G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers.
G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers.
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DOI：
10.1093/nar/gkab407
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发表时间：
2021-07-02
影响因子：
14.9
通讯作者：
Joshi T
中科院分区：
文献类型：
--
作者：
Zeng S;Mao Z;Ren Y;Wang D;Xu D;Joshi T
G2PDeep 是一个开放访问的网络服务器，它为定量表型预测和基因组标记的发现提供了深度学习框架。它使用来自植物和动物的接合性或单核苷酸多态性 (SNP) 信息作为输入来预测感兴趣的定量表型以及与表型相关的基因组标记。它为研究人员提供了一个一站式平台，让他们可以通过交互式网络界面创建深度学习模型，并使用后端插入的高性能计算资源使用上传的数据训练这些模型。 G2PDeep 还提供了一系列信息丰富的界面来监控训练过程并比较训练模型之间的性能。然后可以自动部署经过训练的模型。使用用户选择的训练模型预测定量表型和基因组标记，并将结果可视化。我们最先进的模型已被其他研究人员进行了基准测试并证明了其在定量表型预测方面的竞争性能。此外，该服务器还集成了大豆嵌套关联映射（SoyNAM）数据集，包括产量、高度、水分、油分和蛋白质等五种表型。种子蛋白质和油含量的公开数据集也已集成到服务器中。 G2PDeep 服务器可在 http://g2pdeep.org 上公开获取。基于Python的深度学习模型可在https://github.com/shuaizengMU/G2PDeep_model获得。 G2PDeep：一个基于网络的深度学习框架，用于定量表型预测和基因组标记的发现。
G2PDeep is an open-access web server, which provides a deep-learning framework for quantitative phenotype prediction and discovery of genomics markers. It uses zygosity or single nucleotide polymorphism (SNP) information from plants and animals as the input to predict quantitative phenotype of interest and genomic markers associated with phenotype. It provides a one-stop-shop platform for researchers to create deep-learning models through an interactive web interface and train these models with uploaded data, using high-performance computing resources plugged at the backend. G2PDeep also provides a series of informative interfaces to monitor the training process and compare the performance among the trained models. The trained models can then be deployed automatically. The quantitative phenotype and genomic markers are predicted using a user-selected trained model and the results are visualized. Our state-of-the-art model has been benchmarked and demonstrated competitive performance in quantitative phenotype predictions by other researchers. In addition, the server integrates the soybean nested association mapping (SoyNAM) dataset with five phenotypes, including grain yield, height, moisture, oil, and protein. A publicly available dataset for seed protein and oil content has also been integrated into the server. The G2PDeep server is publicly available at http://g2pdeep.org. The Python-based deep-learning model is available at https://github.com/shuaizengMU/G2PDeep_model. G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers.
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影响因子：
14.9
作者：
Mistry J;Chuguransky S;Williams L;Qureshi M;Salazar GA;Sonnhammer ELL;Tosatto SCE;Paladin L;Raj S;Richardson LJ;Finn RD;Bateman A
通讯作者：
Bateman A
影响因子：
14.9
作者：
Kanehisa M;Furumichi M;Sato Y;Ishiguro-Watanabe M;Tanabe M
通讯作者：
Tanabe M
影响因子：
4.5
作者：
Spindel J;Begum H;Akdemir D;Virk P;Collard B;Redoña E;Atlin G;Jannink JL;McCouch SR
通讯作者：
McCouch SR
影响因子：
5.6
作者：
Goff SA;Vaughn M;McKay S;Lyons E;Stapleton AE;Gessler D;Matasci N;Wang L;Hanlon M;Lenards A;Muir A;Merchant N;Lowry S;Mock S;Helmke M;Kubach A;Narro M;Hopkins N;Micklos D;Hilgert U;Gonzales M;Jordan C;Skidmore E;Dooley R;Cazes J;McLay R;Lu Z;Pasternak S;Koesterke L;Piel WH;Grene R;Noutsos C;Gendler K;Feng X;Tang C;Lent M;Kim SJ;Kvilekval K;Manjunath BS;Tannen V;Stamatakis A;Sanderson M;Welch SM;Cranston KA;Soltis P;Soltis D;O'Meara B;Ane C;Brutnell T;Kleibenstein DJ;White JW;Leebens-Mack J;Donoghue MJ;Spalding EP;Vision TJ;Myers CR;Lowenthal D;Enquist BJ;Boyle B;Akoglu A;Andrews G;Ram S;Ware D;Stein L;Stanzione D
通讯作者：
Stanzione D
DOI：
10.1534/g3.117.300300
发表时间：
2018-02-02
期刊：
G3 (Bethesda, Md.)
影响因子：
--
作者：
Xavier A;Jarquin D;Howard R;Ramasubramanian V;Specht JE;Graef GL;Beavis WD;Diers BW;Song Q;Cregan PB;Nelson R;Mian R;Shannon JG;McHale L;Wang D;Schapaugh W;Lorenz AJ;Xu S;Muir WM;Rainey KM
通讯作者：
Rainey KM