Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes.
Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes.
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DOI：
10.1515/jib-2016-0002
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发表时间：
2017-06-13
影响因子：
1.9
通讯作者：
Rawlings C
中科院分区：
文献类型：
--
作者：
Hassani-Pak K;Rawlings C
旨在揭示基因与表型关系的遗传学和“组学”研究往往会发现大量潜在的候选基因，其中原因基因是隐藏的。科学家普遍缺乏时间和技术专长来审查文献、关键模式物种和可能范围广泛的相关生物学数据库中的所有相关信息，这些数据库采用各种质量和覆盖面各不相同的数据格式。需要计算工具来整合和评估不同的信息，以便优先考虑候选基因和相互作用网络的组件，如果这些基因和组件受到潜在干预的干扰，将对整个生物体的生物结果产生积极影响，而不会产生负面影响。在这里，我们回顾了几个生物信息学工具和数据库，它们在生物学知识发现和候选基因优先排序方面发挥着重要作用。最后，我们总结了几个需要解决的关键挑战，以促进未来的生物学知识发现。
Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.