network. -cw (--customWeights) VAL : an optional file containing weights for compound pairs -dw (--degreeWeights) : penalize traversal of hubs by using degree square weighting (default: false) -f (--format) [gml | tab | nodeList | : Format of the exported graphTabulated json | matrix] edge list by default (source id edge type target id). Other options include GML, JsonGraph, and tabulated node list (label node id node type). (default: tab) -h : prints the help (default: false) -i VAL : input SBML file -mc (--mergecomp) [no | by_name | : merge compartments. Use names if by_id] consistent and unambiguous across compartments, or identifiers if compartment suffix is present (id in form "xxx_y" with xxx as base identifier and y as compartment label). (default: no) -me (--simple) : merge parallel edges to produce a simple graph (default: false) -o VAL : output file: path to the tabulated file where the resulting network will be exported -ri (--removeIsolatedNodes) : remove isolated nodes (default: false) -sc VAL : input Side compound file -tp (--transitionproba) : set weight as random walk transition probability, normalized by reaction (default: false) -un (--undirected) : create as undirected (default: false)
Sbml2Graph	Create a graph representation of a SBML file content, and export it in graph file format. more Create a graph representation of a SBML file content, and export it in graph file format. The graph can be either a compound graph, a reaction graph or a bipartite graph, and can be exported in gml or tabulated file format. References: Lacroix et al.; An Introduction to Metabolic Networks and Their Structural Analysis; IEEE/ACM Transactions on Computational Biology and Bioinformatics; 2008 -b (--bipartite) : create bipartite graph (default: false) -c (--compound) : create compound graph (default: true) -f (--format) [gml | tab | nodeList | : Format of the exported graphTabulated json | matrix] edge list by default (source id edge type target id). Other options include GML, JsonGraph, and tabulated node list (label node id node type). (default: tab) -h : prints the help (default: false) -i VAL : input SBML file -o VAL : output file: path to the tabulated file where the resulting network will be exported -r (--reaction) : create reaction graph (default: false)
Sbml2PathwayNet	Creation of a Pathway Network representation of a SBML file content more Creation of a Pathway Network representation of a SBML file content Genome-scale metabolic networks are often partitioned into metabolic pathways. Pathways are frequently considered independently despite frequent coupling in their activity due to shared metabolites. In order to decipher the interconnections linking overlapping pathways, this app proposes the creation of "Pathway Network", where two pathways are linked if they share compounds. -cw (--customWeights) VAL : an optional file containing weights for pathway pairs -f (--format) [gml | tab | nodeList | : Format of the exported graphTabulated json | matrix] edge list by default (source id edge type target id). Other options include GML, JsonGraph, and tabulated node list (label node id node type). (default: tab) -h : prints the help (default: false) -i VAL : input SBML file -ncw (--connectorWeights) : set number of connecting compounds as weight (default: false) -o VAL : output Graph file -oss (--onlySourcesAndSinks) : consider only metabolites that are source or sink in the pathway (i.e non-intermediary compounds) (default: false) -ri (--removeIsolatedNodes) : remove isolated nodes (default: false) -sc VAL : input Side compound file (recommended)
Sbml2Tab	Create a tabulated file listing reaction attributes from a SBML file more Create a tabulated file listing reaction attributes from a SBML file -h : prints the help (default: false) -i VAL : Sbml file -irr VAL : [-->] String for irreversible reaction (default: -->) -o VAL : [out.tsv] Tabulated file (default: out.tsv) -rev VAL : [<==>] String for reversible reaction (default: <==>)
SbmlWizard	General SBML model processing more General SBML model processing including compound removal (such as side compounds or isolated compounds), reaction removal (ex. blocked or exchange reaction), and compartment merging -h : prints the help (default: false) -i VAL : input SBML file -kc (--retainC) VAL : file containing identifiers of compounds to keep from the metabolic network -kr (--retainR) VAL : file containing identifiers of reactions to keep from the metabolic network -mc (--mergecomp) [no | by_name | : merge compartments using the provided by_id] strategy. No merge by default. "by_name" can be used if names are consistent and unambiguous across compartments, "by_id" can be used if compartment suffix is present in compounds identifiers (id in form "xxx_y" with xxx as base identifier and y as compartment label). (default: no) -o VAL : output SBML file -r0 (--noFlux) : remove reactions with lower and upper flux bounds both set to 0.0 (default: false) -rEX (--removeExchange) VAL : remove exchange reactions and species from given exchange compartment identifier -rc (--removeC) VAL : file containing identifiers of compounds to remove from the metabolic network -rdr (--noDuplicated) : remove duplicated reactions (same reactants, same GPR) (default: false) -ric (--noIsolated) : remove isolated compounds (not involved in any reaction) (default: false) -rr (--removeR) VAL : file containing identifiers of reactions to remove from the metabolic network
Tab2Sbml	Create a Sbml File from a tabulated file that contains the reaction ids and the formulas more Create a Sbml File from a tabulated file that contains the reaction ids and the formulas -cf N : [2] number of the column where are the reaction formulas (default: 2) -ci N : [1] number of the column where are the reaction ids (default: 1) -cpt : [deactivated] Create compartment from metabolite suffixes. If this option is deactivated, only one compartment (the default compartment) will be created (default: false) -dcpt VAL : [c] Default compartment (default: c) -e VAL : [_b] flag to assign metabolite as external (default: _b) -h : prints the help (default: false) -i VAL : Tabulated file -id VAL : [NA] Model id written in the SBML file (default: NA) -irr VAL : [-->] String for irreversible reaction (default: -->) -mp : [deactivated] format the metabolite ids in a Palsson way (M_***_c) (default: false) -n N : [0] Number of lines to skip at the beginning of the tabulated file (default: 0) -o VAL : [out.sbml] Out sbml file (default: out.sbml) -rev VAL : [<==>] String for reversible reaction (default: <==>) -rp : [deactivated] format the reaction ids in a Palsson way (R_***) (default: false)

Package fr.inrae.toulouse.metexplore.met4j_toolbox.mapping
NameMatcher	This tool runs edit-distance based fuzzy matching to perform near-similar name matching between a metabolic model and a list of chemical names in a dataset. A harmonization processing is performed on chemical names with substitutions of common patterns among synonyms, in order to create aliases on which classical fuzzy matching can be run efficiently. more Metabolic models and Metabolomics Data often refer compounds only by using their common names, which vary greatly according to the source, thus impeding interoperability between models, databases and experimental data. This requires a tedious step of manual mapping. Fuzzy matching is a range of methods which can potentially helps fasten this process, by allowing the search for near-similar names. Fuzzy matching is primarily designed for common language search engines and is frequently based on edit distance, i.e. the number of edits to transform a character string into another, effectively managing typo, case and special character variations, and allowing auto-completion. However, edit-distance based search fall short when mapping chemical names: As an example, alpha-D-Glucose et Glucose would require more edits than between Fructose and Glucose. This tool runs edit-distance based fuzzy matching to perform near-similar name matching between a metabolic model and a list of chemical names in a dataset. A harmonization processing is performed on chemical names with substitutions of common patterns among synonyms, in order to create aliases on which classical fuzzy matching can be run efficiently. -c VAL : [#] Comment String in the compound file. The lines beginning by this string won't be read (default: #) -col N : [1] column containing compounds' names (default: 1) -compound VAL : Compound file containing one column with compound names to search among the SBML entries -h : prints the help (default: false) -i VAL : Original sbml file -nMatch N : [1] Number of matchs to return per name (default: 1) -o VAL : Output tabulated file -sep VAL : [\t] separator in the compound file to split the colmumns. (default: ) -skip N : [0] Number of lines to skip at the beginning of the compound file (default: 0)
ORApathwayEnrichment	Perform Over Representation Analysis for Pathway Enrichment, using one-tailed exact Fisher Test. more Perform Over Representation Analysis for Pathway Enrichment, using one-tailed exact Fisher Test. The fisher exact test computes the probability p to randomly get the given set of values. This version computes the probability to get at least the given overlap between the given set and the given modality : Sum the hypergeometric probability with increasing target/query intersection cardinality. The hypergeometric probability is computed from the following contingency table entries. (values in cells correspond to the marginal totals of each intersection groups) Query !Query Target a b !Target c d The probability of obtaining the set of value is computed as following: p = ((a+b)!(c+d)!(a+c)!(b+d)!)/(a!b!c!d!(a+b+c+d)!) The obtained p-value is then adjusted for multiple testing using one of the following methods: - Bonferroni: adjusted p-value = p*n - Benjamini-Hochberg: adjusted p-value = p*n/k - Holm-Bonferroni: adjusted p-value = p*(n+1-k) n : number of tests; k : pvalue rank -c (--correction) [Bonferroni | : Method for multiple testing p-value BenjaminiHochberg | HolmBonferroni] adjustment. (default: BenjaminiHochber g) -d (--data) VAL : Input data : Compounds of interest file, as one SBML specie identifier per line -h : prints the help (default: false) -i (--sbml) VAL : Input model : SBML file with pathway annotation -o (--output) VAL : Output file : tabulated file with pathway identifier, pathway name, adjusted p-value. -th (--threshold) N : threshold to select significant pathways. No filtering if <=0 (default: 0.0)

Package fr.inrae.toulouse.metexplore.met4j_toolbox.networkAnalysis
BipartiteDistanceMatrix	Create a compound to reactions distance matrix. more Create a compound to reactions distance matrix. The distance between two nodes (metabolite or reaction) is computed as the length of the shortest path connecting the two in the bipartite graph, Bipartite graphs are composed of two distinct sets of nodes and two nodes can be linked only if they are from distinct sets. Therefore a metabolite node can be linked to a reaction node if the metabolite is a substrate or product of the reaction. An optional custom edge weighting can be used, turning the distances into the sum of edge weights in the lightest path, rather than the length of the shortest path.Custom weighting can be provided in a file. In that case, edges without weight are ignored during path search. If no edge weighting is set, it is recommended to provide a list of side compounds to ignore during network traversal. -f (--full) : compute full pairwise matrix from both reactions and compounds lists (default: false) -h : prints the help (default: false) -i VAL : input SBML file -m (--mets) VAL : an optional file containing list of compounds of interest. -o VAL : output Matrix file -r (--rxns) VAL : an optional file containing list of reactions of interest. -re (--rExclude) VAL : an optional file containing list of reactions to ignore -sc (--side) VAL : an optional file containing list of side compounds to ignore -u (--undirected) : Ignore reaction direction (default: false) -w (--weights) VAL : an optional file containing weights for compound pairs
ChemSimilarityWeighting	Provides tabulated compound graph edge list, with one column with reactant pair's chemical similarity. more Provides tabulated compound graph edge list, with one column with reactant pair's chemical similarity.Chemical similarity has been proposed as edge weight for finding meaningful paths in metabolic networks, using shortest (lightest) path search. References: Rahman et al.; Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC); Bioinformatics; 2005 McShan et al.; PathMiner: predicting metabolic pathways by heuristic search; Bioinformatics; 2003 Pertusi et al.; Efficient searching and annotation of metabolic networks using chemical similarity; Bioinformatics; 2015 -d (--asDist) : Use distance rather than similarity (default: false) -f (--fingerprint) [EState | Extended : The chemical fingerprint to use | KlekotaRoth | MACCS | PubChem] (default: Extended) -h : prints the help (default: false) -in (--inchiFile) VAL : If not present in SBML's annotations, get structure from a tabulated file with first column as compound id and second column as InChI string, no header. -mc (--mergecomp) [no | by_name | : merge compartments. Use names if by_id] consistent and unambiguous across compartments, or identifiers if compartment suffix is present (id in form "xxx_y" with xxx as base identifier and y as compartment label). (default: no) -me (--simple) : merge parallel edges to produce a simple graph (default: false) -nan (--removeNaN) : do not output edges with undefined weight (default: false) -o VAL : output edge weight file -s VAL : input SBML file -sc VAL : input Side compound file -sm (--smileFile) VAL : If not present in SBML's annotations, get structure from a tabulated file with first column as compound id and second column as SMILE string, no header. Ignored if inchi file is provided -tp (--transitionproba) : set weight as random walk transition probability, normalized by reaction (default: false) -un (--undirected) : create as undirected (default: false)
ChokePoint	Compute the Choke points of a metabolic network. more Compute the Choke points of a metabolic network. Choke points are reactions that are required to consume or produce one compound. Targeting of choke point can lead to the accumulation or the loss of some metabolites, thus choke points constitute an indicator of lethality and can help identifying drug target. References: Rahman et al.; Observing local and global properties of metabolic pathways: ‘load points’ and ‘choke points’ in the metabolic networks; Bioinformatics; 2006 -h : prints the help (default: false) -i VAL : input SBML file -o VAL : output result file
DegreeWeighting	Provides tabulated compound graph edge list, with one column with target's degree. more Provides tabulated compound graph edge list, with one column with target's degree.Degree has been proposed as edge weight for finding meaningful paths in metabolic networks, using shortest (lightest) path search. References: Croes et al.; Inferring Meaningful Pathways in Weighted Metabolic Networks; Journal of Molecular Biology; 2006 -h : prints the help (default: false) -mc (--mergecomp) [no | by_name | : merge compartments. Use names if by_id] consistent and unambiguous across compartments, or identifiers if compartment suffix is present (id in form "xxx_y" with xxx as base identifier and y as compartment label). (default: no) -me (--simple) : merge parallel edges to produce a simple graph (default: false) -nan (--removeNaN) : do not output edges with undefined weight (default: false) -o VAL : output edge weight file -pow (--power) N : set weights as the degree raised to the power of number in parameter. (default: 1) -s VAL : input SBML file -sc VAL : input Side compound file -tp (--transitionproba) : set weight as random walk transition probability, normalized by reaction (default: false) -un (--undirected) : create as undirected (default: false)
DistanceMatrix	Create a compound to compound distance matrix. more Create a compound to compound distance matrix. The distance between two compounds is computed as the length of the shortest path connecting the two in the compound graph, where two compounds are linked if they are respectively substrate and product of the same reaction. An optional edge weighting can be used, turning the distances into the sum of edge weights in the lightest path, rather than the length of the shortest path.The default weighting use target's degree squared. Alternatively, custom weighting can be provided in a file. In that case, edges without weight are ignored during path search. If no edge weighting is set, it is recommended to provide a list of side compounds to ignore during network traversal. -dw (--degree) : penalize traversal of hubs by using degree square weighting (-w must not be set) (default: false) -h : prints the help (default: false) -i VAL : input SBML file -o VAL : output Matrix file -s (--seed) VAL : an optional file containing list of compounds of interest. The returned distance matrix contains only the corresponding rows and columns -sc (--side) VAL : an optional file containing list of side compounds to ignore -u (--undirected) : Ignore reaction direction (default: false) -w (--weights) VAL : an optional file containing weights for compound pairs
ExtractSubBipNetwork	Create a subnetwork from a metabolic network in SBML format, and two files containing lists of compounds and/or reactions of interests ids, one per row, plus one file of the same format containing side compounds ids. more Create a subnetwork from a metabolic network in SBML format, and two files containing lists of compounds and/or reactions of interests ids, one per row, plus one file of the same format containing side compounds ids. The subnetwork corresponds to the part of the network that connects reactions and compounds from the first list to reactions and compounds from the second list. Sources and targets list can have elements in common. The connecting part can be defined as the union of shortest or k-shortest paths between sources and targets, or the Steiner tree connecting them. Contrary to compound graph, bipartite graph often lacks weighting policy for edge relevance. In order to ensure appropriate network density, a list of side compounds and blocked reactions to ignore during path build must be provided. An optional edge weight file, if available, can also be used. -br (--blokedReactions) VAL : a file containing list of blocked reactions to ignore -cw (--customWeights) VAL : an optional file containing weights for reactions pairs -f (--format) [gml | tab | nodeList | : Format of the exported graphTabulated json | matrix] edge list by default (source id edge type target id). Other options include GML, JsonGraph, and tabulated node list (label node id node type). (default: tab) -h : prints the help (default: false) -i VAL : input SBML file -k N : Extract k-shortest paths (default: 1) -o VAL : output file: path to the tabulated file where the resulting network will be exported -s VAL : input sources txt file -sc (--side) VAL : a file containing list of side compounds to ignore -st (--steinertree) : Extract Steiner Tree (default: false) -t VAL : input targets txt file -u (--undirected) : Ignore reaction direction (default: false)
ExtractSubNetwork	Create a subnetwork from a metabolic network in SBML format, and two files containing lists of compounds of interests ids, one per row. more Create a subnetwork from a metabolic network in SBML format, and two files containing lists of compounds of interests ids, one per row. The subnetwork corresponds to the part of the network that connects compounds from the first list to compounds from the second list. Sources and targets list can have elements in common. The connecting part can be defined as the union of shortest or k-shortest paths between sources and targets, or the Steiner tree connecting them. The relevance of considered path can be increased by weighting the edges using degree squared, chemical similarity (require InChI or SMILES annotations) or any provided weighting. See previous works on subnetwork extraction for parameters recommendations. References: Croes et al.; Metabolic PathFinding: inferring relevant pathways in biochemical networks; Nucleic Acids Research; 2005 Pertusi et al.; Efficient searching and annotation of metabolic networks using chemical similarity; Bioinformatics; 2015 Croes et al.; Inferring Meaningful Pathways in Weighted Metabolic Networks; Journal of Molecular Biology; 2006 Rahman et al.; Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC); Bioinformatics; 2005 McShan et al.; PathMiner: predicting metabolic pathways by heuristic search; Bioinformatics; 2003 Frainay et al.; Computational methods to identify metabolic sub-networks based on metabolomic profiles; Briefings in Bioinformatics; 2017 Faust et al.; Prediction of metabolic pathways from genome-scale metabolic networks; Biosystems; 2011 -cw (--customWeights) VAL : an optional file containing weights for compound pairs -dw (--degreeWeights) : penalize traversal of hubs by using degree square weighting (default: false) -f (--format) [gml | tab | nodeList | : Format of the exported graphTabulated json | matrix] edge list by default (source id edge type target id). Other options include GML, JsonGraph, and tabulated node list (label node id node type). (default: tab) -h : prints the help (default: false) -i VAL : input SBML file -k N : Extract k-shortest paths (default: 1) -o VAL : output file: path to the tabulated file where the resulting network will be exported -s VAL : input sources txt file -sc (--side) VAL : an optional file containing list of side compounds to ignore -st (--steinertree) : Extract Steiner Tree (default: false) -sw (--chemSimWeights) : penalize traversal of non-relevant edges by using chemical similarity weighting (default: false) -t VAL : input targets txt file -u (--undirected) : Ignore reaction direction (default: false)
ExtractSubReactionNetwork	Create a subnetwork from a metabolic network in SBML format, and two files containing lists of reactions of interests ids, one per row, plus one file of the same format containing side compounds ids. more Create a subnetwork from a metabolic network in SBML format, and two files containing lists of reactions of interests ids, one per row, plus one file of the same format containing side compounds ids. The subnetwork corresponds to the part of the network that connects reactions from the first list to reactions from the second list. Sources and targets list can have elements in common. The connecting part can be defined as the union of shortest or k-shortest paths between sources and targets, or the Steiner tree connecting them. Contrary to compound graph, reaction graph often lacks weighting policy for edge relevance. In order to ensure appropriate network density, a list of side compounds to ignore for linking reactions must be provided. An optional edge weight file, if available, can also be used. -cw (--customWeights) VAL : an optional file containing weights for reactions pairs -f (--format) [gml | tab | nodeList | : Format of the exported graphTabulated json | matrix] edge list by default (source id edge type target id). Other options include GML, JsonGraph, and tabulated node list (label node id node type). (default: tab) -h : prints the help (default: false) -i VAL : input SBML file -k N : Extract k-shortest paths (default: 1) -o VAL : output file: path to the tabulated file where the resulting network will be exported -re (--rExclude) VAL : an optional file containing list of reactions to ignore -s VAL : input sources txt file -sc (--side) VAL : a file containing list of side compounds to ignore -st (--steinertree) : Extract Steiner Tree (default: false) -t VAL : input targets txt file -u (--undirected) : Ignore reaction direction (default: false)
LoadPoint	Compute the Load points of a metabolic network. Load points constitute an indicator of lethality and can help identifying drug targets. more Compute the Load points of a metabolic network. Load points constitute an indicator of lethality and can help identifying drug targets. From Rahman et al. Observing local and global properties of metabolic pathways: ‘load points’ and ‘choke points’ in the metabolic networks. Bioinf. (2006): For a given metabolic network, the load L on metabolite m can be defined as : ln [(pm/km)/(∑Mi=1Pi)/(∑Mi=1Ki)] p is the number of shortest paths passing through a metabolite m; k is the number of nearest neighbour links for m in the network; P is the total number of shortest paths; K is the sum of links in the metabolic network of M metabolites (where M is the number of metabolites in the network). Use of the logarithm makes the relevant values more distinguishable. References: Rahman et al.; Observing local and global properties of metabolic pathways: ‘load points’ and ‘choke points’ in the metabolic networks; Bioinformatics; 2006 -h : prints the help (default: false) -i VAL : input SBML file -k (--npath) N : Number of alternative paths to consider between a pair of connected metabolites (default: 1) -o VAL : output results file -s (--side) VAL : an optional file containing list of side compounds to ignore
MetaboRank	Compute the MetaboRank, a custom personalized PageRank for metabolic network. more Compute the MetaboRank, a custom personalized PageRank for metabolic network. The MetaboRank takes a metabolic network and a list of compounds of interest, and provide a score of relevance for all of the other compounds in the network. The MetaboRank can, from metabolomics results, be used to fuel a recommender system highlighting interesting compounds to investigate, retrieve missing identification and drive literature mining. It is a two dimensional centrality computed from personalized PageRank and CheiRank, with special transition probability and normalization to handle the specificities of metabolic networks. For convenience, a one dimensional centrality rank is also computed from the highest rank from PageRank or CheiRank, and using lowest rank as tie-breaker. See publication for more information. References: Frainay et al.; MetaboRank: network-based recommendation system to interpret and enrich metabolomics results; Bioinformatics; 2019 -d N : damping factor (default: 0.85) -h : prints the help (default: false) -i VAL : input SBML file: path to network used for computing centrality, in sbml format. -max N : maximal number of iteration (default: 15000) -o VAL : output file: path to the file where the results will be exported -s VAL : input seeds file: tabulated file containing node of interest ids and weight -sc VAL : input Side compound file -t N : convergence tolerance (default: 0.001) -w VAL : input edge weight file: (recommended) path to file containing edges' weights. Will be normalized as transition probabilities
NetworkSummary	Create a report summarizing several graph measures characterising the structure of a metabolic network. more Create a report summarizing several graph measures characterising the structure of a metabolic network. Use a metabolic network in SBML file and an optional list of side compounds, and produce a report summarizing several graph measures characterising the structure of the network.This includes (non-exhaustive list): size and order, connectivity, density, degree distribution, shortest paths length, top centrality nodes... -d (--directed) : use reaction direction for distances (default: false) -h : prints the help (default: false) -i VAL : input SBML file -o VAL : output report file -s (--side) VAL : an optional file containing list of side compounds to ignore (recommended) -sd (--skipdist) : skip full distance matrix computation (quick summary) (default: false)
PrecursorNetwork	Perform a network expansion from a set of compound targets to create a precursor network. more Perform a network expansion from a set of compound targets to create a precursor network. The precursor network of a set of compounds (targets) refer to the sub-part of a metabolic network from which a target can be reachedThe network expansion process consist of adding a reaction to the network if any of its products are either a targets or a substrate of a previously added reaction -f (--format) [gml | tab | nodeList | : Format of the exported graphTabulated json | matrix] edge list by default (source id edge type target id). Other options include GML, JsonGraph, and tabulated node list (label node id node type). (default: tab) -h : prints the help (default: false) -i VAL : input SBML file: path to network used for computing scope, in sbml format. -ir (--ignore) VAL : an optional file containing list of reaction to ignore (forbid inclusion in scope) -o VAL : output file: path to the tabulated file where the resulting network will be exported -sc (--sides) VAL : an optional file containing list of ubiquitous compounds to be considered already available -t (--targets) VAL : input target file: tabulated file containing node of interest ids
ReactionDistanceMatrix	Create a reaction to reaction distance matrix. more Create a reaction to reaction distance matrix. The distance between two reactions is computed as the length of the shortest path connecting the two in the reaction graph, where two reactions are linked if they produce a metabolite consumed by the other or the other way around. An optional edge weighting can be used, turning the distances into the sum of edge weights in the lightest path, rather than the length of the shortest path.The default weighting use target's degree squared. Alternatively, custom weighting can be provided in a file. In that case, edges without weight are ignored during path search. If no edge weighting is set, it is recommended to provide a list of side compounds to ignore during network traversal. -dw (--degree) : penalize traversal of hubs by using degree square weighting (-w must not be set) (default: false) -h : prints the help (default: false) -i VAL : input SBML file -o VAL : output Matrix file -re (--rExclude) VAL : an optional file containing list of reactions to ignore -s (--seeds) VAL : an optional file containing list of reactions of interest. -sc (--side) VAL : an optional file containing list of side compounds to ignore -u (--undirected) : Ignore reaction direction (default: false) -w (--weights) VAL : an optional file containing weights for compound pairs
ScopeNetwork	Perform a network expansion from a set of compound seeds to create a scope network more Perform a network expansion from a set of compound seeds to create a scope network The scope of a set of compounds (seed) refer to the maximal metabolic network that can be extended from them,where the extension process consist of adding a reaction to the network if and only if all of its substrates are either a seed or a product of a previously added reaction References: Handorf et al.; Expanding Metabolic Networks: Scopes of Compounds, Robustness, and Evolution; Journal of Molecular Evolution; 2005 -f (--format) [gml | tab | nodeList | : Format of the exported graphTabulated json | matrix] edge list by default (source id edge type target id). Other options include GML, JsonGraph, and tabulated node list (label node id node type). (default: tab) -h : prints the help (default: false) -i VAL : input SBML file: path to network used for computing scope, in sbml format. -ir (--ignore) VAL : an optional file containing list of reaction to ignore (forbid inclusion in scope -o VAL : output file: path to the tabulated file where the resulting network will be exported -s (--seeds) VAL : input seeds file: tabulated file containing node of interest ids -sc (--sides) VAL : an optional file containing list of ubiquitous side compounds to be considered available by default but ignored during expansion -ssc (--showsides) : show side compounds in output network (default: false) -t (--trace) : trace inclusion step index for each node in output (default: false)
SeedsAndTargets	Identify exogenously acquired compounds, exogenously available producible compounds and/or dead ends metabolites from metabolic network topology more Identify exogenously acquired compounds, exogenously available producible compounds and/or dead ends metabolites from metabolic network topology Metabolic seeds and targets are useful for identifying medium requirements and metabolic capability, and thus enable analysis of metabolic ties within communities of organisms. This application can use seed definition and SCC-based detection algorithm by Borenstein et al. or, alternatively, degree-based sink and source detection with compartment adjustment. The first method (see Borenstein et al. 2008 Large-scale reconstruction and phylogenetic analysis of metabolic environments https://doi.org/10.1073/pnas.0806162105) consider strongly connected components rather than individual nodes, thus, members of cycles can be considered as seeds. A sink from an external compartment can however be connected to a non sink internal counterpart, thus highlighting what could end up in the external compartment rather than what must be exported. The second approach is neighborhood based and identify sources and sinks. Since "real" sinks and sources in intracellular compartment(s) may be involved in transport/exchange reactions reversible by default, thus not allowing extracellular source or sink, an option allows to take the degree (minus extracellular neighbors) of intracellular counterparts. References: Borenstein et al.; Large-scale reconstruction and phylogenetic analysis of metabolic environments; Proceedings of the National Academy of Sciences; 2008 -!s (--notSeed) : export nodes that are not seeds (default: false) -!t (--notTarget) : export nodes that are not targets (default: false) -B (--useBorensteinAlg) : use Borenstein Algorithm. Please cite Borenstein et al. 2008 Large-scale reconstruction and phylogenetic analysis of metabolic environments https://doi.org/10.1073/pnas.0806162105), ignore internal option (default: false) -c (--comp) VAL : selected compartment(s), as model identifiers, separated by "+" sign if more than one -h : prints the help (default: false) -i (--inputSBML) VAL : input SBML file -in (--internal) : if an external compartment is defined, adjust degree by considering internal counterpart (default: false) -is (--keepIsolated) : do not ignore isolated nodes, consider isolated both seeds and targets (default: false) -o (--output) VAL : output seeds file -s (--seeds) : export seeds (default: false) -sc (--sideFile) VAL : input side compound file -t (--targets) : export targets (default: false)
SideCompoundsScan	Scan a network to identify side compounds. more Scan a network to identify side compounds. Side compounds are metabolites of small relevance for topological analysis. Their definition can be quite subjective and varies between sources. Side compounds tend to be ubiquitous and not specific to a particular biochemical or physiological process.Compounds usually considered as side compounds include water, atp or carbon dioxide. By being involved in many reactions and thus connected to many compounds, they tend to significantly lower the average shortest path distances beyond expected metabolic relatedness. This tool attempts to propose a list of side compounds according to specific criteria: - *Degree*: Compounds with an uncommonly high number of neighbors can betray a lack of process specificity. High degree compounds typically include water and most main cofactors (CoA, ATP, NADPH...) but can also include central compounds such as pyruvate or acetyl-CoA - *Neighbor Coupling*: Similar to degree, this criteria assume that side compounds are involved in many reactions, but in pairs with other side compounds. Therefore, the transition from ATP to ADP will appear multiple times in the network, creating redundant 'parallel edges' between these two neighbors. Being tightly coupled to another compound through a high number of redundant edges, can point out cofactors while keeping converging pathways' products with high degree like pyruvate aside. - *Carbon Count*: Metabolic "waste", or degradation end-product such as ammonia or carbon dioxide are usually considered as side compounds. Most of them are inorganic compound, another ill-defined concept, sometimes defined as compound lacking C-C or C-H bonds. Since chemical structure is rarely available in SBML model beyond chemical formula, we use a less restrictive criterion by flagging compound with one or no carbons. This cover most inorganic compounds, but include few compounds such as methane usually considered as organic. - *Chemical Formula*: Metabolic network often contains 'artifacts' that serve modelling purpose (to define a composite objective function for example). Such entities can be considered as 'side entities'. Since they are not actual chemical compounds, they can be detected by their lack of valid chemical formula. However, this can also flag main compounds with erroneous or missing annotation. -cc (--noCarbonSkeleton) : flag as side compound any compound with less than 2 carbons in formula (default: false) -d (--degree) N : flag as side compounds any compound with degree above threshold (default: 400) -dp (--degreep) N : flag as side compounds the top x% of compounds according to their degree (default: NaN) -h : prints the help (default: false) -i VAL : input SBML file -id (--onlyIds) : do not report values in output, export ids of compounds flagged as side compounds, allowing piping results (default: false) -m (--merge) [no | by_name | by_id] : degree is shared between compounds in different compartments. Use names if consistent and unambiguous across compartments, or identifiers if compartment suffix is present (id in form "xxx_y" with xxx as base identifier and y as compartment label). (default: no) -nc (--neighborCoupling) N : flag as side compound any compound with a number of parallel edges shared with a neighbor above the given threshold (default: NaN) -o VAL : output file containing the side compounds -s (--onlySides) : output compounds flagged as side compounds only (default: false) -uf (--undefinedFormula) : flag as side compound any compound with no valid chemical formula (default: false)
TopologicalPathwayAnalysis	Run a Topological Pathway Analysis (TPA) to identify key pathways based on topological properties of its constituting compounds. more Run a Topological Pathway Analysis (TPA) to identify key pathways based on topological properties of its constituting compounds. From a list of compounds of interest, the app compute their betweenness centrality (which quantifies how often a compound acts as a intermediary along the shortest paths between pairs of other compounds in the network, which, if high, suggest a critical role in the overall flow within the network). Each pathway is scored according to the summed centrality of its metabolites found in the dataset. Alternatively to the betweenness, one can make use of the out-degree (the number of outgoing link, i.e. number of direct metabolic product) as a criterion of importance. TPA is complementary to statistical enrichment analysis to ensures a more meaningful interpretation of the data, by taking into account the influence of identified compounds on the structure of the pathways. -cw (--customWeights) VAL : an optional file containing weights for compound pairs, taken into account for betweenness computation. Edges not found in file will be removed -h : prints the help (default: false) -i VAL : input SBML file -mc (--mergecomp) [no | by_name | : merge compartments. Use names if by_id] consistent and unambiguous across compartments, or identifiers if compartment suffix is present (id in form "xxx_y" with xxx as base identifier and y as compartment label). (default: no) -noi VAL : file containing the list of metabolites of interests (one per line) -o VAL : output result file (tsv format) -out (--outDegree) : use out-degree as scoring function instead of betweenness (faster computation) (default: false) -ri (--removeIsolatedNodes) : remove isolated nodes (default: false) -sc VAL : input Side compound file (recommended) -un (--undirected) : the compound graph built from the metabolic network and used for computations will undirected, i.e. the reaction directions won't be taken into account (default: false)

Package fr.inrae.toulouse.metexplore.met4j_toolbox.reconstruction
CreateMetaNetwork	Create a Meta-Network from two sub-networks in SBML format. more Create a Meta-Network from two sub-networks in SBML format. A meta-network is a single model which contains several sub-networks that remains individualized within the meta-network (as opposed to model fusion), but which can share some of their components with other sub-networks through a shared "medium" compartment. -h : prints the help (default: false) -k (--keepCompartment) : keep the original external compartments in the meta-network, otherwise, they will be fused into the new shared external compartment (default: false) -mc (--mergingCriterion) [by_metanetx : field used to identify the same | by_name | by_id] metabolites across the two different networks. "by_name"/"by_id" can be used if names/identifiers are consistent and unambiguous across source models, "by_metanetx" can be used if models contains MetaNetX identifiers in annotation field using standard miriam format. (default: by_name) -n1 (--network1) VAL : input SBML file: path to first network, in sbml format. -n1ex (--external1) VAL : external compartment identifier in first network. -n1meta (--firstAsMeta) : treat first network as meta-network, allowing more than two sub-models with iterative fusions. This will overwrite shared compartment and pool compounds (which must follow the "pool_" prefix convention) and will ignore --n1prefix argument (default: false) -n1px (--n1prefix) VAL : prefix that will be added to first network's entities identifiers (default: Net1_) -n2 (--network2) VAL : input SBML file: path to second network, in sbml format. -n2ex (--external2) VAL : external compartment identifier in second network. -n2px (--n2prefix) VAL : prefix that will be added to second network's entities identifiers (default: Net2_) -o VAL : output meta-network SBML file
SbmlCheckBalance	Check balance of all the reactions in a SBML. more Check balance of all the reactions in a SBML. A reaction is balanced if all its reactants have a chemical formula with a good syntax and if the quantity of each atom is the same in both sides of the reaction. For each reaction, indicates if the reaction is balanced, the list of the atoms and the sum of their quantity, and the list of the metabolites that don't have a correct chemical formula. -h : prints the help (default: false) -i VAL : Input Sbml file -out VAL : [checkBalances.tsv] Output tabulated file (1st col: reaction id, 2nd col: boolean indicating if the reaction is balanced, 3rd col: atom balances, 4th col: metabolites with bad formula (default: checkBalances.tsv)