Abstract

English: Advances in DNA sequencing have revolutionized biology, but converting vast sequencing data into usable, robust biological knowledge depends on sophisticated bioinformatics. This review details computational strategies spanning all phases of DNA sequence analysis, starting from raw reads through to functional interpretation and reporting. It begins by characterizing the main sequencing platforms (short-read, long-read, targeted, and metagenomic), describes critical pipeline steps (sample tracking, quality control, read alignment, error correction, variant and structural variant detection, copy number analysis, de novo assembly), and considers the impact of reference genome choice and computational algorithms. Recent machine learning advances for variant annotation and integration with other omics are discussed, with applications highlighted in rare disease diagnostics, cancer genomics, and infectious disease surveillance. Emphasis is placed on reproducible, scalable, and well-documented pipelines using open-source tools, workflow management (Snakemake, Nextflow), containerization, versioning, and FAIR data principles. The review concludes with discussion of ongoing challenges (heterogeneous data, batch effects, benchmarking, privacy) and practical recommendations for robust, interpretable analyses for both experimental biologists and computational practitioners.

Chinese: DNA测序的持续进步彻底改变了生物学和医学研究，而要将庞大的测序数据转化为可靠的生物学知识，则高度依赖高水平的生物信息学方法。本文详细介绍了DNA序列分析全流程的主流计算策略，涵盖原始reads到功能性注释乃至标准化报告的各个环节。首先评述主流测序技术平台（短读长读、靶向、宏基因组），系统阐述实验设计、样本追踪、数据质控、比对、纠错、变异与结构变异检测、拷贝数分析和de novo组装等流程要点，并分析参考基因组和比对算法对结果的影响。文章还总结了机器学习在变异注释、多组学整合中的最新进展，结合罕见病诊断、肿瘤基因组和病原体监测等实际案例深入说明其应用场景。着重强调可重复、高效、透明的分析流程，包括开源工具、流程管理(Snakemake、Nextflow)、容器化、版本管理与FAIR原则。最后讨论了异质数据、批次效应、评测标准和隐私保护等挑战，并为实验与计算生物学研究者提供实用建议。

Detailed Structure \& Outline

1. Introduction
   • Historical overview of DNA sequencing and bioinformatics development
   • The necessity of bioinformatics for handling scale, complexity, and error sources in modern sequence data
   • Scope: DNA focus (excluding RNA/proteome)
2. DNA Sequencing Technologies \& Data Properties
   • 2.1 Short-read platforms (e.g., Illumina): read length, quality, use cases
   • 2.2 Long-read platforms (PacBio, Nanopore): strengths, error profiles, applications
   • 2.3 Specialized applications: targeted/exome panels, metagenomics, amplicon/barcode-based diagnostics
3. Core Bioinformatics Pipeline Components
   • 3.1 Experimental metadata, sample barcoding, batch tracking: Crucial for reproducibility and QC
   • 3.2 Raw read QC: base quality, adapter/contaminant trimming, typical software/plots
   • 3.3 Read alignment/mapping: reference choice (GRCh38, hg19), algorithmic details (FM-index, seed-and-extend), uniqueness/multimapping
   • 3.4 Post-alignment processing: file sorting, duplicate marking, base recalibration, depth analysis
   • 3.5 Variant calling: SNVs/indels, somatic vs germline separation, quality filters and validation strategies
   • 3.6 Structural variant and CNV analysis: breakpoints, split/discordant reads, long-read tools
   • 3.7 De novo assembly, polishing, and consensus generation where relevant
4. Functional Interpretation
   • 4.1 Annotation: gene models, regulatory regions, predictive algorithms and public databases
   • 4.2 Multi-omics integration: joint analysis of genome, epigenome, transcriptome, regulatory networks
   • 4.3 Machine learning/AI approaches: variant scoring, prioritization, deep learning for sequence features
5. Reproducible and Scalable Workflows
   • 5.1 Pipeline frameworks: Snakemake, Nextflow, CWL, and workflow description languages
   • 5.2 Containerization: Docker, Singularity for reproducible deployments
   • 5.3 Version control/documentation: workflow hubs, deployment on GitHub, FAIR-compliant reporting
   • 5.4 Data management: standard formats (FASTQ/BAM/CRAM/VCF), secure storage, metadata
6. Applications \& Case Studies
   • Rare disease genomics: WGS for diagnosis
   • Cancer genomics: tumor heterogeneity, evolution, therapy response
   • Pathogen surveillance: rapid outbreak detection, resistance tracking
   • Other applications to match research interests
7. Challenges and Future Prospects
   • Technical: population-scale analysis, batch correction, pangenomes, benchmarking complexities
   • Practical: workflow sharing, legal/ethical/privacy issues
   • Methodological: handling new sequencing chemistries, multi-modal omics
8. Conclusions
   • Recap essential lessons
   • Actionable recommendations for robust design and execution
   • Prospects for further automation, integration, and clinical translation

Section Opening (English / 中文): High-throughput DNA sequencing has fundamentally transformed modern genomics, enabling detailed investigation of human diseases, microbial ecology, and evolution. However, the raw output—massive quantities of short or long reads—is only the starting point; extracting meaningful, robust insights requires optimized bioinformatics pipelines that ensure data integrity and biological relevance.

高通量DNA测序极大地推动了现代基因组学，助力人类疾病、微生物生态与进化等领域的深入探索。但测序仪输出的原始reads只是起点——要获得有意义、可靠的生物学结论，必须依赖优化的生物信息学流程以保证数据质量和生物学解释的可信度。