Cotton Genomics and Genetics 2025, Vol.16, No.4, 202-209 http://cropscipublisher.com/index.php/cgg 203 2 Technological Foundations of Cloud Genome Databases 2.1 Cloud computing infrastructure Cloud computing can provide scalable, low-cost, and flexible resources, which is very important for storing and analyzing the large amount of genomic data generated in modern breeding. Because it can quickly increase or decrease resources as needed, researchers are more efficient when processing big data. Distributed systems like Hadoop can also allow multiple computing tasks to be performed simultaneously, which is suitable for analyzing ultra-large data sets (O'Driscoll et al., 2013). Now many countries and international organizations have built their own cloud platforms, some using hybrid clouds, and some using a combination of multiple cloud platforms, which makes it easier for different institutions and countries to do research and share data together (Ogasawara, 2022; Molnár-Gábor et al., 2017). 2.2 Data integration and standardization Many times, the data is not unusable, but "cannot be put together". Especially in genomic databases, data from different sources and in different formats are piled together. If there is no unified processing method, the analysis work cannot be carried out smoothly (Dahlquist et al., 2023). Of course, if you want to integrate these data well, it is not a matter of relying on only one standard. You must first have a unified extraction process, and then the format and description fields of the data (that is, metadata) must be consistent to avoid errors. But the situation is not always so ideal. Once the standards are well implemented, not only will the data fit, but the various platforms will also be more compatible. At this time, researchers can spend less time on data cleaning and focus on truly meaningful analysis. You will find that seemingly messy data can actually piece together a clear picture (Langmead and Nellore, 2018). 2.3 Security and data governance Because genomic data is very sensitive, data security and management are particularly important. Cloud platforms will set up a variety of ways to protect data, such as multi-layer access control, user authentication, data encryption, and operation records (Chen et al., 2018; Satish, 2024). At the same time, a complete set of management rules is also needed to regulate the process of data upload, access, and use to ensure compliance with local laws and ethical requirements (Dove et al., 2014). In addition, some new encryption technologies, such as homomorphic encryption and secure computing protocols, are also used to further protect privacy. These methods allow different teams to analyze data together without exposing the original data (Tang et al., 2016; Cheng et al., 2023; Blindenbach et al., 2024). 3 Architecture of Cotton Breeding Information Platforms 3.1 Core functional modules The cotton breeding information platform is composed of multiple functional modules that support the storage, analysis and display of data. The main modules include: Search and retrieval functions: users can quickly find data on genomes, traits and breeding. Analysis tools: the platform can perform single gene analysis, process a batch of genes, do association studies and draw graphs of different types of data (Yang et al., 2022b). Data management: helps ensure that data from different sources are of good quality, correctly annotated and smoothly integrated (Yu et al., 2013). Special tools: such as genome browsers, genetic map viewers, homology analysis tools, and breeding information management systems, can analyze data in more depth. Download and statistics functions: data can be downloaded in batches, and statistical information on data usage can be viewed, which facilitates others to reproduce and analyze, and ensures the transparency and credibility of the data. 3.2 Data acquisition and upload pipelines There are so many types of data in cotton research that sometimes you can’t even figure out where the data came from. Omics, traits, and even field data are all mixed together. Without a smooth data flow, the platform can’t handle it. The common practice now is to rely on the system to automatically collect and pull all kinds of data first (Figure 1). After that, it can’t be used directly. It needs to be cleaned up to see if there are any problems with the format and whether the data itself is reliable (Issac et al., 2023). However, cleaning alone is not enough, and processing must also be efficient. Tools like Hadoop or Azure are often used to process large-scale data in batches.
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