Inferring gene-to-phenotype and gene-to-disease relationships at Mouse Genome Informatics: challenges and solutions
Inferring gene-to-phenotype and gene-to-disease relationships at Mouse Genome Informatics: challenges and solutions
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DOI：
10.1186/s13326-016-0054-4
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发表时间：
2016-05-20
影响因子：
1.9
通讯作者：
the MGI Software Group
中科院分区：
文献类型：
--
作者：
Bello SM;Eppig JT;the MGI Software Group
在研究基因功能和识别候选疾病基因时，从注释的小鼠表型和疾病关联推断基因与表型以及基因与人类疾病模型的关系至关重要。过滤各种基因型以确定哪些表型是由特定基因突变引起的可能是一个费力且耗时的过程。在小鼠基因组信息学（MGI，www.informatics.jax.org），我们开发了一种基因注释推导算法，可以根据我们现有的基因型注释语料库计算基因到表型和基因到疾病的注释。该算法区分单个基因中具有致病性突变的简单基因型和多个基因中的突变可能导致表型的更复杂的基因型。作为该过程的一部分，作为工具（例如报告基因、重组酶）的等位基因被过滤掉。使用该算法，从分别具有至少一种表型和疾病注释的超过 57,900 和 4800 个基因型开始，分别为 16,000 和 2100 个小鼠标记创建衍生的基因到表型和基因到疾病注释。该算法的实施在整个 MGI 中提供了一致且准确的基因注释，并且相对于管理者的手动注释节省了重要的时间。
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. 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. 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. Implementation of this algorithm provides consistent and accurate gene annotations across MGI and provides a vital time-savings relative to manual annotation by curators.
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影响因子：
15.9
作者：
Pasula, Satish;Cai, Xiaofeng;Chen, Hong
通讯作者：
Chen, Hong
影响因子：
3.9
作者：
Mungall CJ;Washington NL;Nguyen-Xuan J;Condit C;Smedley D;Köhler S;Groza T;Shefchek K;Hochheiser H;Robinson PN;Lewis SE;Haendel MA
通讯作者：
Haendel MA
DOI：
10.1073/pnas.0907008106
发表时间：
2009-08-18
影响因子：
11.1
作者：
Chen, Hong;Ko, Genevieve;Cremona, Ottavio
通讯作者：
Cremona, Ottavio
影响因子：
3.7
作者：
Richez C;Richards RJ;Duffau P;Weitzner Z;Andry CD;Rifkin IR;Aprahamian T
通讯作者：
Aprahamian T
影响因子：
14.9
作者：
Howe DG;Bradford YM;Conlin T;Eagle AE;Fashena D;Frazer K;Knight J;Mani P;Martin R;Moxon SA;Paddock H;Pich C;Ramachandran S;Ruef BJ;Ruzicka L;Schaper K;Shao X;Singer A;Sprunger B;Van Slyke CE;Westerfield M
通讯作者：
Westerfield M