Bioinformatics for Omics Data
Description
Presenting an area of research that intersects with and integrates diverse disciplines, including molecular biology, applied informatics, and statistics, among others, Bioinformatics for Omics Data: Methods and Protocols collects contributions from expert researchers in order to provide practical guidelines to this complex study. Divided into three convenient sections, this detailed volume covers central analysis strategies, standardization and data-management guidelines, and fundamental statistics for analyzing Omics profiles, followed by a section on bioinformatics approaches for specific Omics tracks, spanning genome, transcriptome, proteome, and metabolome levels, as well as an assortment of examples of integrated Omics bioinformatics applications, complemented by case studies on biomarker and target identification in the context of human disease. Written in the highly successful Methods in Molecular BiologyT series format, chapters contain introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and notes on troubleshooting and avoiding known pitfalls.
Authoritative and accessible, Bioinformatics for Omics Data: Methods and Protocols serves as an ideal guide to scientists of all backgrounds and aims to convey the appropriate sense of fascination associated with this research field.
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Content
Omics Technologies, Data, and Bioinformatics Principles.- Data Standards for Omics Data: The Basis of Data Sharing and Reuse.- Omics Data Management and Annotation.- Data and Knowledge Management in Cross-Omics Research Projects.- Statistical Analysis Principles for Omics Data.- Statistical Methods and Models for Bridging Omics Data Levels.- Analysis of Time Course Omics Datasets.- The Use and Abuse of -Omes.- Computational Analysis of High Throughput Sequencing Data.- Analysis of Single Nucleotide Polymorphisms in Case-Control Studies.- Bioinformatics for Copy Number Variation Data.- Processing ChIP-chip Data: From the Scanner to the Browser.- Insights Into Global Mechanisms and Disease by Gene Expression Profiling.- Bioinformatics for RNomics.- Bioinformatics for Qualitative and Quantitative Proteomics.- Bioinformatics for Mass Spectrometry-Based Metabolomics.- Computational Analysis Workflows for Omics Data Interpretation.- Integration, Warehousing, and Analysis Strategies of Omics Data.- Integrating Omics Data for Signaling Pathways, Interactome Reconstruction, and Functional Analysis.- Network Inference from Time-Dependent Omics Data.- Omics and Literature Mining.- Omics-Bioinformatics in the Context of Clinical Data.- Omics-Based Identification of Pathophysiological Processes.- Data Mining Methods in Omics-Based Biomarker Discovery.- Integrated Bioinformatics Analysis for Cancer Target Identification.- Omics-Based Molecular Target and Biomarker Identification.
