Computational Molecular Biology 2024, Vol.14, No.3, 97-105 http://bioscipublisher.com/index.php/cmb 101 and increasing the throughput of multiomic experiments (Gutierrez et al., 2018). Additionally, bioinformatics tools like Metabox facilitate the deep phenotyping analytics of metabolomic data, supporting its integration with proteomic and transcriptomic contexts (Wanichthanarak et al., 2017). The use of software containers and workflow environments, such as Galaxy and Nextflow, has further improved the scalability and reproducibility of proteomic and metabolomic data analysis. These tools allow for the distribution of analytics tasks across multiple computational resources, addressing the challenges of handling large and complex datasets (Perez-Riverol and Moreno, 2019). The integration of these high-throughput and scalable approaches is essential for addressing complex clinical and biological questions, ultimately leading to a better understanding of disease mechanisms and the identification of potential therapeutic targets (Gutierrez et al., 2018; Perez-Riverol and Moreno, 2019). 6 Tools and Platforms for Biological Big Data 6.1 Open-source tools Open-source tools have become indispensable in the realm of biological big data due to their flexibility, cost-effectiveness, and community-driven development. One notable example is TBtools, a comprehensive toolkit designed for interactive analyses of big biological data. TBtools offers over 100 functions for tasks ranging from bulk sequence processing to interactive data visualization, all within a user-friendly interface. This platform-independent software is freely available and supports various operating systems with Java Runtime Environment 1.6 or newer (Figure 2) (Chen et al., 2020). Figure 2 The Powerful Plotting Engine “JIGplot” in TBtools Displays Great Interactability (Adopted from Chen et al., 2020)
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