Computational Molecular Biology 2024, Vol.14, No.3, 97-105 http://bioscipublisher.com/index.php/cmb 102 Another significant open-source platform is Sherlock, which addresses the challenges of data collection, storage, and analysis in computational biology. Sherlock leverages modern big data technologies like Docker and PrestoDB to enable users to manage, query, and share large and complex datasets efficiently. It supports various structured data types and converts them into optimized storage formats, facilitating quick and efficient distributed analytical queries (Bohár et al., 2022). OpenBIS is another flexible open-source framework designed for managing and analyzing complex biological data. It allows users to collect, integrate, share, and publish data while connecting to data processing pipelines. openBIS is highly scalable and customizable, making it suitable for a wide range of biological data types and research domains (Bauch et al., 2011). PipeCraft is a flexible toolkit specifically designed for the bioinformatics analysis of high-throughput amplicon sequencing data. It provides a user-friendly graphical interface that links several public tools, allowing users to customize their analysis pipelines according to their specific needs. PipeCraft supports various sequencing platforms and ensures easy customization and traceability of analytical steps (Anslan et al., 2017). 6.2 Commercial software solutions Commercial software solutions for biological big data often provide robust, enterprise-level support and advanced features that may not be available in open-source tools. These solutions are designed to handle the vast amounts of data generated by modern biological research and offer comprehensive support for data analysis, storage, and management. While the provided data does not include specific examples of commercial software solutions, it is important to note that these solutions typically offer enhanced performance, scalability, and integration capabilities. They often come with dedicated customer support, regular updates, and compliance with industry standards, making them suitable for large-scale and mission-critical applications in biological research. 6.3 Customized pipelines Customized pipelines are essential for addressing the unique requirements of specific biological research projects. These pipelines often integrate multiple software tools and platforms to create tailored workflows that can handle the complexity and scale of big biological data. The use of application containers and workflows, such as those enabled by Docker, has revolutionized the deployment and reproducibility of computational experiments in genomics. By isolating applications and creating secure, scalable platforms, researchers can significantly reduce the time needed for data analysis and improve the reproducibility of their experiments (Schulz et al., 2016). High-performance computing (HPC) platforms also play a crucial role in customized pipelines for big biological data analysis. These platforms provide the computational power needed to handle the complexity and volume of biological data, enabling researchers to gain deeper insights into biological functions. HPC platforms are particularly useful for tasks such as genomic sequencing data analysis and protein structure analysis, where traditional computing platforms may fall short (Yin et al., 2017; Yeh et al., 2023). 7 Challenges and Future Directions 7.1 Scalability and performance issues The rapid growth of biological data, driven by advancements in high-throughput sequencing technologies, has outpaced the capabilities of traditional data analysis platforms. This has necessitated the development of high-performance computing (HPC) platforms and scalable algorithms to handle the massive computational demands of big biological data analytics (Yin et al., 2017). The scalability of bioinformatics software is a critical concern, as it must efficiently manage increasing workloads. Modern cloud computing frameworks like MapReduce and Spark have been employed to implement divide-and-conquer strategies in distributed computing environments, addressing these scalability challenges (Yang et al., 2017). However, ensuring the validity of computational outputs remains a significant issue, requiring robust software testing techniques such as metamorphic testing to ensure the accuracy and reliability of bioinformatics tools (Yang et al., 2017). 7.2 Integration of multimodal data The integration of multimodal data, particularly in single-cell biology, presents a considerable challenge due to the
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