Computational Methods for Next Generation Sequencing Data Analysis (Hardcover)

Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications 

This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: 

Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols.

Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. 

Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. 

Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis.

Computational Methods for Next Generation Sequencing Data Analysis:

• Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms
• Discusses the mathematical and computational challenges in NGS technologies
• Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more

This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

本書介紹了下一代定序（NGS）數據分析的核心算法技術，並討論了各種計算技術和應用。本書深入調查了NGS的一些最新發展，並討論了NGS技術在各個應用領域中的數學和計算挑戰。本書的18章由生物信息學專家撰寫，代表了在快速增長的NGS領域中積極貢獻的領先實驗室的最新工作。本書分為四個部分：

第一部分關注NGS分析的計算和實驗基礎設施，包括雲計算、代謝途徑重構的模塊化流水線、大規模病毒定序的混合策略以及高保真度定序協議等章節。

第二部分集中於DNA定序數據的分析，包括經典的支架問題、基因組變異的檢測（包括插入和刪除）以及DNA甲基化定序數據的分析。

第三部分專注於RNA-seq數據的分析。本部分討論了轉錄組組裝的算法和軟件工具，以及檢測替代剪接的方法和轉錄組定量和差異表達分析的工具。

第四部分探討了NGS在微生物組學應用中的計算工具，包括對病毒群體NGS讀數的錯誤修正、病毒拟種群重構的方法，以及微生物組分析中最先進的方法和未來趨勢的調查。

《下一代定序數據分析的計算方法》：

- 回顧了計算技術，如新的組合優化方法、數據結構、高性能計算、機器學習和推理算法。
- 討論了NGS技術中的數學和計算挑戰。
- 包括NGS錯誤修正、新基因組轉錄組組裝、從NGS讀數中檢測變異等內容。

本書是生物醫學專業人士擴展其NGS數據分析計算技術知識的參考書。該書也適用於生物信息學的研究生和博士生。