Inferring gene-to-phenotype and gene-to-disease relationships at Mouse Genome Informatics: Challenges and solutions
Journal of Biomedical Semantics, ISSN: 2041-1480, Vol: 7, Issue: 1, Page: 14
2016
- 7Citations
- 15Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Background: Inferring gene-to-phenotype and gene-to-human disease model relationships from annotated mouse phenotypes and disease associations is critical when researching gene function and identifying candidate disease genes. Filtering the various kinds of genotypes to determine which phenotypes are caused by a mutation in a particular gene can be a laborious and time-consuming process. Methods: At Mouse Genome Informatics (MGI, www.informatics.jax.org ), we have developed a gene annotation derivation algorithm that computes gene-to-phenotype and gene-to-disease annotations from our existing corpus of annotations to genotypes. This algorithm differentiates between simple genotypes with causative mutations in a single gene and more complex genotypes where mutations in multiple genes may contribute to the phenotype. As part of the process, alleles functioning as tools (e.g., reporters, recombinases) are filtered out. Results: Using this algorithm derived gene-to-phenotype and gene-to-disease annotations were created for 16,000 and 2100 mouse markers, respectively, starting from over 57,900 and 4800 genotypes with at least one phenotype and disease annotation, respectively. Conclusions: Implementation of this algorithm provides consistent and accurate gene annotations across MGI and provides a vital time-savings relative to manual annotation by curators.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84974530808&origin=inward; http://dx.doi.org/10.1186/s13326-016-0054-4; https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-016-0054-4; http://link.springer.com/content/pdf/10.1186/s13326-016-0054-4.pdf; http://link.springer.com/article/10.1186/s13326-016-0054-4/fulltext.html; http://link.springer.com/content/pdf/10.1186/s13326-016-0054-4; https://dx.doi.org/10.1186/s13326-016-0054-4
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know