Results GREEKC
SUMMARY
The Knowledge Commons
The analysis of gene regulation by transcription factor-mediated mechanisms is key to understanding how genomic information determines cellular differentiation and function. This regulation involves a coordinated interaction between various types of transcription factors, the DNA regions where they engage in gene-specific transcription regulation and the specific epigenetic context that defines the accessibility and proximity to their target genes. Systems approaches to study this require ample access to all knowledge that has been obtained and such knowledge should be available from public databases: The Knowledge Commons.
GREEKC COST Action
The COST Action Gene Regulation Ensemble Effort for the Knowledge Commons was proposed by the Gene Regulation Consortium (GRECO) and designed to improve the development of the Gene Regulation Knowledge Commons (GRKC): ‘The collection of freely accessible information resources, with data well annotated with unambiguous descriptors according to quality criteria and standards that allow seamless integration and interoperability as well as automated computational access with third-party software. GREEKC has worked toward improving the resources contributing to the GRKC by coordinating efforts in building and making available high-quality, curated databases, following a responsible research and innovation (RRI) approach. This RRI approach proved to be an extremely good fit with the main use of COST Actions: bringing together people in one room who would not normally discuss or consult each other.
Improvements
Through the organization of nine Workshops, the GREEKC COST Action discussed improvements of the Sequence Ontology and the Gene Ontology, and the testing and use of curation tools including Noctua (which allows Gene Ontology-Causal Activity Modeling GO-CAM)) and Visual Syntax Markup (which aids a curator in the construction of annotations in a readable sentence format). Discussions stimulated the development of new curation guidelines, workflows, and data standards for the annotation of gene regulators at levels of proteins, ncRNA, gene, nucleotide sequence and interactions, regulatory complexes, and network information flow. A critical assessment was done to define what constitutes a DNA binding Transcription Factor, resulting in the GREEKC dbTF list. Given the importance of computationally generated knowledge, the crucial role of the Positional Weight Matrix (PWM) for predicting TF-TG interactions was the inspiration for a comprehensive benchmarking of public PWM models against large experimental reference sets, resulting in benchmarking protocols for future use.
GREEKC also coordinated the new annotation standard ‘Minimum Information about a Molecular Interaction Causal Statement’ (MI2CAST) and the data exchange language CausalTAB (the MITAB 2.8 format), for producing and sharing causal (regulatory) molecular interaction data. The use of text mining was discussed as a way to extract knowledge about regulatory interactions of transcription factors with target genes automatically from MedLine abstracts (www.ExTRI.org), and the results (a large corpus of TF-TG sentences) was made available through the PSICQUIC web service and through the Cytoscape App BioGateway, allowing a network builder to validate the quality of the text mining result by linking out to the ExTRI sentence in the abstract.
RELEVANT LINKS
GREEKC Training Schools results
LISBON
MARSEILLE
To get access to the Results Document Folder for this event, you can send an email to martin.kuiper@ntnu.no
HINXTON
To appear in Biochimica et Biophysica Acta as Gene Ontology representation for transcription factor functions.
Publications
DISSEMINATION VIDEOS
UCL Institute of Cardiovascular Science Newsletter
March 2020
Editor – Barbara Kramarz
“Shirin was awarded a Short Term Scientific Mission (STSM) grant from the European Cooperation In Science and Technology (COST) organisation, which allowed her to travel to Università della Calabria in southern Italy for a week-long knowledge exchange meeting with Dr Simona Panni. During this time Simona trained Shirin in capturing molecular interactions between microRNAs and their mRNA targets using the IntAct curation tool, whereas Shirin shared with Simona her knowledge about GO annotation of microRNAs, and trained Simona in using the Gene Ontology Annotation (GOA) curation tool.”