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Article|21 Dec 2023|OPEN
DataColor: unveiling biological data relationships through distinctive color mapping
Shuang He1,2,9 , Wei Dong3,9 , Junhao Chen4,9 , Junyu Zhang1,2 , Weiwei Lin5 and Shuting Yang1,2 , Dong Xu6 , Yuhan Zhou7 , Benben Miao8 , Wenquan Wang1,2 , , Fei Chen,1,2 ,
1Sanya Institute of Breeding and Multiplication, National Key Laboratory for Tropical Crop Breeding, Hainan University, Sanya 572025, China
2School of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, China
3Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-Sen University, Guangzhou 510055, China
4Department of Biology, Saint Louis University, St Louis, MO 63103, USA
5Merkle Business Information Consultancy (Nanjing) Co., Ltd, Nanjing 210032, China
6Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
7State Key Laboratory of Rice Biology & Breeding, Zhejiang Provincial Key Laboratory of Crop Germplasm, The Advanced Seed Institute, Zhejiang University, Hangzhou 310058, China
8College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, Fujian, China
9Co-first author: Shuang He, Wei Dong, Junhao Chen
*Corresponding author. E-mail: wangwenquan@itbb.org.cn,feichen@hainanu.edu.cn

Horticulture Research 11,
Article number: uhad273 (2024)
doi: https://doi.org/10.1093/hr/uhad273
Views: 48

Received: 04 Sep 2023
Accepted: 06 Dec 2023
Published online: 21 Dec 2023

Abstract

In the era of rapid advancements in high-throughput omics technologies, the visualization of diverse data types with varying orders of magnitude presents a pressing challenge. To bridge this gap, we introduce DataColor, an all-encompassing software solution meticulously crafted to address this challenge. Our aim is to empower users with the ability to handle a wide array of data types through an assortment of tools, while simultaneously streamlining parameter selection for rapid insights and detailed enhancements. DataColor stands as a robust toolkit, encompassing 23 distinct tools coupled with over 600 parameters. The defining characteristic of this toolkit is its adept utilization of the color spectrum, allowing for the representation of data spanning diverse types and magnitudes. Through the integration of advanced algorithms encompassing data clustering, normalization, squarified layouts, and customizable parameters, DataColor unveils an abundance of insights that lay hidden within the intricate relationships embedded in the data. Whether you find yourself navigating the analysis of expansive datasets or embarking on the quest to visualize intricate patterns, DataColor stands as the comprehensive and potent solution. We extend the availability of DataColor to all users at no cost, accessible through the following link: https://github.com/frankgenome/DataColor.