This research reviews recent advances in deep learning approaches tailored for regulatory genomics. It highlights how computational methods help decipher complex regulatory mechanisms within non-coding genomic regions across various tissues, emphasizing predictive applications such as transcription factor binding, chromatin accessibility, RNA processes, and RNA-protein interactions. The paper also discusses the evolution from traditional neural networks to advanced models like transformers and graph neural networks, considering three-dimensional genomic structures. Despite the promising performance, it acknowledges ongoing challenges like overfitting, biological variability, and limited dataset diversity. It emphasizes the urgent need for continued development of interpretable deep learning models to improve functional genomic annotation, underlining this task’s significance in the genomics field.

Summary: Bioinformatics is a rapidly evolving field with numerous specialized tools developed for essential genomic analysis tasks, such as read simulation, mapping, and variant calling. However, managing these tools presents significant challenges due to varied dependencies, execution steps, and output formats, complicating the installation and configuration processes. To address these issues, we introduce “Mars” a bioinformatics solution encapsulated within a singularity container that preloads a comprehensive suite of widely used genomic tools. Mars not only simplifies the installation of these tools but also automates critical workflow functions, including sequence sample preparation, read simulation, read mapping, variant calling, and result comparison. By streamlining the execution of these workflows, Mars enables users to easily manage input-output formats and compare results across different tools, thereby enhancing reproducibility and efficiency. Furthermore, by providing a cohesive environment that integrates tool management with a flexible workflow interface, Mars empowers researchers to focus on their analyses rather than the complexities of tool configuration. This integrated solution facilitates the testing of various combinations of tools and algorithms, enabling users to evaluate performance based on different metrics and identify the optimal tools for their specific genomic analysis needs. Through Mars, we aim to enhance the accessibility and usability of bioinformatics tools, ultimately advancing research in genomic analysis. Availability and implementation: Mars is freely available at https://github.com/GenomicAI/mars. It is implemented within a Singularity container environment and supports modular extension for additional genomic tools and custom workflows.

Next-generation sequencing (NGS) is a pivotal technique in genome sequencing due to its high throughput, rapid results, cost-effectiveness, and enhanced accuracy. Its significance extends across various domains, playing a crucial role in identifying genetic variations and exploring genomic complexity. NGS finds applications in diverse fields such as clinical genomics, comparative genomics, functional genomics, and metagenomics, contributing substantially to advancements in research, medicine, and scientific disciplines. Within the sphere of genomics data science, the execution of read simulation, mapping, and variant calling holds paramount importance for obtaining precise and dependable results. Given the plethora of tools available for these purposes, each employing distinct methodologies and options, a nuanced understanding of their intricacies becomes imperative for optimization. This research, situated at the intersection of data science and genomics, involves a meticulous assessment of various tools, elucidating their individual strengths and weaknesses through rigorous experimentation and analysis. This comprehensive evaluation has enabled the researchers to pinpoint the most accurate tools, reinforcing the alignment between the established workflow and the demonstrated efficacy of specific tools in the context of genomics data analysis. To meet these requirements, “VarFind”, an open-source and freely accessible pipeline tool designed to automate the entire process has been introduced (VarFind GitHub repository: VarFinder.)

The emergence of pangenome graphs represents a paradigm shift in genomics, moving beyond linear reference genomes to embrace the full spectrum of genetic diversity within and across species. These graph-based models provide a unified framework for representing alternative haplotypes, structural variants, and complex genomic rearrangements that are often missed by traditional approaches. This paper reviews the latest developments in pangenome graph construction, data structures, alignment algorithms, and variant inference. We explore recent human, plant, and microbial genomics applications, highlighting the advantages of graph representations in capturing population diversity and improving read mapping accuracy. We briefly discuss emerging directions such as machine learning-assisted graph analysis, although current applications remain limited and exploratory. Furthermore, we examine the emerging field of clinical genomics, where pangenome references have demonstrated measurable improvements in diagnostic yield—specifically increasing variant calling sensitivity for complex structural variants by up to 10–40% compared to linear models. We conclude by addressing ongoing challenges in graph scalability and standardization, aiming to guide future research and implementation in this rapidly evolving field.

Next-generation sequencing (NGS) is a pivotal technique in genome sequencing due to its high throughput, rapid results, cost-effectiveness, and enhanced accuracy. Its significance extends across various domains, playing a crucial role in identifying genetic variations and exploring genomic complexity. NGS finds applications in diverse fields such as clinical genomics, comparative genomics, functional genomics, and metagenomics, contributing substantially to advancements in research, medicine, and scientific disciplines. Within the sphere of genomics data science, the execution of read simulation, mapping, and variant calling holds paramount importance for obtaining precise and dependable results. Given the plethora of tools available for these purposes, each employing distinct methodologies and options, a nuanced understanding of their intricacies becomes imperative for optimization. This research, situated at the intersection of data science and genomics, involves a meticulous assessment of various tools, elucidating their individual strengths and weaknesses through rigorous experimentation and analysis. This comprehensive evaluation has enabled the researchers to pinpoint the most accurate tools, reinforcing the alignment between the established workflow and the demonstrated efficacy of specific tools in the context of genomics data analysis. To meet these requirements, "VarFind", an open-source and freely accessible pipeline tool designed to automate the entire process has been introduced (VarFind GitHub repository: https://github.com/shanikawm/varfinder)

The exponential growth of big data has transformed how large organisations leverage information to drive innovation, optimise processes, and maintain competitive advantages. However, managing and extracting insights from vast, heterogeneous data sources requires a scalable, secure, and well-integrated big data architecture. This paper proposes a comprehensive big data framework that aligns with organisational objectives while ensuring flexibility, scalability, and governance. The architecture encompasses multiple layers, including data ingestion, transformation, storage, analytics, machine learning, and security, incorporating emerging technologies such as Generative AI (GenAI) and low-code machine learning. Cloud-based implementations across Google Cloud, AWS, and Microsoft Azure are analysed, highlighting their tools and capabilities. Additionally, this study explores advancements in big data architecture, including AI-driven automation, data mesh, and Data Ocean paradigms. By establishing a structured, adaptable framework, this research provides a foundational blueprint for large organisations to harness big data as a strategic asset effectively.