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.

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.

Computational models have been playing a significant role for the computer-based analysis of biological and biomedical data. Given the recent availability of genomic sequences and microarray gene expression data, there is an increasing demand for developing and applying advanced computational techniques for exploring these types of data such as functional interpretation of gene expression data, deciphering of how genes and proteins work together in pathways and networks, extracting and analysing phenotypic features of mitotic cells for high throughput screening of novel anti-mitotic drugs. Successful applications of advanced computational algorithms to solving modern life-science problems will make significant impacts on several important and promising issues related to genomic medicine, molecular imaging, and the scientific knowledge of the genetic basis of diseases.

The definitive guide to the clinical and scientific aspects of pulmonary medicine―fully updated with the latest advances in the field A Doody's Core Title for 2023! Fishman’s Pulmonary Diseases and Disorders delivers unparalleled coverage of pulmonary medicine. With nearly 2500 illustrations, 60 videos, and 22,000 references, this peerless, two-volume resource provides a comprehensive overview of the scientific basis of lung function in health and disease. You’ll find detailed coverage of the broad array of disorders affecting the respiratory system, including obstructive and restrictive diseases, pulmonary vascular disorders, sleep-disordered breathing, lung neoplasms, respiratory infections, and respiratory failure. In addition, you’ll learn about all the latest advances, including molecular development of the lung, stem cells and respiratory disease, the genetics of pulmonary disease, the growth of personalized medicine, technical advances in lung transplantation, and much more. Notable new content in the 6th edition includes discussion of the respiratory effects of vaping, detailed consideration of the idiopathic interstitial pneumonitides, state-of the-art discussion of lung nodules, a summary of the use of immunotherapy in the treatment of lung cancer, COVID-19-related lung disease and its management, and a comprehensive discussion of noninvasive ventilation, including its use in ambulatory and ICU settings. In addition, new chapters on cystic lung disease, lung cancer screening, the lung microbiome, developmental lung disorders, nocardiosis and actinomycosis, and application of ECMO are included.

This volume supplements Volume 183 in the "Methods in Enyzmology" series and complements Volume 224. It addresses a variety of areas in which computers are used to manage and manipulate macromolecular sequence data. The manipulations include searching, aligning and determining the significance of similarities as well as the construction of phylogenetic trees that show evolutionary history of related sequences