Harmonizing model organism data in the Alliance of Genome Resources

The Alliance of Genome Resources (the Alliance) is a combined effort of 7 knowledgebase projects: Saccharomyces Genome Database, WormBase, FlyBase, Mouse Genome Database, the Zebrafish Information Network, Rat Genome Database, and the Gene Ontology Resource. The Alliance seeks to provide several benefits: better service to the various communities served by these projects; a harmonized view of data for all biomedical researchers, bioinformaticians, clinicians, and students; and a more sustainable infrastructure. The Alliance has harmonized cross-organism data to provide useful comparative views of gene function, gene expression, and human disease relevance. The basis of the comparative views is shared calls of orthology relationships and the use of common ontologies. The key types of data are alleles and variants, gene function based on gene ontology annotations, phenotypes, association to human disease, gene expression, protein-protein and genetic interactions, and participation in pathways. The information is presented on uniform gene pages that allow facile summarization of information about each gene in each of the 7 organisms covered (budding yeast, roundworm Caenorhabditis elegans, fruit fly, house mouse, zebrafish, brown rat, and human). The harmonized knowledge is freely available on the alliancegenome.org portal, as downloadable files, and by APIs. We expect other existing and emerging knowledge bases to join in the effort to provide the union of useful data and features that each knowledge base currently provides.

Practical issues and limitations of brain attenuation correction on a simultaneous PET-MR scanner

BACKGROUND

Despite the advent of clinical PET-MR imaging for routine use in 2011 and the development of several methods to address the problem of attenuation correction, some challenges remain. We have identified and investigated several issues that might affect the reliability and accuracy of current attenuation correction methods when these are implemented for clinical and research studies of the brain. These are (1) the accuracy of converting CT Hounsfield units, obtained from an independently acquired CT scan, to 511 keV linear attenuation coefficients; (2) the effect of padding used in the MR head coil; (3) the presence of close-packed hair; (4) the effect of headphones. For each of these, we have examined the effect on reconstructed PET images and evaluated practical mitigating measures.

RESULTS

Our major findings were (1) for both Siemens and GE PET-MR systems, CT data from either a Siemens or a GE PET-CT scanner may be used, provided the conversion to 511 keV μ-map is performed by the PET-MR vendor's own method, as implemented on their PET-CT scanner; (2) the effect of the head coil pads is minimal; (3) the effect of dense hair in the field of view is marked (> 10% error in reconstructed PET images); and (4) using headphones and not including them in the attenuation map causes significant errors in reconstructed PET images, but the risk of scanning without them may be acceptable following sound level measurements.

CONCLUSIONS

It is important that the limitations of attenuation correction in PET-MR are considered when designing research and clinical PET-MR protocols in order to enable accurate quantification of brain PET scans. Whilst the effect of pads is not significant, dense hair, the use of headphones and the use of an independently acquired CT-scan can all lead to non-negligible effects on PET quantification. Although seemingly trivial, these effects add complications to setting up protocols for clinical and research PET-MR studies that do not occur with PET-CT. In the absence of more sophisticated PET-MR brain attenuation correction, the effect of all of the issues above can be minimised if the pragmatic approaches presented in this work are followed.

Automated generation of gene summaries at the Alliance of Genome Resources

Short paragraphs that describe gene function, referred to as gene summaries, are valued by users of biological knowledgebases for the ease with which they convey key aspects of gene function. Manual curation of gene summaries, while desirable, is difficult for knowledgebases to sustain. We developed an algorithm that uses curated, structured gene data at the Alliance of Genome Resources (Alliance; www.alliancegenome.org) to automatically generate gene summaries that simulate natural language. The gene data used for this purpose include curated associations (annotations) to ontology terms from the Gene Ontology, Disease Ontology, model organism knowledgebase (MOK)-specific anatomy ontologies and Alliance orthology data. The method uses sentence templates for each data category included in the gene summary in order to build a natural language sentence from the list of terms associated with each gene. To improve readability of the summaries when numerous gene annotations are present, we developed a new algorithm that traverses ontology graphs in order to group terms by their common ancestors. The algorithm optimizes the coverage of the initial set of terms and limits the length of the final summary, using measures of information content of each ontology term as a criterion for inclusion in the summary. The automated gene summaries are generated with each Alliance release, ensuring that they reflect current data at the Alliance. Our method effectively leverages category-specific curation efforts of the Alliance member databases to create modular, structured and standardized gene summaries for seven member species of the Alliance. These automatically generated gene summaries make cross-species gene function comparisons tenable and increase discoverability of potential models of human disease. In addition to being displayed on Alliance gene pages, these summaries are also included on several MOK gene pages.