QTLNetMiner: Mining Candidate Gene Networks From Genetic Studies of Crops and Animals

QTLNetMiner：从农作物和动物的遗传研究中挖掘候选基因网络

• 批准号: BB/I023860/1
• 负责人: Christopher Rawlings
• 金额: $ 12.78万
• 依托单位: Rothamsted Research
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI023860%2F1
• 关键词: QTLNetMiner; Mining; Candidate; Gene; Networks

Discovering which genes determine a particular biological trait in crops, animals or humans is a very important finding. There are many applications of such knowledge including: identifying new biomarkers for animal or human diseases which can lead to new diagnostics; designing screens for new drugs, and helping to select new varieties of crop or livestock animals with improved productivity or resistance to stresses such as disease. Searching for these candidate genes in a crop or animal genome is, however, like searching for a needle in a haystack and gathering the evidence that supports the choice of one gene over another is even more daunting. This is because the evidence is scatted among different internet databases and in incompatible forms that are not easily linked together or integrated. One very important class of information used by biologists to begin their search for candidate genes is genetics. Classical genetics methods use studies of populations and families and employ statistical methods to identify the most likely genome segments that are known as Quantitative Trait Loci (QTL). The nature of complex traits, however, means that many QTL may be identified for a particular trait. For example, a recent study in Brassica napus identified 47 QTLs which were relevant for seed yield and studies in pig have discovered in total more than 400 QTLs related to fatness. For many years, the study of complex traits in crops and livestock animals has been an important adjunct to their improvement through selective breeding. Until recently, the focus on mapping of QTLs has been based on genetic maps constructed using relatively small numbers (hundreds) of genetic markers separated by quite large genetic distances. By linking the genetic maps with newly obtained genome sequence information it is now possible to list the genes that underlie each QTL. These studies show that typical QTLs in both plants and animals generally encompass quite sizeable parts of the genome - typically several hundred genes. While genetics improves the chances of finding the right gene (or genes), reducing the options down from 22,000 or so found in a typical genome, to hundreds genes for a particular QTL, it is still a daunting and expensive task to evaluate candidate gene in the laboratory. Furthermore, as is becoming apparent in diseases such as cancer, a complex phenotype may be the consequence of groups of seemingly independent genes interacting through a network of different biological relationships. The software we plan to develop in this project builds on previously-funded BBSRC research in which we have developed general methods for integrating different sources of biological information and exploring the relationships among genes and proteins using network-based approaches. Our methods help biologists mine the networks of information and interactions among genes in order to make better-informed judgments about which gene or gene networks are involved in a particular trait. In this project we will further develop the software and adapt our methods to create prototypes of biologist-friendly web sites for four species representing important crop and farm animal species where genetics and QTL data can be combined with other data resources, including the scientific literature. These species have been chosen because of their importance to the BBSRC and national priorities around improving the security of our food and energy (bioenergy) supplies. In particular, we will develop integrated data and network biology resources as web sites for use by the farm animal research community; thus translating the applications of our data integration research into a new area of BBSRC-funded biology. In addition to developing several novel resources for biologists, we wish to demonstrate that the Ondex data integration platform can be adapted to new areas of biological research in a cost-effective manner.

发现哪些基因决定作物、动物或人类的特定生物性状是一项非常重要的发现。此类知识有许多应用，包括：识别动物或人类疾病的新生物标志物，这可以带来新的诊断方法;设计新药筛选，并帮助选择具有提高生产力或抵抗力的农作物或畜牧动物新品种。疾病等压力。然而，在作物或动物基因组中寻找这些候选基因就像大海捞针，收集支持选择一个基因而不是另一个基因的证据更是令人生畏。这是因为证据分散在不同的互联网数据库中，并且不兼容，不易链接或整合。遗传学是生物学家开始寻找候选基因时所使用的一类非常重要的信息。经典遗传学方法使用群体和家庭的研究，并采用统计方法来确定最可能的基因组片段，称为数量性状基因座（QTL）。然而，复杂性状的性质意味着可以为特定性状鉴定许多QTL。例如，最近在甘蓝型油菜（Brassica napus）中的研究鉴定了47个与种子产量相关的QTL，并且在猪中的研究已经总共发现了超过400个与肥满度相关的QTL。多年来，对作物和家畜复杂性状的研究一直是通过选择性育种进行改良的重要辅助手段。直到最近，对QTL作图的关注一直基于使用相对少量（数百个）由相当大的遗传距离分开的遗传标记构建的遗传图谱。通过将遗传图谱与新获得的基因组序列信息连接起来，现在可以列出每个QTL的基因。这些研究表明，植物和动物中典型的QTL通常包含相当大的基因组部分-通常是几百个基因。虽然遗传学提高了找到正确基因（或基因）的机会，将选择从典型基因组中发现的22，000个左右减少到特定QTL的数百个基因，但在实验室中评估候选基因仍然是一项艰巨而昂贵的任务。此外，正如在癌症等疾病中变得明显的那样，复杂的表型可能是看似独立的基因组通过不同生物学关系的网络相互作用的结果。我们计划在这个项目中开发的软件建立在以前资助的BBSRC研究的基础上，在该研究中，我们已经开发了整合不同来源的生物信息的通用方法，并使用基于网络的方法探索基因和蛋白质之间的关系。我们的方法帮助生物学家挖掘基因之间的信息和相互作用网络，以便更好地判断哪些基因或基因网络参与特定性状。在这个项目中，我们将进一步开发软件，并调整我们的方法，为代表重要作物和农场动物物种的四个物种创建生物学家友好的网站原型，其中遗传学和QTL数据可以与其他数据资源相结合，包括科学文献。之所以选择这些物种，是因为它们对BBSRC和提高我们的粮食和能源（生物能源）供应安全的国家优先事项具有重要意义。特别是，我们将开发集成的数据和网络生物学资源作为网站供农场动物研究界使用;从而将我们的数据集成研究的应用转化为BBSRC资助的生物学的一个新领域。除了为生物学家开发一些新的资源外，我们还希望证明Ondex数据集成平台可以以具有成本效益的方式适应生物研究的新领域。

项目成果

Ondex Web: web-based visualization and exploration of heterogeneous biological networks.

• DOI: 10.1093/bioinformatics/btt740
• 发表时间: 2014-04-01
• 期刊: Bioinformatics (Oxford, England)
• 作者: Taubert J;Hassani-Pak K;Castells-Brooke N;Rawlings CJ
• 通讯作者: Rawlings CJ

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes.

• DOI: 10.1515/jib-2016-0002
• 发表时间: 2017-06-13
• 期刊: Journal of integrative bioinformatics
• 影响因子: 1.9
• 作者: Hassani-Pak K;Rawlings C
• 通讯作者: Rawlings C

KnetMaps: a BioJS component to visualize biological knowledge networks.

KnetMaps：一个用于可视化生物知识网络的 BioJS 组件。

• DOI: 10.12688/f1000research.16605.1
• 发表时间: 2018
• 期刊: F1000Research
• 作者: Singh A

Developing integrated crop knowledge networks to advance candidate gene discovery.

• DOI: 10.1016/j.atg.2016.10.003
• 发表时间: 2016-12
• 期刊: Applied & translational genomics
• 作者: Hassani-Pak, Keywan;Castellote, Martin;Esch, Maria;Hindle, Matthew;Lysenko, Artem;Taubert, Jan;Rawlings, Christopher
• 通讯作者: Rawlings, Christopher

Getting the best of Linked Data and Property Graphs: rdf2neo and the KnetMiner Use Case

充分利用链接数据和属性图：rdf2neo 和 KnetMiner 用例

• DOI: 10.6084/m9.figshare.7314323.v1
• 发表时间: 2018
• 作者: Brandizi M