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BioInterchange has been superseded by BioInterchange 2!

Legacy Documentation

BioInterchange is a tool for generating interchangable RDF data from non-RDF data sources.

Supported input file formats (see examples directory):

Supported RDF output formats:

Ontologies used in the RDF output:

Note: GFVO replaces the Generic Feature Format Version 3 Ontology (GFF3O) and Genome Variation Format Version 1 Ontology (GVF1O).


If you like to contribute, you are more than welcome. For implementation ideas, please have a look here.


Four interfaces to BioInterchange are available:

  1. command-line tool-suite
  2. API (Ruby gem, Python egg)
  3. RESTful web-service
  4. interactive web-site

Command-Line Tool-Suite

BioInterchange's command-line tool biointerchange can be installed as a command line tools as follows:

gem install biointerchange



biointerchange --input biointerchange.gvf --rdf rdf.biointerchange.gfvo --file examples/estd176_Banerjee_et_al_2011.2012-11-29.NCBI36.gvf
biointerchange --input dbcls.catanns.json --rdf rdf.bh12.sio --file examples/pubannotation.10096561.json --annotate_name 'Peter Smith' --annotate_name_id ''
biointerchange --input --rdf rdf.bh12.sio --file examples/gb-2007-8-3-R40.xml --annotate_name 'Peter Smith' --annotate_name_id ''
biointerchange --input phylotastic.newick --rdf rdf.phylotastic.newick --file examples/ --annotate_date '1 June 2006'

Input formats:

  • biointerchange.gff3
  • biointerchange.gvf
  • dbcls.catanns.json
  • phylotastic.newick

Output formats:

  • rdf.biointerchange.gfvo
  • rdf.bh12.sio
  • rdf.phylotastic.newick

Using a Triple Store

RDF data produced by BioInterchange can be directly loaded into a triple store. The following gives an example about loading and querying RDF data using Sesame; the commands are entered via Sesame's bin/

> create memory.
Please specify values for the following variables:
Repository ID [memory]: testrepo
Repository title [Memory store]: Test Repository
Persist (true|false) [true]: false
Sync delay [0]: 
Repository created
> open testrepo.
testrepo> load <path-to-your-rdf-data> .
testrepo> sparql select * where { ?s ?p ?o } .

To list all seqid entries from a GVF-file conversion in the store, the following SPARQL query can be used:

testrepo> sparql select * where { ?s <> ?o } .

Example Data Provenance

The following list provides information on the origin of the example-data files in the examples directory:

  • Newick formatted Bininda-Emonds mammals tree (see The delayed rise of present-day mammals). Downloaded from
  • BovineGenomeChrX.gff3.gz: Gzipped GFF3 file describing a Bos taurus chromosome X. Downloaded from
  • chromosome_BF.gff: GFF3 file of floating contigs from the Baylor Sequencing Centre. Downloaded from
  • estd176_Banerjee_et_al_2011.2012-11-29.NCBI36.gvf: GVF file of EBI's DGVa. Downloaded from
  • Felis_catus.gvf.gz: Gzipped GVF file of F. catus genomic variations provided by the Ensembl project. Downloaded from
  • Felis_catus_incl_consequences.vcf.gz: Gzipped VCF file of F. catus genomic variations provided by the Ensembl project. Downloaded from
  • gb-2007-8-3-R40.xml: Generated by PDFx from open-access source PDF Sense-antisense pairs in mammals: functional and evolutionary considerations
  • Saccharomyces_cerevisiae_incl_consequences.gvf.gz: Gzipped GVF files of S. cerevisiae genomic variations provided by the Ensembl project. Downloaded from

Additional Example Data

The following lists data that has been used in testing BioInterchange's implementation, but was not included in the GitHub repository due to their size:

  • mgp.v4.indels.dbSNP.vcf.gz: Gzipped VCF file of M. musculus indels by the Sanger Institute. Downloaded from

Application Programming Interface


BioInterchange is available as Ruby gem that can be installed as follows:

sudo gem install biointerchange

The API provides vocabulary wrappers to ontologies that are used within the BioInterchange framework as well as access to RDFization implementations.

Using Vocabulary Wrappers

Ruby classes are provided for the ontologies that is used for serializing RDF. Each ontology is represented by its own Ruby class. The classes provide access to the ontology terms and additional methods for resolving OWL classes, datatype properties and object properties.

Usage example (see also vocabulary.rb):

require 'rubygems'
require 'biointerchange'

include BioInterchange

def print_resource(resource)
  puts "    #{resource}"
  puts "        Ontology class:             #{GFF3O.is_class?(resource)}"
  puts "        Ontology object property:   #{GFF3O.is_object_property?(resource)}"
  puts "        Ontology datatype property: #{GFF3O.is_datatype_property?(resource)}"

# Get the URI of an ontology term by label:
puts "'seqid' property:"

# Ambiguous labels will return an array of URIs:
# "start" can refer to a sub-property of "feature_properties" or "target_properties"
puts "'start' properties:"
GFF3O.start().each { |start_synonym|
# "feature_properties" can be either a datatype or object property
puts "'feature_properties' properties:"
GFF3O.feature_properties().each { |feature_properties_synonym|

# Use build-in method "is_datatype_property" to resolve ambiguity:
# (Note: there is exactly one item in the result set, so the selection of the first item is acceptable.)
feature_properties = GFF3O.feature_properties().select { |uri| GFF3O.is_datatype_property?(uri) }
puts "'feature_properties' properties, which are a datatype property:"
feature_properties.each { |feature_property|

# Use build-in method "with_parent" to pick properties based on their context:
puts "'start' property with parent datatype property 'feature_properties':"
GFF3O.with_parent(GFF3O.start(), feature_properties[0]).each { |feature_property|

With the BioInterchange gem installed, the example can be executed on the command line via:

git clone git://
cd BioInterchange
git checkout v1.0.0
ruby examples/vocabulary.rb
RDFization Framework

Usage example (see also rdfization.rb):

require 'rubygems'
require 'biointerchange'

include BioInterchange::Phylogenetics

# Create a reader that reads phylogenetic trees in Newick format:
reader =

# Create a model from a single example tree:
# (Note: the `deserialize` method also takes streams as parameter -- not just strings.)
model = reader.deserialize('((B:0.2,(C:0.3,D:0.4)E:0.5)F:0.1)A;')

# Serialize the model as RDF N-Triples to STDOUT:
Implementing New Readers, Models and Writers

New readers, models and writers are best adopted from or build upon the existing implementations. The phylogenetic trinity of Newick file format reader, BioRuby based tree model, and CDAO RDF writer is used here as a guidline due to its simplicity.

Reader: Creating an Object Model

The quintessential Newick tree reader is depicted below. Its class is placed in a Ruby module that encapsulates all phylogenetic related source code. The NewickReader class inherits from the BioInterchange framework class Reader that provides method stubs which need to be overwritten. Using the central registry BioInterchange::Registry, the reader informs the framework of its: unique identifier (phylotastic.newick), Ruby class (NewickReader), command line parameters that it accepts (date, which becomes --annotate_date), whether the reader can operate without reading the complete input all at once (true), a descriptive name of the reader (Newick Tree [...]), and an array with descriptions for each parameter stated above.

Deserialization of Newick trees is done using the deserialize method, which must take both strings and input streams as valid arguments. If this contraint is not satisfied, then an ImplementationReaderError is thrown that is caught by the framework and handled appropriately.

Finally, the postponed? method keeps track of deferred input processing. If the batch size was reached and the model was passed on for serialization to a writer, then this method will have to return true.

require 'bio'
require 'date'

module BioInterchange::Phylogenetics

class NewickReader < BioInterchange::Reader

  # Register reader:
    [ 'date' ],
    'Newick Tree File Format reader',
      [ 'date <date>', 'date when the Newick file was created (optional)' ]

  # Creates a new instance of a Newick file format reader.
  # The reader supports batch processing.
  # +date+:: Optional date of when the Newick file was produced, annotated, etc.
  # +batch_size+:: Optional integer that determines that number of features that
  # should be processed in one go.
  def initialize(date = nil, batch_size = nil)
    @date = date
    @batch_size = batch_size

  # Reads a Newick file from the input stream and returns an associated model.
  # If this method is called when +postponed?+ returns true, then the reading will
  # continue from where it has been interrupted beforehand.
  # +inputstream+:: an instance of class IO or String that holds the contents of a Newick file
  def deserialize(inputstream)
    if inputstream.kind_of?(IO)
    elsif inputstream.kind_of?(String) then
      raise BioInterchange::Exceptions::ImplementationReaderError, 'The provided input stream needs to be either of type IO or String.'

  # Returns true if the reading of the input was postponed due to a full batch.
  def postponed?


# ...concrete implementation omitted.
Tree Model

A model is created by a reader and it is subsequently consumed by a writer. The phylogenetic tree model inherits BioInterchange::Model which defines the prune method. If batch operation is in place, i.e. the input is not completely read into memory, then the prune method will be called to instruct the model to drop all information that has not to be kept in memory anymore. In a sense, this can be seen as a form of garbage collection, where data that has been serialized is purged from memory.

module BioInterchange::Phylogenetics

# A phylogenetic tree set that can contain multiple phylogenetic trees.
class TreeSet < BioInterchange::Model

  # Create a new instance of a tree set. A tree set can contain multiple phylogenetic trees.
  def initialize
    # Trees are stored as the keys of a hash map to increase performance:
    @set = {}

  # ...omitted internal data structure handling.

  # Removes all features from the set, but keeps additional data (e.g., the date).
  def prune


Writer: From Object Model to RDF

The writer takes an object model and serializes it via the BioInterchange::Writer derived serialize method. A writer uses BioInterchange::Registry to make itself known to the BioInterchange framework, where it signs up using the following arguments: a unique identifier (rdf.phylotastic.newick), its implementing class (CDAORDFWriter), a list of readers that it is compatible with (phylotastic.newick), whether the writer supports batch processing where only parts of the input need to be kept in memory (true), and a descriptive name for the writer.

require 'rdf'
require 'rdf/ntriples'

module BioInterchange::Phylogenetics

# Serialized phylogenetic tree models based on BioRuby's phylogenetic tree implementation.
class CDAORDFWriter < BioInterchange::Writer

  # Register writers:
    [ 'phylotastic.newick' ],
    'Comparative Data Analysis Ontology (CDAO) based RDFization'

  # Creates a new instance of a CDAORDFWriter that will use the provided output stream to serialize RDF.
  # +ostream+:: instance of an IO class or derivative that is used for RDF serialization
  def initialize(ostream)
    @ostream = ostream

  # Serialize a model as RDF.
  # +model+:: a generic representation of input data that is an instance of BioInterchange::Phylogenetics::TreeSet
  # +uri_prefix+:: optional URI prefix that should be used in the RDFization of individuals/class instances
  def serialize(model, uri_prefix = nil)
    model.contents.each { |tree|
      serialize_model(model, tree, uri_prefix)


# ...omitted actual serialization implementation.


Currently, there are only wrappers to the vocabularies of the ontologies that are used by BioInterchange available.

To install the BioInterchange egg, run:

sudo easy_install rdflib
sudo easy_install

Usage examples:

import biointerchange
from biointerchange import *
from rdflib.namespace import Namespace

def print_resource(resource):
    print "    " + resource
    print "        Ontology class:             " + str(GFF3O.is_class(resource))
    print "        Ontology object property:   " + str(GFF3O.is_object_property(resource))
    print "        Ontology datatype property: " + str(GFF3O.is_datatype_property(resource))

# Get the URI of an ontology term by label:
print "'seqid' property:"

# Ambiguous labels will return an array of URIs:
# "start" can refer to a sub-property of "feature_properties" or "target_properties"
print "'start' properties:"
for start_synonym in GFF3O.start():

# "feature_properties" can be either a datatype or object property
print "'feature_properties' properties:"
for feature_properties_synonym in GFF3O.feature_properties():

# Use build-in method "is_datatype_property" to resolve ambiguity:
# (Note: there is exactly one item in the result set, so the selection of the first item is acceptable.)
feature_properties = filter(lambda uri: GFF3O.is_datatype_property(uri), GFF3O.feature_properties())
print "'feature_properties' properties, which are a datatype property:"
for feature_property in feature_properties:

# Use build-in method "with_parent" to pick properties based on their context:
print "'start' property with parent datatype property 'feature_properties':"
for feature_property in GFF3O.with_parent(GFF3O.start(), feature_properties[0]):

The example can be executed on the command line via:

git clone git://
cd BioInterchange
git checkout v1.0.0
cd supplemental/python


Only vocabulary wrapper classes are provided for the Java API. In order to make use of the RDF generation features in BioInterchange, either use the Ruby implementation or connect Java to BioInterchange's web-services.

To use the BioInterchange artifact, set-up add the following to your Maven POM file:


Usage examples of accessing GFF3O's vocabulary:

package org.biointerchange;

import com.hp.hpl.jena.rdf.model.*;
import com.hp.hpl.jena.vocabulary.*;
import org.apache.commons.collections.CollectionUtils;
import org.apache.commons.collections.Predicate;

import java.util.Set;

import org.biointerchange.vocabulary.*;

 * Demo on how to make use of BioInterchange's vocabulary classes.
 * @author Joachim Baran
public class App 
    public static void main(String[] args) {
        Resource seqid = GFF3O.seqid();
        System.out.println("'seqid' property:");
        System.out.println("'start' properties:");
        Set<Resource> start = GFF3O.start();
        for (Resource startSynonym : start)
        System.out.println("'feature_properties' properties:");
        Set<Resource> featureProperties = GFF3O.feature_properties();
        for (Resource featurePropertiesSynonym : featureProperties)
        System.out.println("'feature_properties' properties, which are a datatype property:");
        CollectionUtils.filter(featureProperties, new Predicate() {
            public boolean evaluate(Object o) {
                return GFF3O.isDatatypeProperty((Resource)o);
        for (Resource featurePropertiesSynonym : featureProperties)
        System.out.println("'start' property with parent datatype property 'feature_properties':");
        Set<Resource> startUnderDatatypeFeatureProperties = GFF3O.withParent(start, featureProperties.iterator().next());
        for (Resource startSynonym : startUnderDatatypeFeatureProperties)
    private static void printResource(Resource resource) {
        System.out.println("    " + resource.toString());
        System.out.println("        Namespace:                            " + resource.getNameSpace());
        System.out.println("        Local name:                           " + resource.getLocalName());
        System.out.println("        Jena Property (rather than Resource): " + (resource instanceof Property));
        System.out.println("        Ontology class:                       " + GFF3O.isClass(resource));
        System.out.println("        Ontology object property:             " + GFF3O.isObjectProperty(resource));
        System.out.println("        Ontology datatype property:           " + GFF3O.isDatatypeProperty(resource));

Another example that uses SIO instead of GFF3O is provided as

The examples can be executed through Maven:

cd supplemental/java/biointerchange
mvn exec:java -Dexec.mainClass="org.biointerchange.App"
mvn exec:java -Dexec.mainClass="org.biointerchange.AppSIO"

RESTful Web-Service

A RESTful web-service is available via the URI:

RDFization parameters and data are send as a single HTTP POST requests containing a JSON object. The JSON object has to be formatted as follows:

  "parameters" : {
    "input" : "INPUT_FORMAT",
    "output": "OUTPUT_METHOD"
  "data" : "URL_ENCODED_DATA"
  • INPUT_FORMAT: determines the input data type; available input formats are
  • OUTPUT_METHOD: determines the RDFization method that should be used, output will always be RDF N-Triples; available output formats are
    • rdf.biointerchange.gfvo: RDFization of biointerchange.gff3 or biointerchange.gvf
    • rdf.bh12.sio: RDFization of dbcls.catanns.json or
    • rdf.phylotastic.newick: RDFization of phylotastic.newick
  • URL_ENCODED_DATA: data for RDFization as URL encoded string


A query example is part of BioInterchange's source repository. The file webservice_example.json contains the following query:

    "parameters" : {
      "input" : "biointerchange.gff3",
      "output": "rdf.biointerchange.gff3"
    "data" : "ChrX.38%09bovine_complete_cds_gmap_perfect%09gene%0915870%0916254%09.%09+%09.%09ID%3DBC109609_ChrX.38%0AChrX.38%09bovine_complete_cds_gmap_perfect%09mRNA%0915870%0916254%09.%09+%09.%09ID%3Dbovine_complete_cds_gmap_perfect_BC109609_ChrX.38%3BParent%3DBC109609_ChrX.38%0AChrX.38%09bovine_complete_cds_gmap_perfect%09CDS%0915870%0916254%09.%09+%090%09Parent%3Dbovine_complete_cds_gmap_perfect_BC109609_ChrX.38%0AChrX.38%09bovine_complete_cds_gmap_perfect%09exon%0915870%0916254%09.%09+%090%09Parent%3Dbovine_complete_cds_gmap_perfect_BC109609_ChrX.38%0A"

The query can be run using the popular cURL tool:

curl -d '@webservice_example.json'

Interactive Web-Site

BioInterchange has an interactive web-interface for RDFizing small amounts of data. Each input format and RDF serialization method pair comes with an example, which can be used as a guidance or test bed for learning how to use BioInterchange.

Usage Instructions

  1. select a data input format (for example, GFF3)
  2. select a RDF serialization method/output format (for example, "RDF using GFF3O ontology")
  3. paste RDF serialization method parameters and data in the text fields (or, click "Paste Input-Specific Example")
  4. click "Generate RDF" and the RDFized data will appear below

Build Notes

This section is only relevant if you are building newer versions of BioInterchange yourself. If you are using the Ruby gem, web-service or interactive web-site, then you can safely skip the steps explained here.

Note that the following set-up only works with Ruby 1.9.2p290 or newer.


Software requirements:

  • Ruby 1.9.2p290 or newer
  • Bundler gem 1.1.5 or newer
  • Rake gem 0.8.7 or newer

With Ruby installed, the following commands install the additional packages:

sudo gem install bundler
sudo gem install rake

The last step, bundle, will install gem dependencies of BioInterchange automatically.

Building Vocabulary Classes

Building a new version of the Ruby vocabulary classes for CDAO, FALDO, GFF3O, GVF1O, SIO, SOFA (requires that the OBO files are saves as RDF/XML using Protege; Apache Jena's rdfcat tool is required to reformat RDF Turtle as RDF/XML):

sudo gem install rdf
sudo gem install rdf-rdfxml
echo -e "require 'rdf'\nmodule BioInterchange\n" > lib/biointerchange/cdao.rb
ruby generators/rdfxml.rb <path-to-rdf/xml-version-of-cdao> CDAO >> lib/biointerchange/cdao.rb
echo -e "\nend" >> lib/biointerchange/cdao.rb
echo -e "require 'rdf'\nmodule BioInterchange\n" > lib/biointerchange/faldo.rb
rdfcat -ttl <path-to-turtle-version-of-faldo> > faldo.xml.tmp
ruby generators/rdfxml.rb faldo.xml.tmp FALDO >> lib/biointerchange/faldo.rb
rm -f faldo.xml.tmp
echo -e "\nend" >> lib/biointerchange/faldo.rb
echo -e "require 'rdf'\nmodule BioInterchange\n" > lib/biointerchange/gfvo.rb
ruby generators/rdfxml.rb <path-to-rdf/xml-version-of-gfvo> GFVO >> lib/biointerchange/gfvo.rb
echo -e "\nend" >> lib/biointerchange/gfvo.rb
echo -e "module BioInterchange\n" > lib/biointerchange/sio.rb
ruby generators/rdfxml.rb <path-to-rdf/xml-version-of-sio> SIO >> lib/biointerchange/sio.rb
echo -e "\nend" >> lib/biointerchange/sio.rb
echo -e "module BioInterchange\n" > lib/biointerchange/so.rb
ruby generators/rdfxml.rb <path-to-rdf/xml-version-of-so> SO >> lib/biointerchange/so.rb
echo -e "\nend" >> lib/biointerchange/so.rb
echo -e "module BioInterchange\n" > lib/biointerchange/sofa.rb
ruby generators/rdfxml.rb <path-to-rdf/xml-version-of-sofa> SOFA >> lib/biointerchange/sofa.rb
echo -e "\nend" >> lib/biointerchange/sofa.rb

A Geno Ontology external reference (GOxref) vocabulary can be created by directly downloading the latest version of GO.xrf_abbs:

echo -e "module BioInterchange\n" > lib/biointerchange/goxref.rb
curl | ruby generators/GOxrefify.rb
echo -e "\nend" >> lib/biointerchange/goxref.rb

Building an external reference vocabulary based on Life Science Registry external database abbreviations (based on download of the Life Science registry spreadsheet as TSV file):

echo -e "module BioInterchange\n" > lib/biointerchange/life_science_registry.rb
cut -f 1,25 <path-to-registry-tsv-file> | grep -E 'https?://.*\$id' | ruby generators/tsv2rubyclass.rb LifeScienceRegistry >> lib/biointerchange/life_science_registry.rb
echo -e "\nend" >> lib/biointerchange/life_science_registry.rb

Python Vocabulary Classes

The source-code generation can be skipped, if none of the ontologies that are used by BioInterchange have been changed. Otherwise, the existing Python vocabulary class wrappers can be generated as follows:

ruby generators/make_supplement_releases.rb

Generate the BioInterchange Python vocabulary egg:

cd supplemental/python
python bdist_egg
Required Python Library

The vocabulary wrapper makes used of RDFLib, which does not install automatically with the egg.

Java Vocabulary Classes

The source-code generation can be skipped, if none of the ontologies that are used by BioInterchange have been changed. Otherwise, the existing Java vocabulary class wrappers can be generated as follows:

ruby generators/make_supplement_releases.rb

Generate the BioInterchange Java vocabulary artifact:

cd supplemental/java/biointerchange
mvn package
Required Java Packages

The following Java packages will automatically install alongside BioInterchange's Maven artifact:

Gem Bundling/Installing

Mac OS X prerequisites and bundle install difference:

sudo port install libxml2 libxslt
sudo ARCHFLAGS=-Wno-error=unused-command-line-argument-hard-error-in-future bundle install

Actual gem bundling:

bundle exec rake gemspec
bundle exec gem build biointerchange.gemspec
sudo bundle exec gem install biointerchange-`cat VERSION`.gem

If you encounter problems with gem dependencies, then you can try to explictly use Ruby 1.9:

bundle exec gem1.9 build biointerchange.gemspec
sudo bundle exec gem1.9 install biointerchange-`cat VERSION`.gem

Alternative build script,, which installs the gem without RDocs and ri pages (quicker when testing):


Unit Testing

BioInterchange uses unit testing using RSpec, where the unit tests are located in the spec directory.

Using bundler, a quick check can be carried out using:

bundle update
bundle exec rake spec

A more verbose is produced by calling rspec directly:

rspec -c -f d

Generating RDocs

bundle exec rake rdoc

Deploying on Rubygems

Note: Only BioInterchange package maintainers can deploy the 'biointerchange' gem on Rubygems.

bundle exec rake version:bump:(major | minor | patch)
bundle exec rake gemspec
bundle exec gem build biointerchange.gemspec
bundle exec gem push biointerchange-VERSION.gem


GCC: No such file or directory

On Mac OS X, you might see this error:

make: /usr/bin/gcc-4.2: No such file or directory
make: *** [generator.o] Error 1

This can be solved by executing:

sudo ln -s /usr/bin/llvm-gcc-4.2 /usr/bin/gcc-4.2


In alphabetical order of the last name:


If you use this software, please cite

  • BioInterchange: An Open Source Framework for Transforming Heterogeneous Data Formats Into RDF (in preparation)

and one of the following Biogem publications

This Biogem is published at #biointerchange and hosted on its primary site

The BioRuby community is on IRC server:, channel: #bioruby.


See LICENSE file.


Generates interchangeable RDF from non-RDF data sources.







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