The rapid advancement of high-throughput sequencing technology, also known as next-generation sequencing (NGS), has significantly propelled research in genomics and transcriptomics. However, the exponential growth of data poses substantial challenges to data analysis capabilities, effectively positioning bioinformatics analysis platforms as core tools for the interpretation of high-throughput sequencing datasets. This review explores the integration and application of high-throughput sequencers and bioinformatics analysis platforms in bulk sequencing, focusing on the critical roles of data processing workflow integration, algorithm optimization, and automated processing in enhancing research efficiency and accuracy. Firstly, it introduces the fundamental principles and instrument characteristics of various high-throughput sequencing technologies and their applicable scenarios in bulk sequencing, and analyzes NGS data preprocessing and quality control, the evolution of sequence alignment and variant detection algorithms, and the coordination between automated analysis workflows and sequencing systems. On this basis, the integration modes are discussed from three perspectives: integration in research settings, workflow-based integration in clinical testing, and novel integration driven by intelligent and cloud platforms. By incorporating practical examples from population genomics research, precision oncology, and rapid pathogen detection, it explores the value of integrating high-throughput sequencers with bioinformatics analysis platforms in improving data processing efficiency, analytical accuracy, and the translational potential of results. Finally, the future prospects of artificial intelligence algorithms, cloud computing platforms, real-time analysis, and multimodal data integration in bioinformatics analysis for bulk sequencing are discussed.