The Functional Gene Annotation team captures the following functional information for cardiovascular-relevant gene products.

Gene Ontology (GO)

We use the GOA curation tool to associate terms from the GO to gene products including proteins, RNAs and macromolecular complexes. The data we produce is incorporated into the GOA and GO Consortium databases.

Protein-protein interactions

We use the IntAct editing tool to capture interactions at a very detailed level as described in the IMEx guidelines. These annotations are directly incorporated into the IntAct database, from where they are exported to the IMEx Consortium database.

All of our annotations are propagated to popular freely available online knowledgebases, such as UniProt, Ensembl and NCBIGene as well as numerous other public and commercial analysis tools.

We are manually capturing protein-protein interaction (PPI) experimental data from the cardiovascular-related literature and submitting this to the IntAct public dataset at EBI. This data is then incorporated into the IMEx Consortium dataset. These annotations will contribute to the expansion and development of the existing PPI network and advance our knowledge about protein interactions within the cardiovascular system.

We are currently focusing on re-annotating papers which we had previously identified as including PPI experimental data, during the previous cardiovascular GO annotation Initiative. For example, the interaction between DVL1 and DVL3 described by Kishida et al, 1999, was captured as a GO annotation in 2008. This interaction is now in the IntAct and IMEx datasets and can therefore be included in software creating PPI networks.

Current PPI annotation projects:

  • Cardiac conduction (Ruth)
  • Lipid traits (Nancy, Mila)
  • Telomeres (Nancy)
  • Wnt signalling (Anna)
  • Heart development (Mila)

Annotation progress can be viewed either using this link or using the IntAct browser, with the advanced fields search option and selecting from 2 drop down menus field: dataset, cardiac. These approaches both retrieve a list of interactions, which can either be downloaded or viewed using the graph tab.


Our additional PPIs have improved the networks available for individual proteins, for example the number of PPIs associated with ABCA1 has more than doubled by increasing from 21 to 54 interactions (including duplicated and self-interactions).

Selection of TERT GO annotations

We are manually annotating proteins thought or known to play a role in cardiovascular systems through the application of Gene Ontology (GO) terms. Manual annotation is a time-consuming process but provides highly descriptive annotations. The UCL team in general takes a process-focused approach when prioritising which gene products to annotate. The advantage of a process-focused annotation approach is that it gives the curators time to fully understand the biological area they are annotating and consequently provide highly descriptive annotations. In addition, the curators request very descriptive GO terms, because they are keen to improve the annotation of this domain. Finally each paper is fully annotated, which may not happen when curators are taking a gene product focused approach.

Current GO annotation projects:

Previous GO annotation projects:

  • Proteins involved in miRNA processing (Rachael)
  • Folic acid metabolism (Hadil Alrohaif, MSc project student)
  • Hereditary hemochromatosis (Klaus Mitchell, MSc project student)
  • Apoptosis (Ruth)
  • Heart development (Varsha, Ruth)
  • Insulin signaling (Ruth)
  • Lipid metabolism (Ruth)
  • Immunity (Evelyn)
  • Notch Pathway in Cardiac Development (Greg Rowe, MSc project student)
  • Transcription factors associated with heart development (Varsha)
  • Heart jogging (Varsha)
  • BMP signalling pathway (Varsha)
  • Inhibitory SMAD signalling pathway (Varsha)
  • Growth factor regulated SMAD signalling pathway (Varsha)
  • TGF-beta regulated SMAD signalling pathway (Varsha)
  • Inositol 1,4,5-triphosphate-sensitive calcium-release channel pathway (Varsha)
  • Growth hormone release pathway (Varsha)
  • Reverse cholesterol transport (Ruth)
  • Ryanodine-sensitive calcium-release channel activity (Varsha)
  • Telomere maintenance (Ruth, Nancy)
  • Transcriptional regulation by TCF7L2 (Ruth)
  • Vitamin D metabolism (Varsha)
  • Lipid trait risk associated genes (Ruth)
  • CAFA2 project list (Anna, Ruth)

Since January 2015 we have been using the Gene Ontology (GO) to describe the biological roles of microRNAs (miRNAs). Our aim is to to curate miRNAs that are involved with cardiovascular development and related processes. In conjunction with this effort we have curated the roles of the key human proteins that are involved in miRNA processing, such as Drosha and Dicer.

The RNA annotations are available in the regular GOA release files and in AmiGO2.

Example: View the annotations associated with hsa-miR-133a

Any queries about the miRNA project should be directed to Rachael Huntley, the lead biocurator for this project.

Current annotation projects

  • Cardiac conduction (Rachael)
  • Cardiac regeneration (Rachael and Barbara)
  • Aortic aneurysm (Zara Umrao, MSc project student)

miRNA curation guidelines

As part of this project we have drawn up miRNA curation guidelines, in consultation with the Gene Ontology Consortium and experts in miRNA research, which cover how a curator should translate into GO annotations the common experimental assays used to investigate miRNA function, including how to annotate experimentally verified targets of miRNAs as well as the physiological effects that silencing has on the cell or organism. We also describe how to capture the context of miRNA function, e.g. in which cell or tissue types the miRNAs act - information that is essential for pathway analysis. Additionally, we provide guidance of which GO terms should be applied to the proteins of the miRNA biogenesis machinery, for example Dicer, in both animals and plants.

View the miRNA curation guidelines

miRNA Decision Tree

Figure showing the Decision Tree of GO terms and annotation extensions used for capturing targets of miRNAs.

The types of evidence are listed in the blue boxes. The green boxes summarise the  annotations that can be created based on the available evidence. The pink boxes indicate that the published descriptions of a miRNA target does not meet the GOC guideline criteria and will not be captured.

A reporter assay, or a Co-IP plus an assay demonstrating an effect of the miRNA on mRNA levels, is sufficient to classify a target as “validated binding”, additional evidence that the target is predicted for the miRNA does not affect the annotation given therefore this option is not shown. Author justification means the author indicates why this mRNA is an expected target or shows an effect on an expected downstream process.