GeneWeaver:发现异质跨物种功能基因组数据的一致性。

Jason A Bubier, Charles A Phillips, Michael A Langston, Erich J Baker, Elissa J Chesler
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引用次数: 9

摘要

一个持久的挑战在于解释功能基因组学实验的共识和分歧。协调和分析这些数据将使研究人员能够通过基因组底物发现许多基因与许多疾病的关系,以及从许多表型和实验范式到许多疾病的关系。GeneWeaver.org系统提供了一个跨物种整合和查询异质策划和实验衍生功能基因组数据的平台。GeneWeaver使研究人员能够在一个环境中存储、共享、分析和比较他们自己的全基因组功能基因组学实验的结果,该环境包含从主要管理存储库获得的丰富伴侣数据,包括小鼠基因组数据库和其他模型生物数据库,以及来自高度专业化的资源、出版物和用户提交的派生数据。这些数据主要由基因集和假定的生物网络组成,通过基因标识符和物种间的同源性相互映射。一套多功能的交互式工具使调查人员能够执行各种集分析操作,以找到这些经常嘈杂的实验结果之间的一致性。快速算法支持对大型查询进行实时分析。具体应用包括确定数量性状位点的候选基因的优先级,确定生物有效的小鼠模型和人类疾病的表型分析,发现相关疾病的共同生物底物,从经验数据中对实验及其所代表的生物学概念进行分类,以及应用基因组证据模式来暗示疾病中的新基因。这些结果说明了严格强调可重复性的另一种选择,即研究人员对实验结果进行分类,以确定导致其相似性的条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data.

GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data.

GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data.

GeneWeaver: finding consilience in heterogeneous cross-species functional genomics data.

A persistent challenge lies in the interpretation of consensus and discord from functional genomics experimentation. Harmonizing and analyzing this data will enable investigators to discover relations of many genes to many diseases, and from many phenotypes and experimental paradigms to many diseases through their genomic substrates. The GeneWeaver.org system provides a platform for cross-species integration and interrogation of heterogeneous curated and experimentally derived functional genomics data. GeneWeaver enables researchers to store, share, analyze, and compare results of their own genome-wide functional genomics experiments in an environment containing rich companion data obtained from major curated repositories, including the Mouse Genome Database and other model organism databases, along with derived data from highly specialized resources, publications, and user submissions. The data, largely consisting of gene sets and putative biological networks, are mapped onto one another through gene identifiers and homology across species. A versatile suite of interactive tools enables investigators to perform a variety of set analysis operations to find consilience among these often noisy experimental results. Fast algorithms enable real-time analysis of large queries. Specific applications include prioritizing candidate genes for quantitative trait loci, identifying biologically valid mouse models and phenotypic assays for human disease, finding the common biological substrates of related diseases, classifying experiments and the biological concepts they represent from empirical data, and applying patterns of genomic evidence to implicate novel genes in disease. These results illustrate an alternative to strict emphasis on replicability, whereby researchers classify experimental results to identify the conditions that lead to their similarity.

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