Suppr超能文献

利用模式生物和疾病数据库支持人类疾病基因发现的匹配工作。

Use of model organism and disease databases to support matchmaking for human disease gene discovery.

作者信息

Mungall Christopher J, Washington Nicole L, Nguyen-Xuan Jeremy, Condit Christopher, Smedley Damian, Köhler Sebastian, Groza Tudor, Shefchek Kent, Hochheiser Harry, Robinson Peter N, Lewis Suzanna E, Haendel Melissa A

机构信息

Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California.

San Diego Supercomputing Center, UC San Diego, La Jolla, California.

出版信息

Hum Mutat. 2015 Oct;36(10):979-84. doi: 10.1002/humu.22857. Epub 2015 Sep 8.

Abstract

The Matchmaker Exchange application programming interface (API) allows searching a patient's genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enable matching known diseases and variant-phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases.

摘要

“媒人交换”应用程序编程接口(API)允许跨临床站点搜索患者的基因型或表型概况,以进行队列发现和变异疾病因果验证。该API不仅可用于搜索匹配的患者,还可与公共疾病和模式生物数据进行匹配。这些公共疾病数据能够利用君主计划开发的表型语义相似性算法,匹配已知疾病和变异-表型关联。模型数据可以提供额外的证据来辅助诊断,为疾病机制和治疗探索建议相关模型,并识别跨转化鸿沟的合作者。君主计划提供了此API的一个实现,用于搜索多个集成数据源,这些数据源将关于任何给定患者或患者家族的知识置于更广阔的生物医学知识背景中。虽然这个数据集有助于诊断,但它也是增进对罕见人类疾病理解的研究起点。

相似文献

1
Use of model organism and disease databases to support matchmaking for human disease gene discovery.
Hum Mutat. 2015 Oct;36(10):979-84. doi: 10.1002/humu.22857. Epub 2015 Sep 8.
3
Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking.
Hum Mutat. 2022 Jun;43(6):659-667. doi: 10.1002/humu.24373. Epub 2022 May 10.
4
PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases.
Hum Mutat. 2015 Oct;36(10):931-40. doi: 10.1002/humu.22851. Epub 2015 Aug 31.
5
The Matchmaker Exchange: a platform for rare disease gene discovery.
Hum Mutat. 2015 Oct;36(10):915-21. doi: 10.1002/humu.22858.
7
Facilitating collaboration in rare genetic disorders through effective matchmaking in DECIPHER.
Hum Mutat. 2015 Oct;36(10):941-9. doi: 10.1002/humu.22842. Epub 2015 Aug 20.
8
Matchmaker Exchange.
Curr Protoc Hum Genet. 2017 Oct 18;95:9.31.1-9.31.15. doi: 10.1002/cphg.50.
10
Genomic Data Sharing for Novel Mendelian Disease Gene Discovery: The Matchmaker Exchange.
Annu Rev Genomics Hum Genet. 2020 Aug 31;21:305-326. doi: 10.1146/annurev-genom-083118-014915. Epub 2020 Apr 27.

引用本文的文献

1
Prenatal phenotyping: A community effort to enhance the Human Phenotype Ontology.
Am J Med Genet C Semin Med Genet. 2022 Jun;190(2):231-242. doi: 10.1002/ajmg.c.31989. Epub 2022 Jul 24.
2
Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking.
Hum Mutat. 2022 Jun;43(6):659-667. doi: 10.1002/humu.24373. Epub 2022 May 10.
3
The case for open science: rare diseases.
JAMIA Open. 2020 Sep 11;3(3):472-486. doi: 10.1093/jamiaopen/ooaa030. eCollection 2020 Oct.
4
Soft windowing application to improve analysis of high-throughput phenotyping data.
Bioinformatics. 2020 Mar 1;36(5):1492-1500. doi: 10.1093/bioinformatics/btz744.
5
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.
Nucleic Acids Res. 2019 Jan 8;47(D1):D1018-D1027. doi: 10.1093/nar/gky1105.
6
Knowledge-based biomedical Data Science.
EPJ Data Sci. 2017;1(1-2):19-25. doi: 10.3233/DS-170001. Epub 2017 Dec 8.
7
Zebrafish Models of Human Disease: Gaining Insight into Human Disease at ZFIN.
ILAR J. 2017 Jul 1;58(1):4-16. doi: 10.1093/ilar/ilw040.
8
Disease model discovery from 3,328 gene knockouts by The International Mouse Phenotyping Consortium.
Nat Genet. 2017 Aug;49(8):1231-1238. doi: 10.1038/ng.3901. Epub 2017 Jun 26.
9
International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases.
Am J Hum Genet. 2017 May 4;100(5):695-705. doi: 10.1016/j.ajhg.2017.04.003.
10
Use of Biomedical Ontologies for Integration of Biological Knowledge for Learning and Prediction of Adverse Drug Reactions.
Gene Regul Syst Bio. 2017 Mar 15;11:1177625017696075. doi: 10.1177/1177625017696075. eCollection 2017.

本文引用的文献

1
The Matchmaker Exchange: a platform for rare disease gene discovery.
Hum Mutat. 2015 Oct;36(10):915-21. doi: 10.1002/humu.22858.
3
GeneMatcher: a matching tool for connecting investigators with an interest in the same gene.
Hum Mutat. 2015 Oct;36(10):928-30. doi: 10.1002/humu.22844. Epub 2015 Aug 13.
5
York platelet syndrome is a CRAC channelopathy due to gain-of-function mutations in STIM1.
Mol Genet Metab. 2015 Mar;114(3):474-82. doi: 10.1016/j.ymgme.2014.12.307. Epub 2014 Dec 24.
6
OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders.
Nucleic Acids Res. 2015 Jan;43(Database issue):D789-98. doi: 10.1093/nar/gku1205. Epub 2014 Nov 26.
7
The National Institutes of Health undiagnosed diseases program.
Curr Opin Pediatr. 2014 Dec;26(6):626-33. doi: 10.1097/MOP.0000000000000155.
8
Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome.
Sci Transl Med. 2014 Sep 3;6(252):252ra123. doi: 10.1126/scitranslmed.3009262.
9
Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon.
J Biomed Semantics. 2014 May 19;5:21. doi: 10.1186/2041-1480-5-21. eCollection 2014.
10
Phenotype ontologies and cross-species analysis for translational research.
PLoS Genet. 2014 Apr 3;10(4):e1004268. doi: 10.1371/journal.pgen.1004268. eCollection 2014 Apr.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验