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Data files to integrate

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Following the steps on this page you will set up an example InterMine. You will:

  • Load some real data sets for Malaria (‘’P. falciparum’‘)
  • Learn about post-processing after data is loaded
  • Deploy a webapp to query the data

Getting Started

We use git to manage and distribute source code. Download InterMine software from Get the Software and dependencies from System Requirements.

Creating a new Mine

Change into the directory you checked out the InterMine source code to and look at the sub-directories:

$ cd git/intermine
$ ls

The directories include:

the core InterMine code, this is generic code to work with any data model.
code to deal specifically with biological data, it includes sources to load data from standard biological formats.
the configuration used to create FlyMine, a useful reference when building your own Mine.
a non-biological test data model used for testing the core InterMine system.
InterMine’s ant-based build system, you shouldn’t need to edit anything here.

All configuration to create a new Mine is held in a directory in git/intermine, your Mine will depend on code in intermine, bio and imbuild. Any Mine needs to be a top level directory in your InterMine checkout.

There is a script for creating the Mine; in git/intermine run:

# in git/intermine
$ bio/scripts/make_mine MalariaMine

You will see the message: Created malariamine directory for MalariaMine

A malariamine directory has been created with several sub-directories, change into the directory and look what is created:

$ cd malariamine
$ ls

We will look at each of the sub-directories in much more detail later, they are:

contains information about the data model to be used and ant targets relating to the data model and database creation.
provides ant targets for loading data into the Mine.
ant targets to run post-processing operations on the data in the Mine once it is integrated.
basic configuration and commands for building and deploying the web application

In addition there are two files which we won’t need to edit and a project.xml file. Most generated directories have a file and a short build.xml file, these are used by the InterMine build system.


The project.xml allows you to configure which data to load into your Mine. The automatically generated file has empty <sources> and <post-processing> sections:

$ less project.xml

There is a project.xml already prepared to define a new MalariaMine, copy it to this directory now and look at it:

$ cp ../bio/tutorial/project.xml .
$ less project.xml


The <source> elements list and configure the data sources to be loaded, each one has a type that corresponds to a directory in git/intermine/bio/sources or a subdirectory (the locations of sources to read are defined by source.location properties at the top of the file). These directories include parsers to retrieve data and information on how it will be integrated. The name can be anything and can be the same as type, using a more specific name allows you to define specific integration keys (more on this later).

<source> elements can have several properties:, and are all used to define locations of files that the source should load. Other properties are used as parameters to specific parsers.


Specific operations can be performed on the Mine once data is loaded, these are listed here as <post-process> elements. We will look at these in more detail later.

Data to load

The InterMine checkout includes a tar file with data to load into MalariaMine. These are real, complete data sets for ‘’P. falciparum’‘. We will load genome annotation from PlasmoDB, protein data from UniProt and GO annotation also from PlasmoDB.

Copy this to some local directory (your home directory is fine for this workshop) and extract the archive:

$ cd
$ cp git/intermine/bio/tutorial/malaria-data.tar.gz .
$ tar -zxvf malaria-data.tar.gz

In your malariamine directory edit project.xml to point each source at the extracted data, just replace DATA_DIR with /home/username (or on a mac /Users/username). Do use absolute path.

$ cd ~/git/intermine/malariamine
$ sed -i 's/DATA_DIR/\/home\/username/g' project.xml

For example, the uniprot-malaria source:

  <source name="uniprot-malaria" type="uniprot">
    <property name="uniprot.organisms" value="36329"/>
    <property name="" location="/home/username/malaria/uniprot/"/>


All file locations must be absolute not relative paths.

The project.xml file is now ready to use.

Properties file

Configuration of local databases and tomcat deployment is kept in a file in a .intermine directory under your home directory. We need to set up a file.

If you don’t already have a .intermine directory in your home directory, create one now:

$ cd
$ mkdir .intermine

There is a partially completed properties file for MalariaMine already. Copy it into your .intermine directory:

$ cd
$ cp git/intermine/bio/tutorial/ .intermine/

Update this properties file with your postgres server location, username and password information for the two databases you just created. The rest of the information is needed for the webapp and will be updated in Step 13.

For the moment you need to change PSQL_USER and PSQL_PWD in the db.production and db.common-tgt-items properties.

# Access to the postgres database to build into and access from the webapp
# port: uncomment the next line if use different prot other than 3306
# db.production.datasource.port=PORT_NUMBER

If you don’t have a password for your postgres account you can leave password blank.

Create databases

Finally, we need to create malariamine and items-malariamine postgres databases as specified in the file:

$ createdb malariamine
$ createdb items-malariamine

New postgres databases default to UTF-8 as the character encoding. This will work with InterMine but performance is better with SQL_ASCII.

The Data Model

Now we’re ready to set up a database schema and load some data into our MalariaMine, first some information on how data models are defined in InterMine.

Defining the model

  • InterMine uses an object-oriented data model, classes in the model and relationships between them are defined in an XML file. Depending on which data types you include you will need different classes and fields in the model, so the model is generated from a core model XML file and any number of additions files. These additions files can define extra classes to be added to the model and define extra fields for additional classes.
  • Elements of the model are represented by Java classes and references between them.
  • These Java classes map automatically to tables in the database schema.
  • The object model is defined as an XML file, that defines classes, their attributes and references between classes.
  • The Java classes and database schema are automatically generated from an XML file.
  • You can easily adapt InterMine to include your own data by creating new additions files, we’ll see how to do this later.
  • All targets relating to the model for a Mine are in the dbmodel directory, go there now:
$ cd ~/git/intermine/malariamine/dbmodel

The core data model (and some extra model files) are defined in the file:

core.model.path = bio/core

You can view the contents of the core model:

$ less ../../bio/core/core.xml

Note the fields defined for Protein:

<class name="Protein" extends="BioEntity" is-interface="true">
  <attribute name="md5checksum" type="java.lang.String"/>
  <attribute name="primaryAccession" type="java.lang.String"/>
  <attribute name="length" type="java.lang.Integer"/>
  <attribute name="molecularWeight" type="java.lang.Integer"/>
  <reference name="sequence" referenced-type="Sequence"/>
  <collection name="genes" referenced-type="Gene" reverse-reference="proteins"/>

Protein is a subclass of BioEntity, defined by extends=”BioEntity”. The Protein class will therefore also inherit all fields of BioEntity.

<class name="BioEntity" is-interface="true">
  <attribute name="primaryIdentifier" type="java.lang.String"/>
  <attribute name="secondaryIdentifier" type="java.lang.String"/>

The model is generated from a core model XML file and any number of additions files. The first file merged into the core model is the so_additions.xml file. This XML file is generated from terms listed in the so_terms file. The build system creates classes corresponding to the Sequence Ontology terms:

$ less resources/so_terms

The model is then combined with any extra classes and fields defined in the sources to integrate, those listed as <source> elements in project.xml. Look at an example ‘additions’ file for the UniProt source:

$ less ../../bio/sources/uniprot/uniprot_additions.xml

This defines extra fields for the Protein class which will be added to those from the core model. * Other model components can be included by specifying in the dbmodel/ file, for example we include bio/core/genomic_additions.xml * The reverse-reference elements in definitions of some references and collections, this defines in the model that two references/collections are opposing ends of the same relationship. The value should be set to the name of the reference/collection in the referenced-type.

Creating a database

Now run the ant target to merge all the model components, generate Java classes and create the database schema, in dbmodel run:

# in malariamine/dbmodel
$ ant clean build-db

The clean is necessary when you have used the target before, it removes the build and dist directories and any previously generated model.

This target has done several things:

  1. Merged the core model with other model additions and created a new XML file:
$ less build/model/genomic_model.xml

Look for the Protein class, you can see it combines fields from the core model and the UniProt additions file.

  1. The so_additions.xml file has also been created using the sequence ontology terms in so_term:
$ less build/model/so_additions.xml

Each term from so_term was added to the model, according to the sequence ontology.

  1. Generated and compiled a Java class for each of the <class> elements in the file. For example
$ less build/gen/src/org/intermine/model/bio/

Each of the fields has appropriate getters and setters generated for it, note that these are interfaces and are turned into actual classes dynamically at runtime - this is how the model copes with multiple inheritance.

  1. Automatically created database tables in the postgres database specified in as db.production - in our case malariamine. Log into this database and list the tables and the columns in the protein table:


It may be necessary to switch to the user malariamine before continuing.

$ psql malariamine
malariamine=#  \d
malariamine=#  \d protein

The different elements of the model XML file are handled as follows:

there is one column for each attribute of Protein - e.g. primaryIdentifer and length.
references to other classes are foreign keys to another table - e.g. Protein has a reference called organism to the Organism class so in the database the protein table has a column organismid which would contain an id that appears in the organism table.
indirection tables are created for many-to-many collections - e.g. Protein has a collection of Gene objects so an indirection table called genesproteins is created.

This has also created necessary indexes on the tables:

malariamine=#  \d genesproteins


Running build-db will destroy any existing data loaded in the malariamine database and re-create all the tables.

The model XML file is stored in the database once created, this and some other configuration files are held in the intermine_metadata table which has key and value columns:

malariamine=# select key from intermine_metadata;

Loading Data

For this tutorial we will run several data integration and post-processing steps manually. This is a good way to learn how the system works and to test individual stages. For running actual builds there is a project_build script that will run all steps specified in project.xml automatically. We will cover this later.

Loading data from a source

Loading of data is done by running ant in the integrate directory. You can specify one or more sources to load or choose to load all sources listed in the project.xml file. When you specify sources by name the order that they appear in project.xml doesn’t matter. Now load data from the uniprot-malaria source:

$ cd ../integrate
$ ant -Dsource=uniprot-malaria -v

The -v flag is to run ant in verbose mode, this will display complete stack traces if there is a problem.

This will take a couple of minutes to complete, the command runs the following steps:

  1. Checks that a source with name uniprot-malaria exists in project.xml
  2. Reads the UniProt XML files at the location specified by
  3. Calls the parser included in the uniprot source with the list of files, this reads the original XML and creates Items which are metadata representations of the objects that will be loaded into the malariamine database. These items are stored in an intermediate items database (more about Items later).
  4. Reads from the items database, converts items to objects and loads them into the malariamine database.

This should complete after a couple of minutes, if you see an error message then see Troubleshooting tips.

If an error occurred during loading and you need to try again you need to re-initialise the database again by running clean build-db in dbmodel. This is only the case if dataloading actually started - if the following was displayed in the terminal:

[ant] load:
[ant]      [echo]
[ant]      [echo]       Loading uniprot-malaria (uniprot) tgt items into production DB
[ant]      [echo]

A useful command to initialise the database and load a source from the integrate directory is:

$ (cd ../dbmodel; ant clean build-db) && ant -Dsource=uniprot-malaria

Now that the data has loaded, log into the database and view the contents of the protein table:

$ psql malariamine
malariamine#  select count(*) from protein;

And see the first few rows of data:

malariamine#  select * from protein limit 5;

Object relational mapping

InterMine works with objects, objects are loaded into the production system and queries return lists of objects. These objects are persisted to a relational database. Internal InterMine code (the ObjectStore) handles the storage and retrieval of objects from the database automatically. By using an object model InterMine queries benefit from inheritance, for example the Gene and Exon classes are both subclasses of SequenceFeature. When querying for SequenceFeatures (representing any genome feature) both Genes and Exons will be returned automatically.

We can see how see how inheritance is represented in the database:

  • One table is created for each class in the data model.
  • Where one class inherits from another, entries are written to both tables. For example:
malariamine#  select * from gene limit 5;

The same rows appear in the sequencefeature table:

malariamine#  select * from sequencefeature limit 5;

All classes in the object model inherit from InterMineObject. Querying the intermineobject table in the database is a useful way to find the total number of objects in a Mine:

malariamine#  select count(*) from intermineobject;

All tables include an id column for unique ids and a class column with the actual class of that object. Querying the class column of intermineobject you can find the counts of different objects in a Mine:

malariamine#  select class, count(*) from intermineobject group by class;

A technical detail: for speed when retrieving objects and to deal with inheritance correctly (e.g. to ensure a Gene object with all of its fields is returned even if the query was on the SequenceFeature class) a serialised copy of each object is stored in the intermineobject table. When queries are run by the ObjectStore they actually return the ids of objects - these objects are may already be in a cache, if not the are retrieved from the intermineobject table.

Loading Genome Data from GFF3 and FASTA

We will load genome annotation data for P. falciparum from PlasmoDB

  • genes, mRNAs, exons and their chromosome locations - in GFF3 format:
  • chromosome sequences - in FASTA format

Data integration

Note that genes from the gff3 file will have the same primaryIdentifier as those already loaded from UniProt. These will merge in the database such that there is only one copy of each gene with information from both data sources. We will load the genome data then look at how data integration in InterMine works.

First, look at the information currently loaded for gene “PFL1385c” from UniProt:

malariamine=#  select * from gene where primaryIdentifier = 'PFL1385c';

GFF3 files

GFF3 is a standard format use to represent genome features and their locations. It is flexible and expressive and defined by a clear standard - An example of the file will load can be used to explain the format, each line represents one feature and has nine tab-delimited columns:

MAL1    ApiDB   gene    183057  184457  .       -       .       ID=gene.46311;description=hypothetical%20protein;Name=PFA0210c
MAL1    ApiDB   mRNA    183057  184457  .       +       .       ID=mRNA.46312;Parent=gene.46311
MAL1    ApiDB   exon    183057  184457  .       -       0       ID=exon.46313;Parent=mRNA.46312
col 1: “seqid”
an identifier for a ‘landmark’ on which the current feature is locatated, in this case ‘MAL1’, a ‘’P. falciparum’’ chromosome.
col 2: “source”
the database or algorithm that provided the feature
col 3: “type”
a valid SO term defining the feature type - here gene or mRNA
col 4 & 5: “start” and “end”
coordinates of the feature on the landmark in col 1
col 6: “score”
an optional score, used if the feature has been generated by an algorithm
col 7: “strand”
‘+’ or ‘-‘ to indicate the strand the feature is on
col 8: “phase”
for CDS features to show where the feature begins with reference to the reading frame
col 9: “attributes”
custom attributes to describe the feature, these are name/value pairs separated by ‘;’. Some attributes have predefined meanings, relevant here:
  • ID - identifier of feature, unique in scope of the GFF3 file
  • Name - a display name for the feature
  • Parent - the ID of another feature in the file that is a parent of this one. In our example the gene is a Parent of the mRNA.

A dot means there is no value provided for the column.

The files we are loading are from PlasmoDB and contain gene, exon and mRNA features, there is one file per chromosome. Look at an example:

$ less DATA_DIR/malaria/genome/gff/MAL1.gff3

The GFF3 source

InterMine includes a parser to load valid GFF3 files. The creation of features, sequence features (usually chromosomes), locations and standard attributes is taken care of automatically.

Many elements can be configured by properties in project.xml, to deal with any specific attributes or perform custom operations on each feature you can write a handler in Java which will get called when reading each line of GFF.

Other gff3 properties can be configured in the project.xml The properties set for malaria-gff are:

gff3.seqClsName = Chromosome
the ids in the first column represent Chromosome objects, e.g. MAL1
gff3.taxonId = 36329
taxon id of malaria
gff3.dataSourceName = PlasmoDB
the data source for features and their identifiers, this is used for the DataSet (evidence) and synonyms.
gff3.seqDataSourceName = PlasmoDB
the source of the seqids (chromosomes) is sometimes different to the features described
gff3.dataSetTitle = PlasmoDB P. falciparum genome
a DataSet object is created as evidence for the features, it is linked to a DataSource (PlasmoDB)

In some cases specific code is required to deal with attributes in the gff file and any special cases. A specific source can be created to contain the code to do this and any additions to the data model necessary. For malaria gff we need a handler to switch which fields from the file are set as primaryIdentifier and symbol/secondaryIdentifier in the features created. This is to match the identifiers from UniProt, it is quite a common issue when integrating from multiple data sources.

From the example above, by default: ID=gene.46311;description=hypothetical%20protein;Name=PFA0210c would make Gene.primaryIdentifier be gene.46311 and Gene.symbol be PFA0210c. We need PFA0210c to be the primaryIdentifier.

The malaria-gff source is held in the bio/sources/example-sources/malaria-gff directory. Look at the file in this directory, there are two properties of interest:

# set the source type to be gff

# specify a Java class to be called on each row of the gff file to cope with attributes
gff3.handlerClassName =

Look at the MalariaGFF3RecordHandler class in bio/sources/example-sources/malaria-gff/main/src/org/intermine/bio/dataconversion. This code changes which fields the ID and Name attributes from the GFF file have been assigned to.

$ less ~/git/intermine/bio/sources/example-sources/malaria-gff/main/src/org/intermine/bio/dataconversion/

Loading GFF3 data

Now load the malaria-gff source by running this command in malariamine/integrate:

$ ant -Dsource=malaria-gff -v

This will take a few minutes to run. Note that this time we don’t run build-db in dbmodel as we are loading this data into the same database as UniProt. As before you can run a query to see how many objects of each class are loaded:

$ psql malariamine
malariamine#  select class, count(*) from intermineobject group by class;

FASTA files

FASTA is a minimal format for representing sequence data. Files comprise a header with some identifier information preceded by ‘>’ and a sequence. At present the InterMine FASTA parser loads just the first entry in header after > and assigns it to be an attribute of the feature created. Here we will load one FASTA file for each malaria chromosome. Look at an example of the files we will load:

$ less DATA_DIR/malaria/genome/fasta/MAL1.fasta

The type of feature created is defined by a property in project.xml, the attribute set defaults to primaryIdentifier but can be changed with the fasta.classAttribute property. The following properties are defined in project.xml for malaria-chromosome-fasta:

fasta.className =
the type of feature that each sequence is for
fasta.dataSourceName = PlasmoDB
the source of identifiers to be created
fasta.dataSetTitle = PlasmoDB chromosome sequence
a DataSet object is created as evidence
fasta.taxonId = 36329
the organism id for malaria

fasta.includes = MAL*.fasta

This will create features of the class Chromosome with primaryIdentifier set and the Chromosome.sequence reference set to a Sequence object. Also created are a DataSet and DataSource as evidence.

Loading FASTA data

Now load the malaria-chromosome-fasta source by running this command in malariamine/integrate:

$ ant -Dsource=malaria-chromosome-fasta -v

This has integrated the chromosome objects with those already in the database. In the next step we will look at how this data integration works.

Data Integration

Data integration in MalariaMine

The sources uniprot-malaria and malaria-gff have both loaded information about the same genes. Before loading genome data we ran a query to look at the information UniProt provided about the gene “PFL1385c”:

malariamine=# select id, primaryidentifier, secondaryidentifier, symbol, length , chromosomeid, chromosomelocationid, organismid from gene where primaryIdentifier = 'PFL1385c';
    id    | primaryidentifier | secondaryidentifier | symbol | length | chromosomeid | chromosomelocationid | organismid
83000626 | PFL1385c          |                     | ABRA   |        |              |                      |   83000003
(1 row)

Which showed that UniProt provided primaryIdentifier and symbol attributes and set the organism reference. The id was set automatically by the ObjectStore and will be different each time you build your Mine.

Running the same query after malaria-gff is added shows that more fields have been filled in for same gene and that it has kept the same id:

malariamine=# select id, primaryidentifier, secondaryidentifier, symbol, length , chromosomeid, chromosomelocationid, organismid from gene where primaryIdentifier = 'PFL1385c';
    id    | primaryidentifier | secondaryidentifier | symbol | length | chromosomeid | chromosomelocationid | organismid
83000626 | PFL1385c          | gene.33449          | ABRA   |   2232 |     84017653 |             84018828 |   83000003
(1 row)

This means that when the second source was loaded the integration code was able to identify that an equivalent gene already existed and merged the values for each source, the equivalence was based on primaryIdentifier as this was the field that the two sources had in common.

Note that malaria-gff does not include a value for symbol but it did not write over the symbol provided by UniProt, actual values always take precedence over null values (unless configured otherwise).

Now look at the organism table:

malariamine=# select * from organism;
genus | taxonid | species | abbreviation |    id    | shortname | name |               class
      |   36329 |         |              | 83000003 |           |      | org.intermine.model.genomic.Organism
(1 row)

Three sources have been loaded so far that all included the organism with taxonId 36329, and more importantly they included objects that reference the organism. There is still only one row in the organism table so the data from three sources has merged, in this case taxonId was the field used to define equivalence.

How data integration works

Data integration works by defining keys for each class of object to describe fields that can be used to define equivalence for objects of that class. For the examples above:

  • primaryIdentifier was used as a key for Gene
  • taxonId ` was used as a key for `Organism

For each Gene object loaded by malaria-gff a query was performed in the malariamine database to find any existing Gene objects with the same primaryIdentifier. If any were found fields from both objects were merged and the resulting object stored.

Many performance optimisation steps are applied to this process. We don’t actually run a query for each object loaded, requests are batched and queries can be avoided completely if the system can work out no integration will be needed.

We may also load data from some other source that provides information about genes but doesn’t use the identifier scheme we have chosen for primaryIdentifier (in our example PFL1385c). Instead it only knows about the symbol (ABRA), in that case we would want that source to use the symbol to define equivalence for Gene.

Important points:

  • A primary key defines a field or fields of a class that can be used to search for equivalent objects
  • Multiple primary keys can be defined for a class, sources can use different keys for a class if they provide different identifiers
  • One source can use multiple primary keys for a class if the objects of that class don’t consistently have the same identifier type
  • null - if a source has no value for a field that is defined as a primary key then the key is not used and the data is loaded without being integrated.

Primary keys in MalariaMine

The keys used by each source are configured in the corresponding bio/sources/ directory.

For uniprot-malaria:

$ less ../../bio/sources/uniprot/resources/

And malaria-gff:

$ less ../../bio/sources/example-sources/malaria-gff/resources/

The key on Gene.primaryIdentifier is defined in both sources, that means that the same final result would have been achieved regardless of the order in the two sources were loaded.

These files define keys in the format:

Class.name_of_key = field1, field2

The name_of_key can be any string but you must use different names if defining more than one key for the same class, for example in there are two different keys defined for Gene:

Gene.key_primaryidentifier = primaryIdentifier
Gene.key_secondaryidentifier = secondaryIdentifier

It is better to use common names for identical keys between sources as this will help avoid duplicating database indexes.

Each key should list one or more fields that can be a combination of attributes of the class specified or references to other classes, in this cases there should usually be a key defined for the referenced class as well.

It is still possible to use a legacy method of configuring keys, where keys are defined centrally in dbmodel/resources/ and referenced in source files.

The tracker table

A special tracker table is created in the target database by the data integration system. This tracks which sources have loaded data for each field of each object. The data is used along with priorities configuration when merging objects but is also useful to view where objects have come from.

  • Look at the columns in the tracker table, objectid references an object from some other table
  • Query tracker information for the objects in the examples above:
select distinct sourcename from tracker, gene where objectid = id and primaryidentifier = 'PFL1385c';

select objectid, sourcename, fieldname, version from tracker, gene where objectid = id and primaryidentifier = 'PFL1385c';

select distinct sourcename from tracker, organism where objectid = id;

Updating Organism and Publication Information

Organisms and publications in InterMine are loaded by their taxon id and PubMed id respectively. The entrez-organism and update-publications sources can be run at the end of the build to examine the ids loaded, fetch details via the NCBI Entrez web service and add those details to the Mine.

Fetching organism details

You will have noticed that in previous sources and in project.xml we have referred to organisms by their NCBI Taxonomy id. These are numerical ids assigned to each species and strain. We use these for convenience in integrating data, the taxon id is a good unique identifier for organisms whereas names can come in many different formats: for example in fly data sources we see: ‘’Drosophila melanogaster’‘, ‘’D. melanogaster’‘, Dmel, DM, etc.

Looking at the organism table in the database you will see that the only column filled in is taxonid:

$ psql malariamine
malariamine#  select * from organism;

From the integrate directory run the entrez-organism source:

$ ant -v -Dsource=entrez-organism

This should only take a few seconds. This source does the following:

  • runs a query in the production database for all of the taxon ids
  • creates an NCBI Entrez web service request to fetch details of those organisms
  • converts the data returned from Entrez into a temporary Items XML file
  • loads the Items XML file into the production database

Now run the same query in the production database, you should see details for ‘’P. falciparum’’ added:

$ psql malariamine
malariamine#  select * from organism;


As this source depends on organism data previously loaded it should be one of the last sources run and should appear at the end of <sources> in project.xml.

Fetching publication details

Publications are even more likely to be cited in different formats and are prone to errors in their description. We will often load data referring to the same publication from multiple sources and need to ensure those publications are integrated correctly. Hence we load only the PubMed id and fetch the details from the NCBI Entrez web service as above.

Several InterMine sources load publications:

malariamine#  select count(*) from publication;
malariamine#  select * from publication limit 5;

Now run the update-publications source to fill in the details:

$ ant -v -Dsource=update-publications

As there are often large numbers of publications they are retrieved in batches from the web service.

Now details will have been added to the publication table:

malariamine#  select * from publication where title is not null limit 5;

Sometimes, especially with very large numbers of publications, this source will fail to fetch details correctly. Usually running it again will work correctly.

Occasionally erroneous PubMed ids are included from some sources and their details will not be updated, there is no good way to deal with this situation.


As this source depends on publication data previously loaded it should be one of the last sources run and should appear at the end of <sources> in project.xml.

Post Processing

Post-processing steps are run after all data is loaded, they are specified as <post-process> elements in project.xml.

Some of these can only be run after data from multiple sources are loaded. For example, for the Malaria genome information we load features and their locations on chromosomes from malaria-gff but the sequences of chromosomes from malaria-chromosome-fasta. These are loaded independently and the Chromosome objects from each are integrated, neither of these on their own could set the sequence of each Exon. However, now they are both loaded the transfer-sequences post-process can calculate and set the sequences for all features located on a Chromosome for which the sequence is known.

Some post-process steps are used to homogenize data from different sources or fill in shortcuts in the data model to improve usability - e.g. create-references.

Finally, there are post-process operations that create summary information to be used by the web application: summarise-objectstore, create-search-index and create-autocomplete-indexes.

MalariaMine Post Processing

The following <post-process> targets are included in the MalariaMine project.xml. The post-processes are run as a single stage of the build process. (see step 11.2 below for how to run the post-processing steps).

Run queries listed here before and after running the post-processing to see examples of what each step does.


This fills in some shortcut references in the data model to make querying easier. For example, Gene has a collection of transcripts and Transcript has a collection of exons. create-references will follow these collections and create a gene reference in Exon and the corresponding exons collection in Gene.

malariamine#  select * from exon limit 5;

The empty geneid column will be filled in representing the reference to gene.


The sequence for chromosomes is loaded by malaria-chromosome-fasta but no sequence is set for the features located on them. This step reads the locations of features, calculates and stores their sequence and sets the sequenceid column. The sequenceid column for this exon is empty:

malariamine# select * from exon where primaryidentifier = 'exon.32017';

After running transfer-sequences the sequenceid column is filled in.


Each source can also provide code to execute post-process steps if required. This command loops through all of the sources and checks whether there are any post-processing steps configured. There aren’t any for the sources we are using for MalariaMine but you should always include the do-sources element.

summarise-objectstore, create-search-index & create-autocomplete-index

These generate summary data and indexes used by the web application, see WebappConfig.

Run the post-procesing

To run all the post-processing steps:

$ cd ../postprocess
$ ant -v

This will take a few minutes. When complete you can re-run the queries above to see what has been added.

Post-processing steps can also be run individually,

Building a Mine

So far we have created databases, integrated data and run post-processing with individual ant targets. InterMine includes a perl program called project_build that reads the project.xml definition and runs all of the steps in sequence. It also has the option of dumping the production database during the build and recovering from these dumps in case of problems.

Build complete MalariaMine

Build MalariaMine now using the project_build script, we will need a completed MalariaMine for the webapp.

Run the project_build script from your malariamine directory:

$ ../bio/scripts/project_build -b -v localhost ~/malariamine-dump

This will take ~15-30mins to complete.


If you encounter an “OutOfMemoryError”, you should set your $ANT_OPTS variable, see Troubleshooting tips

Deploying the web application

Once you have read access to a production database, you can build and release a web application against it.


In the ~/.intermine directory, update the webapp properties in your file]. Update the following properties:

  • tomcat username and password
  • superuser username and password


The userprofile database stores all user-related information such as username and password, tags, queries, lists and templates.

  1. Configure

Update your file with correct information for the db.userprofile-production database:

  1. Create the empty database:
$ createdb userprofile-malariamine
  1. Build the database:
# in malariamine/webapp
$ ant build-db-userprofile


The build-db and build-db-userprofile commands rebuild the database and thus will delete any data.

This command creates the SuperUser account and loads the default-template-queries.xml file. You only need to build the userprofile database once.

Deploying the webapp


Tomcat is the webserver we use to launch InterMine webapps. Start Tomcat with this command:

# from the directory where tomcat is installed.
$ bin/

Visit the Tomcat manager at http://localhost:8080/. The username and password required to access the manager are webapp.manager and webapp.password as specified in


There are extra steps to take if you are using Tomcat 7. See Tomcat for details.


Run the following command to release your webapp:

# in malariamine/webapp
$ ant default remove-webapp release-webapp

This will fetch the model from the database and generate the model java code, remove and release the webapp. The default target forces a rebuild of the .war file. If you’ve made updates, you may want to add clean, which removes temporary directories.

Using the webapp

Navigate to http://localhost:8080/malariamine to view your webapp. The path to your webapp is the webapp.path value set in


Now that you have a database and a working webapp, you’ll want to know how to add your own logo, pick a colour scheme, modify how data is displayed etc. Our webapp tutorial is a detailed guide on how to customise all parts of the InterMine web application.



Anytime you run ant and something bad happens, add the verbose tag:

$ ant -v

This will give you more detailed output and hopefully a more helpful error message.


If the error occurs while you are integrating data, the error message will be in the intermine.log file in the directory you are in.

If the error occurs while you are browsing your webapp, the error message will be located in the Tomcat logs: $TOMCAT/logs.