Announcement: Protein Signaling conference

This year I am once again involved in organizing an exclusive conference on protein signaling. There is no registration fee and accommodation is also free; all you have to pay yourself is your travel expenses.

Click the image to see the poster in full size.

This year we are fortunate to once again have an amazing lineup of invited speakers: Albert Heck, Anne-Claude Gavin, Bernd Bodenmiller, Brenda Schulman, Daniel Durocher, Gianni Cesareni, Giulio Superti-Furga, Ileana Cristea, Ivan Dickic, James Ferrell, Jason Chin, Jiri Lukas, Julio Saez-Rodriguez, Marc Kirschner, Matthias Mann, Nevan Krogan, Niels Mailand, Oskar Fernandez-Capetillo, Ray Deshaies, Ronald Hay, Steve Jackson, Søren Brunak, Titia Sixma, and Wade Harper.

Please note that although the poster says July 1, the application deadline is in fact June 20, which is only four days from now. To apply, please see the conference website.

Resource: The TISSUES database on tissue expression of genes and proteins

As mentioned in the last entry, 2015 has been a year of publishing web resources for my group. The COMPARTMENTS and DISEASES databases have yet another sister resource, namely TISSUES.

This web resource allows users to easily obtain a color-coded schematic of the tissue expression of a protein of interest, providing an at-a-glance overview of evidence from database annotations, from proteomics and transcriptomics studies as well as from automatic text mining of the scientific literature:

Whereas the resource integrates all of the above-mentioned types of evidence, the focus in this work was primarily on combining data from systematic tissue expression atlases, produced using a variety of different high-throughput assays. This required extensive work on mapping, scoring, and benchmarking the different datasets to put them on a common confidence scale. The scientific results and details of all those analyses can be found in the article “Comprehensive comparison of large-scale tissue expression datasets”.

Resource: The DISEASES database on disease–gene associations

2015 has been an exceptionally busy year in my group in terms of publishing databases and other web resources; so busy that I have failed to write blog posts describing several of them.

One of them is the DISEASES database, which is described in detail in an article with the informative, if not very inventive title “DISEASES: Text mining and data integration of disease–gene associations”.

The DISEASES database can be viewed as a sister resource to the subcellular localization database COMPARTMENTS, which you can read more about in this blog post. Indeed, the two resources share much of their infrastructure, including the web framework, the backend database, and the text-mining pipeline.

The big difference between the two resources is the scope: whereas COMPARTMENTS links proteins to their subcellular localizations, DISEASES links them to the diseases in which they are implicated. To this end we make use of the Disease Ontology, which turned out to be very well suited for text-mining purposes due to its many synonyms for terms. Text mining is the most important source of associations but is complemented by manually curated associations from Genetics Home Reference and UniProtKB as well as GWAS results imported from DistiLD.

To facilitate usage in large-scale analysis and integration into other databases, all data in DISEASES are available for download. Indeed, the text-mined associations from DISEASES are already included in both GeneCards and Pharos.

Resource: The COMPARTMENTS database on protein subcellular localization

Together with collaborators in the groups of Seán O’Donoghue and Reinhard Schneider, my group has recently launched a new web-accessible database named COMPARTMENTS.

COMPARTMENTS unifies subcellular localization evidence from many sources by mapping all proteins and compartments to their STRING identifiers and Gene Ontology terms, respectively. We import curated annotations from UniProtKB and model organism databases and assign confidence scores to them based on their evidence codes. For human proteins, we similarly import and score evidence from The Human Protein Atlas. COMPARTMENTS also uses text mining to derive subcellular localization evidence from co-occurrence of proteins and compartments in Medline abstracts. Finally, we precompute subcellular localization predictions with the sequence-based methods WoLF PSORT and YLoc. For further details, please refer to our recently published paper entitled “COMPARTMENTS: unification and visualization of protein subcellular localization evidence”.

To provide a simple overview of all this information, we visualize the combined localization evidence for each protein onto a schematic of an animal, fungal, or plant cell:

You can click any of the three images above to go to the COMPARTMENTS web resource. To facilitate use in large-scale analyses, the complete datasets for major eukaryotic model organisms are available for download.