KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species.
KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species.
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DOI：
10.1111/pbi.13583
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发表时间：
2021-08
影响因子：
13.8
通讯作者：
Rawlings C
中科院分区：
文献类型：
--
作者：
Hassani-Pak K;Singh A;Brandizi M;Hearnshaw J;Parsons JD;Amberkar S;Phillips AL;Doonan JH;Rawlings C
新思想和科学假设的产生通常是广泛的文献和数据库搜索的结果，但是，随着公共和私人知识的不断丰富，搜索多样化和相互关联的数据以产生对基因，基因网络，性状和疾病的新见解的过程变得更加复杂和耗时。为了指导这项具有技术挑战性的数据集成任务，并使研究人员更容易发现基因和生成假设，我们开发了一个名为KnetMiner的综合软件包，该软件包是开源的，并且易于使用。KnetMiner是一个集成的，智能的，交互式的基因和基因网络发现平台，支持科学家探索和理解跨物种复杂性状和疾病的生物学故事。它具有快速算法，用于生成丰富的交互式基因网络，并根据知识挖掘方法对候选基因进行优先级排序。KnetMiner被许多植物科学机构使用，并已被几个植物育种组织采用，以加速基因发现。该软件是通用的，可定制的，因此可以很容易地应用于新物种和数据类型;例如，它已被应用于害虫和真菌病原体;最近被重新用于支持COVID-19研究。在这里，我们概述了KnetMiner背后的主要方法，并报告了以植物为中心的案例研究，用于识别小麦（面包小麦）中的基因，基因网络和性状关系，以及一种基于证据的方法来对大型拟南芥QTL下的候选基因进行排名。KnetMiner可在https://knetminer.org上获得。
The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time‐consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open‐source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID‐19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant‐centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence‐based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.
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影响因子：
64.5
作者：
Boyle EA;Li YI;Pritchard JK
通讯作者：
Pritchard JK
DOI：
10.1016/b978-0-12-401678-1.00007-5
发表时间：
2014-01-01
期刊：
METHODS IN BIOMEDICAL INFORMATICS: A PRAGMATIC APPROACH
影响因子：
--
作者：
Holmes, John H.
通讯作者：
Holmes, John H.
DOI：
10.1109/tvcg.2013.126
发表时间：
2013-12-01
影响因子：
5.2
作者：
Isenberg, Tobias;Isenberg, Petra;Moeller, Torsten
通讯作者：
Moeller, Torsten
DOI：
10.1093/bioinformatics/bty559
发表时间：
2018-09-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Alshahrani M;Hoehndorf R
通讯作者：
Hoehndorf R
影响因子：
4.2
作者：
Blake, Victoria C.;Birkett, Clay;Jannink, Jean-Luc
通讯作者：
Jannink, Jean-Luc