Molecular Pathway Analysis Using High-Throughput OMICS Molecular Data

Analysis of molecular pathway composition, architecture, and activation using high-throughput genomic, epigenetic, transcriptomic, proteomic, and metabolomic data

1st Edition - October 26, 2024
Imprint: Academic Press
Editor: Anton Buzdin
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 5 5 6 8 - 0
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 5 5 6 9 - 7

The field molecular pathway analysis evolves rapidly, and many progressive methods have recently been discovered. Molecular Pathway Analysis Using High-Throughput OMICS Data conta… Read more

Molecular Pathway Analysis Using High-Throughput OMICS Molecular Data

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The field molecular pathway analysis evolves rapidly, and many progressive methods have recently been discovered. Molecular Pathway Analysis Using High-Throughput OMICS Data contains the largest collections of molecular pathways. For the first time, guidelines on how to do genomic, epigenetic, transcriptomic, proteomic, and metabolomic data analysis in real-world research practice are given. Molecular Pathway Analysis Using High-Throughput OMICS Molecular Data also focuses on the pathway analysis applications for solving tasks in biotechnology, pharmaceutics, and molecular diagnostics It demonstrates how pathway analysis can be applied for the research and treatment of chronic and acute diseases, for next-generation molecular diagnostics, for drug design and preclinical testing; relevant real-world examples, molecular tests, and web resources will be reviewed in-depth. The book shows a tendency of erasing the borders between chemistry, physics, informatics, mathematics, biology, and medicine by means of novel research approaches and instruments, providing a truly multidisciplinary approach.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Dedication
Contributors
About the editor
Preface
Part I. Foundational information
Chapter 1. Past, current, and future of molecular pathway analysis
1.1 Molecular pathways
1.2 Quantitative omics data
1.3 Different levels of omics data analysis
1.4 Quantization of IMP activities
1.4.1 Annotation of functional roles for pathway participants
1.5 Applications of IMP analysis
1.5.1 Applications in medicine
1.6 Software for quantitative assessment of IMP activation
1.7 Concluding remarks
Chapter 2. Molecular data for the pathway analysis
2.1 Omics data available for the molecular pathway analysis
2.2 Data needed to reconstruct IMPs
2.3 Data needed to estimate activation levels of IMPs
Chapter 3. Benefits and challenges of OMICS data integration at the pathway level
3.1 Background
3.2 The comparison
3.2.1 Functional annotation of gene expression data
3.2.1.1 Topology analysis of pathway phenotype association
3.2.1.2 Topology-based score
3.2.1.3 Pathway-Express
3.2.1.4 Signal pathway impact analysis
3.2.2 Statistical tests
3.2.3 Mathematical modeling
3.2.4 Analysis of gene expression datasets
3.2.5 Biological relevance of cross-platform harmonized expression data
3.2.5.1 PTSD gene expression datasets
3.2.6 Marker gene and pathway analysis
3.3 Results
3.3.1 Cross-platform processing of transcriptomic and proteomic data
3.3.2 Building pathway activation profiles and assessment of batch effects
3.3.3 Mathematical modeling of data aggregation effects
3.3.4 Experimental model of cross-platform comparisons
3.3.5 Data aggregation effects assessed for RNA and protein expression levels
3.3.6 Comparison of data aggregation capacities of different PAL scoring methods
3.3.7 Retention of biological features
3.3.8 Gene and pathway analysis of PTSD datasets
3.4 Discussion
Abbreviations
Chapter 4. Controls for the molecular data: Normalization, harmonization, and quality thresholds
4.1 Background
4.2 Principles of harmonization algorithms
4.3 Differential clustering of human normal and cancer expression profiles
4.4 Correlation, regression, and sign-change analysis of cancer drug balanced efficiency score (BES) after application of different methods of harmonization
4.5 Discussion
Abbreviations
Chapter 5. Reconstruction of molecular pathways
5.1 Molecular pathways
5.2 An approach to reconstruct the pathway
5.2.1 The interactome model
5.2.2 Building gene-centric pathways
5.2.3 Overall functional annotation of reconstructed pathways—gene ontology classification
5.2.4 Visual annotation of reconstructed pathways
5.2.5 Algorithmic annotation of functional roles for pathway components
5.2.6 Examples of building and annotation of molecular pathways
Chapter 6. Qualitative and quantitative molecular pathway analysis: Mathematical methods and algorithms
6.1 Background
6.2 Topology-based methods for pathway activation assessment
6.2.1 Oncobox
6.2.2 Topology analysis of pathway phenotype association
6.2.3 Topology-based score
6.2.4 Pathway-express
6.2.5 Signal pathway impact analysis
6.2.6 iPANDA (in silico pathway activation network decomposition analysis)
6.3 Methods for database preparation for pathway activation assessment
6.3.1 Curation of pathway databases
6.3.2 Algorithmic annotation of pathway graph nodes
6.3.3 Finding gene importance factors for iPANDA
6.4 Personalized ranking of cancer drugs based on PALs
6.4.1 Oncobox balance efficiency score (BES)
6.4.2 Drug efficiency index (DEI)
6.5 Multi-omics data pathway analysis
6.5.1 Pathway activation assessment for methylome, microRNAs, and long noncoding (LNC) antisense (AS) RNAs
6.6 Concluding remarks
Abbreviations
Part II. Methods and guidelines
Chapter 7. Getting started with the molecular pathway analysis
7.1 Strategies of pathway analysis
7.2 Reconstruction of pathways and networks
7.3 The devil is in the things
7.4 Applications of molecular pathway analysis
7.5 Preprocessing of data for pathway analysis
7.6 Visualization of pathways
Chapter 8. Molecular pathway analysis using comparative genomic and epigenomic data
8.1 Types of pathway analysis requiring (epi)genomic data
8.2 Profiling of genomic pathway instability by using DNA mutation data
8.2.1 Initial mutation data
8.2.2 Algorithm validation dataset
8.2.3 Molecular target interrogation dataset
8.2.4 Clinical trial data
8.2.5 Molecular pathway data
8.2.6 Pathway instability scoring
8.2.7 PI analysis of cancer mutation signatures
8.2.8 PI-based drug scoring
8.2.9 Assessment of MDS family methods performance using clinical trial data
8.2.10 Application of MDS to identify putative drug target genes
8.3 Epigenetic marks as the measure of IMP molecular evolution
8.3.1 Study design
8.3.2 Source IMPs
8.3.3 Aggregated dN/dS data
8.3.4 RE regulation enrichment data
8.3.5 Functional classification of histone modifications
8.3.6 Aggregated NGRE score
8.3.7 Correlation between structural and regulatory evolutionary rate metrics
8.3.8 Functional groups of genes and pathways with different evolutionary rates
8.4 Concluding remarks
Chapter 9. Quantitative molecular pathway analysis using transcriptomic and proteomic data
9.1 Types of molecular pathway analysis
9.2 Quantitative analysis of gene expression
9.3 Quantitative assessment of the pathway activities
9.3.1 Calculation of PAL
9.3.2 Annotation of functional roles of IMP members
9.4 Software
9.4.1 Visualization of the pathways
9.4.2 Manual on the installation of oncoboxlib library
Chapter 10. MicroRNA data for quantitative analysis of molecular pathways
10.1 Relevance of microRNA profiles to molecular pathway activation analysis
10.2 Algorithmic analysis of pathway activation
10.3 Applications of pathway analysis for microRNAs
10.3.1 MiRImpact application to profile regulation of IMPs in bladder cancer
10.3.2 MiRImpact application to profile regulation of IMPs during cytomegaloviral infection
10.4 Concluding remarks
Chapter 11. Methods and tools for OMICS data integration
11.1 A snapshot of the current state of OMICS integration landscape
11.2 The most important part of this chapter
11.3 Best practices in preprocessing multiomics datasets
11.4 OMICS data integration in the eyes of a life scientist
11.4.1 From genotype to phenotype: Step I—Transcription
11.4.1.1 Unraveling gene regulatory networks (GRNs)
11.4.1.2 Decoding alternative splicing
11.4.1.3 Spotting novel transcripts
11.4.1.4 Quantifying gene expression
11.4.1.5 Probing post-transcriptional regulation
11.4.1.6 Integrating genomic variants and gene expression
11.4.1.7 Noncoding RNAs disrobed
11.4.2 From genotype to phenotype: Step II—Translation
11.4.3 From genotype to phenotype: Step III—Proteins
11.4.4 From genotype to phenotype: Step IV—Metabolites
11.5 Data scientist summary
11.6 Life scientist summary
Part III. Practical applications
Chapter 12. Molecular pathway approach in clinical oncology
12.1 Gene expression data in clinical oncology
12.2 Conversion of pathway activation data into personalized prediction of cancer drug efficacy
12.2.1 Molecular pathway databank
12.2.2 Clinical trial database
12.2.3 Drug target database
12.2.4 Algorithmic scoring of cancer drug efficiencies
12.3 Examples of IMP-based clinical ranking of drugs in oncology
12.3.1 Example 1. Ranking of cancer drugs based on mRNA expression data
12.3.2 Example 2. Comparison of alternative drug scoring methods
12.4 Conclusion
Chapter 13. Molecular pathway approach in pharmaceutics
13.1 Molecular pathway analysis in general
13.1.1 What is intracellular molecular pathway
13.1.2 Molecular pathway analysis
13.1.3 Pathway analysis instruments
13.2 Pathway analysis to facilitate tasks in molecular pharmacology
13.2.1 Task 1. To establish mechanism of action of drug candidate X
13.2.2 Task 2. To identify robust response biomarkers for drug (candidate) X
13.2.3 Task 3. To identify drugs that act similarly to drug (candidate) X or to identify molecular targets of X
13.3 Practical examples how IMP analysis may help
13.4 Useful online resources
13.5 Conclusion
Chapter 14. Molecular pathway approach in biotechnology
14.1 Pathways of biotechnology
14.1.1 Biotechnology
14.1.2 The pathways
14.1.3 Molecular pathways in biotech
14.2 Examples of pathway analysis in biotechnology
14.2.1 Golden rice
14.2.2 A humanized N-glycosylation system for expression of human proteins in yeast
14.2.3 Optimization of the photosynthesis system
14.3 Conclusion and perspective
Chapter 15. Molecular pathway approach in biology and fundamental medicine
15.1 Molecular pathway analysis in biomedicine
15.2 IMP analysis in oncology
15.2.1 IMPs in cancer
15.2.2 Quantitative analysis of IMPs in oncology
15.2.3 IMPs as cancer biomarkers
15.2.4 Pathway-based scoring of cancer drug efficiencies
15.2.4.1 Gene signature PAL biomarkers
15.2.4.2 Molecular mechanism-based biomarkers
15.2.4.3 Translation to personalized medicine
15.3 Other applications of IMP analysis in biomedicine
15.3.1 Ranking and repurposing of drugs
15.3.2 Understanding molecular mechanisms
15.4 Conclusion
Index

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Molecular Pathway Analysis Using High-Throughput OMICS Molecular Data

Analysis of molecular pathway composition, architecture, and activation using high-throughput genomic, epigenetic, transcriptomic, proteomic, and metabolomic data

Purchase options

BLOOM INTO SPRING SAVINGS

Institutional subscription on ScienceDirect

Anton Buzdin

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