- ホーム
- > 洋書
- > 英文書
- > Science / Mathematics
Full Description
This second edition volume expands on the previous edition with updates on the latest methodologies in the transcriptomics field. The chapters in this book cover topics such as spatial omics, long-read sequencing technology, tissue microarrays, analysis of saliva and extracellular vesicles, machine learning and artificial intelligence-based approaches for analysis of singe cells transcriptome, and large sets of data on multi-omics including transcriptomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.
Cutting-edge and practical, Gene Expression Analysis: Methods and Protocols, Second Edition is a valuable resource for advanced undergraduate and graduate students studying gene expression analysis, and scientists interested in learning more about this rapidly advancing field.
Contents
The Salivary Transcriptome: A Window into Local and Systemic Gene Expression Patterns.- Digital PCR Based Gene Expression Analysis Using a Highly Multiplexed Assay with Universal Detection Probes to Study Induced Pluripotent Stem Cell Differentiation into Cranial Neural Crest Cells.- Identification of Circular RNA Variants by Oxford Nanopore Long-Read Sequencing.- Combining Short- and Long-Read Transcriptomes for Targeted Enzyme Discovery.- Spatial-Omics Methods and Applications.- Semi-Quantitative Cardiac Specific Gene Expression Validation of the DNA Methylation Microarray in Human Mesenchymal Stem Cells.- Fusion Transcript Detection from Short-Read RNA-Seq.- RNA-Seq and Gene Set Enrichment Analysis (GSEA) in Peripheral Blood Mononuclear Cells (PBMCs).- Integrating Tissue Microarray to GeoMx® Digital Spatial Profiler Spatial Transcriptomics Assay with Bioinformatics Analysis.- Establishing a De Novo Annotation of Human Liver Transcriptome Based on Long-Read Direct RNA Sequencing Technology and a Liver-Specific Humanized Mouse Model.- Exploring Extracellular Vesicle Transcriptomic Diversity through Long-Read Nanopore Sequencing.- Enhancing Robust and Stable Feature Selection through the Integration of Ranking Methods and Wrapper Techniques in Genetic Data Classification.- Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach.- A Selective Review of Network Analysis Methods for Gene Expression Data.- Deconvolving Bulk Transcriptomics Samples to Obtain Cell Type Proportion Estimates.- Applying AI/ML for Analyzing Gene Expression Patterns.- A Machine Learning Pipeline to Screen Large In Vivo Molecular Data to Curate Disease Signatures of High Translational Potential.- Regulatory Perspectives for Gene Expression-Based Diagnostic Devices.
