InterMine 4.0 – InterMine as a FAIR framework

We are excited to publish the latest version of InterMine, version 4.0.

It’s a collection of our efforts to make InterMine more “FAIR“. As an open source data warehouse, InterMine’s raison d’être is to be a framework that enables people to quickly and easily provide public access to their data in a user friendly manner. Therefore InterMine has always strived to make data Findable, Accessible, Interoperable and Reusable and this push is meant to formally apply the FAIR principles to InterMine.

What’s included in this release?

  1. Generate globally unique and stable URLs to identify InterMine data objects in order to provide more findable and accessible data.
  2. Apply suitable ontologies to the core InterMine data model to make the semantic of InterMine data explicit and facilitate data exchange and interoperability
  3. Embed metadata in InterMine web pages to make data more findable
  4. Improve accessibility of data licenses for integrated sources via web interface and REST web-service.

More details below!

How to upgrade?

This is a non-disruptive release, but there are additions to the data model. Therefore, you’ll want to increment your version, then build a new database when upgrading. No other action is required.

However, keep reading for how to take advantages of the new FAIR features in this release.

Unique and stable URLs

We’ve added a beautiful new user-friendly URL.

Example: http://beta.flymine.org/beta/gene:FBgn0000606

Currently this is used only in the “share” button in the report pages and in the web pages markup. In the future, this will be the only URL seen in the browser location bar.

For details on how to configure your mine’s URLs, see the docs here.

See our previous blog posts on unique identifiers.

Decorating the InterMine data model with ontology terms

InterMine 4.0 introduces the ability to annotate your InterMine data model with ontology terms.

While these data are not used (yet), it’s an important feature in that it’s going to facilitate cross-InterMine querying, and eventually cross-database analysis — allowing us to answer questions like “Is the ‘gene’ in MouseMine the same ‘gene’ at the EBI?”.

For details on how to add ontologies to your InterMine data model, see the docs here.

Embedding metadata in InterMine webpages

We’ve added structured data to web pages in format of JSON-LD to make data more findable, and these data are indexed by Google data search. Bioschemas.org is extending Schema.org with life science-specific types, adding required properties and cardinality on the types. For more details see the docs here.

By default this feature is disabled. For details on how to enable embedding metadata in your webpages, see the docs here.

Data licences

In our ongoing effort to make the InterMine system more FAIR, we have started working on improving the accessibility of data licences, retaining licence information supplied by the data sources integrated in InterMine, and making it available to humans via our web application and machines via queries.

See our previous blog post on data licences.

For details on how to add data licences to your InterMine, see the docs.

Future FAIR plans

  1. Provide a RDF representation of data stored, lists and query results, and the bulk download of all InterMine in RDF form, in order to allow the users to import InterMine resources into their local triplestore
  2. Provide an infrastructure for a SPARQL endpoint where the user can perform federated queries over multiple data sets

Upcoming Releases

The next InterMine version will likely be ready in the Fall/Winter and include some user interface updates.

Docs

To update your mine with these new changes, see upgrade instructions. This is a non-disruptive release.

See release notes for detailed information.

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Data integration and Machine Learning for drug target validation

Hi!

In this blog post I would like to give a brief overview of what I’m currently working on.

Knowledge Transfer Partnership: what & why?

First, in order to give context to this post, last year InterMine at University of Cambridge and STORM Therapeutics, a spin-out of University of Cambridge working on small modulating RNA enzymes for the treatment of cancer, were awarded a Knowledge Transfer Partnership (KTP) from the UK Government (read this post for more information). With this award, the objective is to help STORM Therapeutics advance their efforts in cancer research, and contribute to their ultimate goal of drug target validation.

As part of the KTP Award, a KTP Associate needs to be appointed by both the knowledge base (University of Cambridge) and the company (STORM). The role of the KTP Associate is to act as the KTP Project Manager and is in charge of the successful delivery of the project. For this project, I was appointed as the KTP Associate, with a Research Software Engineer / Research Associate role at the University of Cambridge, for the total duration of the project: 3 years.

Machine learning and a new mine: StormMine

Now that you know what the KTP project is about, and who is delivering it, let’s move on to more interesting matters. In order to successfully delivering this project, the idea is to use the InterMine data warehouse to build a knowledge base for the company, STORM, that enables their scientist to have all the relevant data for their research in a single, integrated, place. For this reason, several new data sources will be integrated into a STORM’s deployment of the InterMine data warehouse (StormMine, from now on), and appropiate data visualizations will be added.

Then, once the data is integrated, we can think towards analysing the data to gather insights that may help the company goals, such as applying statistical and Machine Learning methods to gather information from the data, as well as building computational intelligence models. This leads the way towards what I’ve been working on since my start in February, and will continue until July 2019.

In general terms, I’m currently focused on building Machine Learning models that are able to learn how to differentiate between known drug targets and non-targets from available biological data. This part of work is going to be used as my Master’s Thesis, which I hopefully will deliver in July! Moreover, with this analysis, we will be able to answer three extremely relevant questions for STORM, and which are the questions leading the current work on the project. These questions are

  1. Which are the most promising target genes for a cancer type?
  2. Which features are most informative in predicting novel targets?
  3. Given a gene, for which cancer types is it most relevant?

If you are interested in learning more about this work, stay tuned for next posts, and don’t hesitate contacting me, either by email (ar989@cam.ac.uk) or connect with me in LinkedIn (click here)!