The field of genomic analysis has undergone rapid evolution, becoming a cornerstone of modern biological and biomedical research. The continual development of high-throughput sequencing technologies has generated an unprecedented volume and diversity of genomic data, enabling breakthroughs in areas such as disease gene discovery, evolutionary biology, and precision medicine. Despite these advances, significant challenges remain in effectively processing, analyzing, and interpreting such vast datasets. Persistent questions relate to the integration of heterogenous data types, the accuracy and scalability of analytical frameworks, and the reliable translation of raw sequencing reads into actionable biological knowledge. In response, the scientific community is actively exploring new computational approaches and re-evaluating existing methodologies to address these critical gaps and unresolved debates.

This Research Topic aims to showcase original research and insightful reviews that propel the field of genomic analysis forward through novel methods, innovative tools, and advanced algorithms. The objective is to enhance understanding of computational and analytical strategies across all stages of genomic data analysis, from initial sequence processing to high-level biological interpretation. By attracting interdisciplinary work, this initiative seeks to address how state-of-the-art algorithmic and software solutions can improve accuracy, efficiency, and reproducibility across various applications. It also aims to highlight ongoing challenges such as benchmarking performance, managing data complexity, and minimizing biases in large-scale studies, while encouraging the development of adaptable and interoperable tools for the research community.

The scope of this Research Topic includes both theoretical and applied aspects of computational genomic analysis. It covers a wide range of approaches and solutions used for diverse sequencing technologies and biological contexts but does not include studies unrelated to computational methods for genomic data interpretation. To gather further insights within these boundaries, we welcome articles addressing, but not limited to, the following themes:

Algorithms for sequence alignment, genome assembly, and variant detection

Cutting-edge strategies for functional annotation and gene prediction

Machine learning and artificial intelligence in genomic analysis

Benchmarking and comparative evaluation of bioinformatics tools

Scalable and reproducible analysis pipelines

Approaches for integrating multi-omics and complex genomic datasets

Methods for analyzing structural variants and rare genomic alterations

Quality assurance and standardization processes for high-throughput sequencing data

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Methods, Tools and Algorithms in Genomic Analysis