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Update BUILD.org
Instructions for adding new data resources and converting the ontology into a Memgraph graph database
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‎BUILD.org‎

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Original file line numberDiff line numberDiff line change
@@ -49,10 +49,10 @@ other flavor of SQL).
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|-----------+----------------+-----------------------------------+---------------------------+--------------------|
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| Source | Directory name | Entity type(s) | URL | Extra instructions |
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|-----------+----------------+-----------------------------------+---------------------------+--------------------|
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| Hetionet | =hetionet= | Many - see =populate-ontology.py= | [[https://github.com/hetio/hetionet/tree/master/hetnet/tsv][GitHub]] | [[Hetionet]] |
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| NCBI Gene | =ncbigene= | Genes | [[https://ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz][Homo_sapiens.gene_info.gz]] | [[NCBI Gene]] |
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| Drugbank | =drugbank= | Drugs / drug candidates | [[https://go.drugbank.com/releases/latest#open-data][DrugBank website]] | [[Drugbank]] |
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| DisGeNET | =disgenet= | Diseases and disease-gene edges | [[https://www.disgenet.org/][DisGeNET]] | [[DisGeNET]] |
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| Hetionet | =hetionet= | Many - see =populate-ontology.py= | [[https://github.com/hetio/hetionet/tree/master/hetnet/tsv][GitHub]] | [[https://het.io][Hetionet]] |
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| NCBI Gene | =ncbigene= | Genes | [[https://ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz][Homo_sapiens.gene_info.gz]] | [[https://www.ncbi.nlm.nih.gov/gene/][NCBI Gene]] |
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| Drugbank | =drugbank= | Drugs / drug candidates | [[https://go.drugbank.com/releases/latest#open-data][DrugBank website]] | [[https://go.drugbank.com][Drugbank]] |
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| DisGeNET | =disgenet= | Diseases and disease-gene edges | [[https://www.disgenet.org/][DisGeNET]] | [[https://disgenet.com][DisGeNET]] |
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| | | | | |
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*** Hetionet
@@ -438,3 +438,367 @@ CALL db.relationshipTypes() YIELD relationshipType as type
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CALL apoc.cypher.run('MATCH ()-[:`'+type+'`]->() RETURN count(*) as count',{}) YIELD value
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RETURN type, value.count ORDER BY type
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#+end_src
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* 4.: Adding new data resources, nodes, relationships, and properties.
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In version 2.0, we added "TranscriptionFactor" nodes, "TRANSCRIPTIONFACTORINTERACTSWITHGENE" relationships, node properties of "chromosome" number and "sourcedatabase", relationships properties of "correlation", "score", "p_fisher", "z_score", "affinity_nm", "confidence", "sourcedatabase", and "unbiased".
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To achieve this, we added the above entities to the ontology RDF and now named =alzkb_v2.rdf= in the =alzkb\data= directory. Then collect additional source data files as detailed in the table below.
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| Source | Directory name | Entity type(s) | URL | Extra instructions |
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|-----------|----------------|---------------------------------------------|-----------------------|--------------------|
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| TRRUST | =dorothea= | Transcription factors(TF) and TF-gene edges | [[https://www.grnpedia.org/trrust/downloadnetwork.php][TRRUST Download]] | [[https://www.grnpedia.org/trrust/][TRRUST]] |
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| DoRothEA | =dorothea= | Transcription factors(TF) and TF-gene edges | [[https://saezlab.github.io/dorothea/][DoRothEA Installation]] | [[https://bioconductor.org/packages/release/data/experiment/vignettes/dorothea/inst/doc/dorothea.R][DoRothEA RScript]] |
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** Prepare Source Data
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Download =trrust_rawdata.human.tsv= from TRRUST Download. Install DoRothEA by following the DoRothEA Installation within R. Place the =trrust_rawdata.human.tsv= and =alzkb_parse_dorothea.py= inside of =Dorothea/= subdirectory, which should be within your raw data directory (e.g., =D:\data=). Run =alzkb_parse_dorothea.py=. You’ll notice that it creates a =tf.tsv= file that is used while populating the ontology.
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** Replicate Hetionet Resources
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Since Hetionet does not have an up-to-date update plan, we have replicated them using the rephetio paper and source code to ensure AlzKB has current data. Follow the steps in [[https://github.com/EpistasisLab/AlzKB-updates][AlzKB-updates]] Github repository to create =hetionet-custom-nodes.tsv= and =hetionet-custom-edges.tsv=. Place these files in the =hetionet/= subdirectory.
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** Process Data Files
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Place the updated =alzkb_parse_ncbigene.py=, =alzkb_parse_drugbank.py=, and =alzkb_parse_disgenet.py= from the =scripts/= directory in their respective raw data file subdirectory. Run each script to process the data for the next step.
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** Populate Ontology
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Now that we have the updated ontology and updated data files, run the updated =alzkb/populate_ontology.py= to populate records. It creates a =alzkb_v2-populated.rdf= file that will be used in next step.
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* 5.: Converting the ontology into a Memgraph graph database
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** Installing Memgraph
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If you haven't done so already, download Memgraph from the [[https://memgraph.com/docs/getting-started/install-memgraph][Install Memgraph]] page. Most users install Memgraph using a =pre-prepared docker-compose.yml= file by executing:
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- for Linux and macOS:
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=curl https://install.memgraph.com | sh=
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- for Windows:
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=iwr https://windows.memgraph.com | iex=
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More details are in [[https://memgraph.com/docs/getting-started/install-memgraph/docker][Install Memgraph with Docker]]
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** Generating the CSV File
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Before uploading the file to Memgrpah, run =alzkb/rdf_to_memgraph_csv.py= with the =alzkb_v2-populated.rdf= file to generate =alzkb-populated.csv=.
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** Starting Memgraph with Docker
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Follow the instructions in [[https://memgraph.com/docs/data-migration/migrate-from-neo4j#importing-data-into-memgraph][importing-data-into-memgraph]] Step 1. Starting Memgraph with Docker to upload the =alzkb-populated.csv= file to the container.
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Open Memgraph Lab. Memgraph Lab is available at =http://localhost:3000=. Click the =Query Execution= in MENU on the left bar. Then, you can type a Cypher query in the =Cypher Editor=.
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** Gaining speed with indexes and analytical storage mode
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- To create indexes, run the following Cypher queries:
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#+begin_src cypher
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CREATE INDEX ON :Drug(nodeID);
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CREATE INDEX ON :Gene(nodeID);
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CREATE INDEX ON :BiologicalProcess(nodeID);
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CREATE INDEX ON :Pathway(nodeID);
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CREATE INDEX ON :MolecularFunction(nodeID);
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CREATE INDEX ON :CellularComponent(nodeID);
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CREATE INDEX ON :Symptom(nodeID);
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CREATE INDEX ON :BodyPart(nodeID);
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CREATE INDEX ON :DrugClass(nodeID);
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CREATE INDEX ON :Disease(nodeID);
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CREATE INDEX ON :TranscriptionFactor (nodeID);
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#+end_src
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- To check the current storage mode, run:
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#+begin_src cypher
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SHOW STORAGE INFO;
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#+end_src
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- Change the storage mode to analytical before import:
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#+begin_src cypher
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STORAGE MODE IN_MEMORY_ANALYTICAL;
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#+end_src
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** Importing data into Memgraph
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- Drug nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':Drug' AND row.commonName <> ''
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CREATE (d:Drug {nodeID: row._id, commonName: row.commonName, sourceDatabase: row.sourceDatabase,
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xrefCasRN: row.xrefCasRN, xrefDrugbank: row.xrefDrugbank});
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MATCH (d:Drug)
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RETURN count(d);
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#+end_src
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- Gene nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':Gene'
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CREATE (g:Gene {nodeID: row._id, commonName: row.commonName, geneSymbol: row.geneSymbol, sourceDatabase: row.sourceDatabase,
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typeOfGene: row.typeOfGene, chromosome: row.chromosome, xrefEnsembl: row.xrefEnsembl,
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xrefHGNC: row.xrefHGNC, xrefNcbiGene: toInteger(row.xrefNcbiGene), xrefOMIM: row.xrefOMIM});
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MATCH (g:Gene)
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RETURN count(g);
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#+end_src
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- BiologicalProcess nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':BiologicalProcess'
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CREATE (b:BiologicalProcess {nodeID: row._id, commonName: row.commonName, sourceDatabase: row.sourceDatabase,
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xrefGeneOntology: row.xrefGeneOntology});
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MATCH (b:BiologicalProcess)
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RETURN count(b)
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#+end_src
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- Pathway nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':Pathway'
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CREATE (p:Pathway {nodeID: row._id, pathwayId: row.pathwayId, pathwayName: row.pathwayName, sourceDatabase: row.sourceDatabase});
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MATCH (p:Pathway)
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RETURN count(p)
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#+end_src
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- MolecularFunction nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':MolecularFunction'
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CREATE (m:MolecularFunction {nodeID: row._id, commonName: row.commonName, xrefGeneOntology: row.xrefGeneOntology});
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MATCH (m:MolecularFunction)
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RETURN count(m)
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#+end_src
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- CellularComponent nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':CellularComponent'
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CREATE (c:CellularComponent {nodeID: row._id, commonName: row.commonName, xrefGeneOntology: row.xrefGeneOntology});
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MATCH (c:CellularComponent)
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RETURN count(c)
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#+end_src
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- Symptom nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':Symptom'
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CREATE (s:Symptom {nodeID: row._id, commonName: row.commonName, sourceDatabase: row.sourceDatabase, xrefMeSH: row.xrefMeSH});
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MATCH (s:Symptom)
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RETURN count(s)
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#+end_src
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- BodyPart nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':BodyPart'
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CREATE (b:BodyPart {nodeID: row._id, commonName: row.commonName, sourceDatabase: row.sourceDatabase, xrefUberon: row.xrefUberon});
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MATCH (b:BodyPart)
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RETURN count(b)
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#+end_src
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- DrugClass nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':DrugClass'
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CREATE (d:DrugClass {nodeID: row._id, commonName: row.commonName, sourceDatabase: row.sourceDatabase, xrefNciThesaurus: row.xrefNciThesaurus});
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MATCH (d:DrugClass)
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RETURN count(d)
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#+end_src
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- Disease nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':Disease'
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CREATE (d:Disease {nodeID: row._id, commonName: row.commonName, sourceDatabase: row.sourceDatabase,
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xrefDiseaseOntology: row.xrefDiseaseOntology, xrefUmlsCUI: row.xrefUmlsCUI});
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MATCH (d:Disease)
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RETURN count(d)
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#+end_src
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- Transcription Factor nodes
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._labels = ':TranscriptionFactor'
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CREATE (t:TranscriptionFactor {nodeID: row._id, sourceDatabase: row.sourceDatabase, TF: row.TF});
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MATCH (t:TranscriptionFactor)
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RETURN count(t)
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#+end_src
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- GENEPARTICIPATESINBIOLOGICALPROCESS relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'GENEPARTICIPATESINBIOLOGICALPROCESS'
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MATCH (g:Gene {nodeID: row._start}) MATCH (b:BiologicalProcess {nodeID: row._end})
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MERGE (g)-[rel:GENEPARTICIPATESINBIOLOGICALPROCESS]->(b)
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RETURN count(rel)
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#+end_src
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- GENEREGULATESGENE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'GENEREGULATESGENE'\
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MATCH (g:Gene {nodeID: row._start}) MATCH (g2:Gene {nodeID: row._end})
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MERGE (g)-[rel:GENEREGULATESGENE]->(g2)
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RETURN count(rel)
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#+end_src
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- GENEINPATHWAY relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'GENEINPATHWAY'
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MATCH (g:Gene {nodeID: row._start}) MATCH (p:Pathway {nodeID: row._end})
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MERGE (g)-[rel:GENEINPATHWAY]->(p)
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RETURN count(rel)
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#+end_src
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- GENEINTERACTSWITHGENE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'GENEINTERACTSWITHGENE'
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MATCH (g:Gene {nodeID: row._start}) MATCH (g2:Gene {nodeID: row._end})
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MERGE (g)-[rel:GENEINTERACTSWITHGENE]->(g2)
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RETURN count(rel)
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#+end_src
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- BODYPARTUNDEREXPRESSESGENE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'BODYPARTUNDEREXPRESSESGENE'
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MATCH (b:BodyPart {nodeID: row._start}) MATCH (g:Gene {nodeID: row._end})
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MERGE (b)-[rel:BODYPARTUNDEREXPRESSESGENE]->(g)
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RETURN count(rel)
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#+end_src
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- BODYPARTOVEREXPRESSESGENE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'BODYPARTOVEREXPRESSESGENE'
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MATCH (b:BodyPart {nodeID: row._start}) MATCH (g:Gene {nodeID: row._end})
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MERGE (b)-[rel:BODYPARTOVEREXPRESSESGENE]->(g)
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RETURN count(rel)
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#+end_src
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- GENEHASMOLECULARFUNCTION relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'GENEHASMOLECULARFUNCTION'
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MATCH (g:Gene {nodeID: row._start}) MATCH (m:MolecularFunction {nodeID: row._end})
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MERGE (g)-[rel:GENEHASMOLECULARFUNCTION]->(m)
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RETURN count(rel)
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#+end_src
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- GENEASSOCIATEDWITHCELLULARCOMPONENT relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'GENEASSOCIATEDWITHCELLULARCOMPONENT'
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MATCH (g:Gene {nodeID: row._start}) MATCH (c:CellularComponent {nodeID: row._end})
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MERGE (g)-[rel:GENEASSOCIATEDWITHCELLULARCOMPONENT]->(c)
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RETURN count(rel)
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#+end_src
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- GENECOVARIESWITHGENE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'GENECOVARIESWITHGENE'
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MATCH (g:Gene {nodeID: row._start}) MATCH (g2:Gene {nodeID: row._end})
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MERGE (g)-[rel:GENECOVARIESWITHGENE {sourceDB: row.sourceDB, unbiased: row.unbiased, correlation: ToInteger(row.correlation)}]->(g2)
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RETURN count(rel)
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#+end_src
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- CHEMICALDECREASESEXPRESSION relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'CHEMICALDECREASESEXPRESSION'
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MATCH (d:Drug {nodeID: row._start}) MATCH (g:Gene {nodeID: row._end})
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MERGE (d)-[rel:CHEMICALDECREASESEXPRESSION {sourceDB: row.sourceDB, unbiased: row.unbiased, z_score: ToInteger(row.z_score)}]->(g)
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RETURN count(rel)
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#+end_src
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- CHEMICALINCREASESEXPRESSION relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'CHEMICALINCREASESEXPRESSION'
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MATCH (d:Drug {nodeID: row._start}) MATCH (g:Gene {nodeID: row._end})
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MERGE (d)-[rel:CHEMICALINCREASESEXPRESSION {sourceDB: row.sourceDB, unbiased: row.unbiased, z_score: ToInteger(row.z_score)}]->(g)
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RETURN count(rel)
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#+end_src
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- CHEMICALBINDSGENE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'CHEMICALBINDSGENE'
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MATCH (d:Drug {nodeID: row._start}) MATCH (g:Gene {nodeID: row._end})
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MERGE (d)-[rel:CHEMICALBINDSGENE {sourceDB: row.sourceDB, unbiased: row.unbiased, affinity_nM: ToInteger(row.affinity_nM)}]->(g)
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RETURN count(rel)
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#+end_src
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- DRUGINCLASS relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'DRUGINCLASS'
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MATCH (d:Drug {nodeID: row._start}) MATCH (d2:DrugClass {nodeID: row._end})
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MERGE (d)-[rel:DRUGINCLASS]->(d2)
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RETURN count(rel)
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#+end_src
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- GENEASSOCIATESWITHDISEASE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'GENEASSOCIATESWITHDISEASE'
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MATCH (g:Gene {nodeID: row._start}) MATCH (d:Disease {nodeID: row._end})
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MERGE (g)-[rel:GENEASSOCIATESWITHDISEASE {sourceDB: row.sourceDB, score: ToInteger(row.score)}]->(d)
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RETURN count(rel)
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#+end_src
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- SYMPTOMMANIFESTATIONOFDISEASE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'SYMPTOMMANIFESTATIONOFDISEASE'
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MATCH (s:Symptom {nodeID: row._start}) MATCH (d:Disease {nodeID: row._end})
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MERGE (s)-[rel:SYMPTOMMANIFESTATIONOFDISEASE {sourceDB: row.sourceDB, unbiased: row.unbiased, p_fisher: row.p_fisher}]->(d)
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RETURN count(rel)
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#+end_src
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- DISEASELOCALIZESTOANATOMY relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'DISEASELOCALIZESTOANATOMY'
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MATCH (d:Disease {nodeID: row._start}) MATCH (b:BodyPart {nodeID: row._end})
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MERGE (d)-[rel:DISEASELOCALIZESTOANATOMY {sourceDB: row.sourceDB, unbiased: row.unbiased, p_fisher: row.p_fisher}]->(b)
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RETURN count(rel)
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#+end_src
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- DRUGTREATSDISEASE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'DRUGTREATSDISEASE'
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MATCH (d:Drug {nodeID: row._start}) MATCH (d2:Disease {nodeID: row._end})
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MERGE (d)-[rel:DRUGTREATSDISEASE]->(d2)
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RETURN count(rel)
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#+end_src
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- DRUGCAUSESEFFECT relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'DRUGCAUSESEFFECT'
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MATCH (d:Drug {nodeID: row._start}) MATCH (d2:Disease {nodeID: row._end})
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MERGE (d)-[rel:DRUGCAUSESEFFECT]->(d2)
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RETURN count(rel)
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#+end_src
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- TRANSCRIPTIONFACTORINTERACTSWITHGENE relationships
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#+begin_src cypher
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LOAD CSV FROM "/usr/lib/memgraph/alzkb-populated.csv" WITH HEADER AS row
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WITH row WHERE row._type = 'TRANSCRIPTIONFACTORINTERACTSWITHGENE'
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MATCH (t:TranscriptionFactor {nodeID: row._start}) MATCH (g:Gene {nodeID: row._end})
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MERGE (t)-[rel:TRANSCRIPTIONFACTORINTERACTSWITHGENE {sourceDB: row.sourceDB, confidence: row.confidence}]->(g)
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RETURN count(rel)
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#+end_src
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** Switching Back to Transactional Storage Mode
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After importing the data, follow these steps to switch back to the transactional storage mode:
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- Switch to Transactional Storage Mode:
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#+begin_src cypher
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STORAGE MODE IN_MEMORY_TRANSACTIONAL;
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#+end_src
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- Verify the Storage Mode Switch:
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#+begin_src cypher
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SHOW STORAGE INFO;
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#+end_src

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