This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the “What”, “Why” and “How” of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches.
Author(s): Kang Ning
Series: Translational Bioinformatics, 19
Publisher: Springer
Year: 2023
Language: English
Pages: 172
City: Singapore
Preface
About the Book
Contents
About the Editor
Chapter 1: Introduction to Multi-Omics
1.1 The History of Omics
1.1.1 1971-1910: Discovery of DNA
1.1.2 1950-1968: Development of Knowledge about DNA
1.1.3 1977-Present: Sequencing of DNA Related Stories
1.2 Omics: DNA, RNA, Protein, and Microbiome
1.3 Databases and Tools for Omics Studies
1.4 Multi-Omics Applications
1.5 Future Perspectives
References
Part I: Omics Integration Techniques
Chapter 2: Biomedical Applications: The Need for Multi-Omics
2.1 Biomedical Big Data and Challenges
2.2 Deep Learning for Biomedical Big Data
2.2.1 Application of Functional Gene Mining
2.2.1.1 Using Comparisons and Annotations to Discover Genes
2.2.1.2 Using Expression Differences to Discover Genes
2.2.2 Protein Structure Prediction
2.3 Representative Databases and Analytical Tools
2.4 Representative Applications Based on Multi-Omics Big Data
2.4.1 Microbiome Mining for Cancer Research
2.4.2 The Twin Astronauts
2.4.3 Integrative Analysis of Genomics, Epigenomics, Transcriptomics
2.5 When Biocuration Meet Artificial Intelligence
2.5.1 The Current State of Biocuration
2.5.2 The Current State of Artificial Intelligence and its Application in Biocuration
2.5.3 In Alliance Is the Trend
2.6 Conclusion
References
Chapter 3: -Omics Technologies and Big Data
3.1 Multi-Omics Data Types and Underlying Technology
3.1.1 Genomics & Transcriptomics Data Analysis
3.1.2 Metagenomics Data Analysis
3.1.3 Proteomics Data Analysis
3.1.4 Metabolomics Data Analysis
3.1.5 Single-Cell Data Analysis
3.1.6 Biomedical Image Data Analysis
3.2 Biological Big Data Research
3.2.1 Research Trend of Biological Big Data
3.2.2 Challenges in -Omics Research
3.2.3 Multi-Omics Data Integration Tools and Databases
3.2.4 Auxiliary Data and Tools for Multi-Omics Data Integration
3.2.4.1 Relevant Metadata
3.2.4.2 Quality Assurance Example
3.3 Case Studies on Multi-Omics Data Integration: Resources and Applications
3.3.1 Multi-Omics Data Resources for Human Brain Diseases
3.3.2 Multi-Omics Data Resources for Cancer Cell Lines
3.3.3 Multi-Omics Research for Retinoblastoma
3.3.4 Multi-Omics Research for Cardiovascular Disease
3.3.5 Multi-Omics Research for Infectious Disease
References
Chapter 4: Multi-Omics Data Mining Techniques: Algorithms and Software
4.1 Introduction
4.2 Software for Multi-Omics Data Integration
4.2.1 Matrix Factorization Methods
4.2.1.1 Joint/Integrative Non-negative Matrix Factorization (jNMF, iNMF)
4.2.1.2 iCluster
4.2.1.3 iCluster+
4.2.1.4 Multiple Factor Analysis (MFA)
4.2.1.5 Joint and Individual Variation Explained (JIVE)
4.2.1.6 Joint Bayes Factor
4.2.2 Bayesian Approach
4.2.2.1 Bayesian Consensus Clustering (BCC)
4.2.2.2 Multiple Dataset Integration (MDI)
4.2.2.3 COpy Number and EXpression in Cancer (CONEXIC)
4.2.2.4 Multi-Omics Factor Analysis (MOFA)
4.2.2.5 Patient-Specific Data Fusion (PSDF)
4.2.3 Network-Based Methods
4.2.3.1 Similarity Network Fusion (SNF)
4.2.3.2 Low-Rank Approximation Based Multi-Omics Data Clustering (LRAcluster)
4.2.3.3 Pathway Representation and Analysis by Direct Reference on Graphical Models (PARADIGM)
4.2.3.4 NetICS
4.2.3.5 Perturbation Clustering for Data INtegration and Disease Subtyping (PINS) and PINSPLUS
4.2.4 Multiple Kernel Learning Methods and Multi-Step Analysis-Based Methods
4.2.4.1 Feature Selection Multiple Kernel Learning (FSMKL)
4.2.4.2 Regularized Multiple Kernel Learning Locality Preserving Projections (rMKL-LPP) & Web-rMKL
4.2.4.3 CNAmet
4.2.4.4 Integrative Bayesian Analysis of Genomics Data (iBAG)
4.3 Software for Multi-Omics Data Interpretation and Visualization
4.3.1 UCSC Xena
4.3.2 LinkedOmics
4.3.3 NetGestalt
4.3.4 3Omics
4.3.5 Paintomics 3
4.3.6 MethHC & MethHC 2
4.4 Challenges of Multi-Omics Data Manipulation
4.5 Conclusions and Future Perspectives
References
Part II: Applications of Multi-omics Analyses
Chapter 5: Multi-Omics Data Analysis for Cancer Research: Colorectal Cancer, Liver Cancer and Lung Cancer
5.1 Introduction
5.2 Various Multi-Omics Data Types and Selected Repositories
5.2.1 DriverDB v3
5.2.2 TCGA Portal
5.2.3 ICGC
5.2.4 CCLE
5.2.5 LinkedOmics
5.2.6 RHPCG
5.2.7 MOBCdb
5.2.8 Target
5.3 Selected Integrative Tools for Multi-Omics Analysis
5.4 Overview of Cancer Multi-Omics Research
5.4.1 Lung Cancer
5.4.2 Colorectal Cancer
5.4.3 Liver Cancer
5.5 Conclusion
References
Chapter 6: Multi-Omics Data Analysis for Inflammation Disease Research: Correlation Analysis, Causal Analysis and Network Anal...
6.1 Introduction
6.2 Human Gut Microbiota and Gut Microbiome
6.3 Relationship Between Inflammation Diseases and Human Gut Microbiota
6.4 The Advantages of Multi-Omics Approaches and the Methodology for Integrating the Multi-Omics Datasets
6.5 The Application of Multi-Omics Approaches to Inflammation Diseases and its Clinical Treatment with Microbiome Approaches
6.6 Conclusion
References
Chapter 7: Microbiome Data Analysis and Interpretation: Correlation Inference and Dynamic Pattern Discovery
7.1 Microbiome and its Importance
7.2 Experimental and Analytical Approaches for Microbiome Researches
7.2.1 Metagenomics
7.2.1.1 The Differences Between 16S and Metagenome (Ruairi Robertson 2020)
The Sequencing Principles
Different Fields of Study
Different Degrees of Species Identification
Application Fields of Metagenomics
The Process of Metagenomics Research
7.2.2 High-Throughput Sequencing Technology
7.2.2.1 Application of High-Throughput Sequencing Technology to Species Identification
7.2.2.2 Application of High-Throughput Sequencing Technology to Individual Identification
7.2.2.3 Technical Deviations of High-Throughput Sequencing Technology
7.2.3 Optimizing Microbiome Research Methods to Avoid Misunderstandings
7.2.3.1 Influencing Factors
7.2.3.2 Precautions during Sample Collection and Processing
Sample Storage Conditions
Set Negative Control
Set Positive Control
7.3 Microbiome Big Data and Challenges
7.3.1 Main Methods of Microbiome Analysis
7.3.1.1 Amplicon Analysis Software
7.3.1.2 Metagenomics Analysis Software
7.3.1.3 Statistics and Visualization Tools
7.3.2 The Basic Process of Microbiome Analysis
7.3.2.1 Experimental Design
7.3.2.2 Higher Level Analysis
7.3.2.3 Integrating Other Omics Data
7.3.3 The Basic Flow of Microbiome Data Analysis
7.4 Representative Microbiome Databases and Analysis Tools
7.5 Representative Microbiome Analysis Applications
7.5.1 Biogeographical Characteristics of the Intestinal Flora
7.5.2 Plasticity of Intestinal Flora (Dynamic Pattern)
7.5.3 Gene Mining
7.6 Conclusion and Perspectives
References
Chapter 8: Current Progress of Bioinformatics for Human Health
8.1 Introduction
8.2 Genome Comparison and Analysis Expands our Understanding of Genetic Diseases and Treatments
8.3 Transcriptome Analysis Enables the in-Depth Elucidation of Disease Mechanisms
8.4 A New Sight on Human-Microbe Associations by Data Mining of Microbiome
8.5 High-Resolution Bioinformatics on Single-Cell Level
8.6 Machine Learning Brings the Opportunity of Disease Screening and Prediction in Precise Medicine
References
Concluding Remarks
References
