
Recent Trends in 'Computational Omics: Concepts and Methodology'
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Content
- Intro
- Contents
- Preface
- Chapter 1
- Integrative Omics: Current Status and Future Directions
- Abstract
- 1. Introduction
- 1.1. Omics
- 1.1.1. Genomics
- 1.1.2. Transcriptomics
- 1.1.3. Proteomics
- 1.1.4. Metabolomics
- 2. From Omics to Multi/Integrative Omics
- 2.1. First Step Towards Multi-Omics
- 2.2. Integrative Omics
- 2.3. Potential of Integrative Omics
- 3. Integration of Multi-Omics Data Resources
- 4. Data Mining for Omics Data
- 5. Data Mining Techniques
- 5.1. Data Mining Language/Tools
- 5.1.1. R-Language: As a Data Mining Tool
- 5.1.2. Weka
- 6. Integrative Data Mining Challenges and Opportunities
- 6.1. Bioinformatics Challenges
- 6.2. Data Challenges
- 7. Future Direction
- 7.1. Artificial Intelligence
- 7.1.1. Machine Learning
- 7.1.2. Deep Learning
- 7.1.3. Data Dependencies
- 7.1.4. Feature Learning
- 7.1.5. Problem-Solving Approach
- 7.2. Big Data Analytics
- 7.2.1. Big Data Analytics Technologies and Tools
- Conclusion
- Conflict of Interest
- References
- Chapter 2
- Database Resources for Computational Omics
- Abstract
- Introduction
- Human Health/Disease Related Omic Databases
- Kyoto Encyclopedia of Genes and Genomes (KEGG)
- Human Metabolome Database (HMDB)
- Small Molecule Pathway database (SMPDB)
- WikiPathways
- Proteomics Identifications Database (PRIDE)
- Human Ethnic and Region Specific Omics Database (HEROD)
- Cardiovascular Disease Database (C/VDdb)
- LinkedOmics
- Multi-Omics Breast Cancer Database (MOBCdb)
- Pancreatic Cancer Database (PCD)
- Lnc2Cancer
- Crops/Plants Related Omic Databases
- OryzaExpress
- RiceSRTFDB
- TAIR Database
- PhosPhAt
- MCENet
- MODEM
- GourdBase
- TOMATOMICS
- Cotton Functional Genomics Database (CottonFGD)
- Gramene Database
- CoP
- GabiPD
- SoyFN
- UniVIO
- Other Omic Databases
- BioCyc
- Integrated Microbial Genomes (IMG) System
- KaPPA-View4
- CyanOmics
- The CyberCell Database (CCDB)
- Chinese Hamester Genome Database
- Yeast Metabolome Database (YMDB)
- PATRIC
- Central Carbon Metabolic Flux Database (CeCaFDB)
- Ciona Intestinalis Protein Database (CIPRO)
- Newt-Omics
- Conclusion
- Conflict of Interest
- Acknowledgments
- References
- Chapter 3
- Algorithms in Bioinformatics
- Abstract
- Introduction
- Algorithm Basics and Mechanisms
- Definition
- Types of Algorithms Often Used in Bioinformatics
- Combinatorial Pattern Matching
- Machine Learning
- Dynamic Programming Algorithms
- Divide and Conquer Algorithms
- Clustering Algorithms
- Randomized Algorithms
- Exhaustive Searching or Brute-Force Algorithms
- Greedy Algorithms
- Graph Algorithms
- Hidden Markov Models
- Branch and Bound Algorithm
- Conclusion
- Conflict of Interest
- References
- Chapter 4
- Recent Trends in 'Computational Transcriptomics'
- Abstract
- 1. Introduction
- 2. cDNA Microarray Technology
- 2.1. Representation of Gene Expression Data
- 2.2. Microarray Database
- 2.3. Microarray Data Analysis
- 2.3.1. Data Preprocessing and Normalization (Quality Control)
- 2.3.2. Differential Gene Expression
- 3. RNA-Seq
- 3.1. RNA-Seq Database
- 3.1.1. Sequence Retrieval Archives (SRA)
- 3.2. RNA-Seq Pipeline
- 3.3. RNA-Seq Data Analysis
- 3.3.1. Data Preprocessing
- 3.3.2. Read Mapping
- 3.3.2.1. Challenges and Possible Solution
- 3.3.3. Quantification
- 3.3.3.1. Challenges and Possible Solution
- 3.3.4. Normalization
- 3.3.5. Differential Gene Expression
- 4. Clustering
- 4.1. Clustering for Transcriptome Analysis
- 5. Computational Transcriptomics through Bioconductor
- 6. Scope of Transcriptomics
- 7. cDNA Microarray versus RNA-Seq
- 8. Selection of Technique for Transcriptome Analysis
- Conclusion
- Conflict of Interest
- References
- Chapter 5
- Single-Cell RNA Sequencing Technologies and Computational Analysis
- Abstract
- 1. Introduction
- 2. Isolation of Single-Cells
- 3. Single-Cell Sequencing Technologies
- 4. Computational Pipeline to Analyze scRNA-seq Data
- 4.1. Quality Control and Filtering of the Data
- 4.2. Normalization of Data
- 4.3. Correcting for Confounding Factors
- 4.4. Batch Effects Correction and Data Integration in scRNA-seq Data
- 4.5. Feature Selection
- 4.6. Dimensionality Reduction
- 4.7. Clustering of the scRNA-seq Data
- 4.8. Cluster Annotation
- 4.9. Trajectory Inference
- 5. Applications of scRNA-seq
- Conclusion and Outlook
- Conflict of Interest
- References
- Chapter 6
- Computational Analysis of Metabol(om)ic Networks
- Abstract
- Introduction
- Metabolic Network Modelling and Analysis
- Top-Down Approach
- Bottom-Up Approach
- Databases, Tools and Resources for Metabolomics
- Metabolic Networks as Graphs
- Chemical Graphs
- Metabolic Pathways as a Graph
- Mathematical Representations
- Adjacency Matrix
- Stoichiometric Matrix
- The Elemental Space
- The Fundamental features of Stoichiometric matrix
- The Stoichiometric Matrix as a Connectivity Matrix
- Reaction Maps, Open and Closed Networks
- The Stoichiometry of Metabolites
- Pathway Analysis
- Metabolic Fluxes for Drug Target Identification
- Metabolic Control Analysis (MCA)
- Drug Target Identification Using MCA
- Differential Control Analysis for Drug Selectivity
- Cancer Control
- Metabol(om)ic Networks
- The Signalling and Gene Regulatory Space
- Gene-Protein-Reaction-Metabolite Association Network
- The Future of Computation and Metabolomics
- Conflict of Interest
- Acknowledgments
- References
- Chapter 7
- Immuno-Informatics: Computational Trends in Immuno-Oncogenomics
- Abstract
- 1. Introduction
- 1.1. Introduction to Cancer Immunotherapy and Concepts
- 1.2. Informatics in Cancer
- 1.2.1. Sample Heterogeneity
- 1.2.2. Somatic Mutation Calling
- 1.2.3. Annotation
- 1.2.4. Mutational Signatures
- 1.2.5. Molecular Subtypes
- 1.2.6. Pathway Analysis
- 1.3. Immuno-Informatics
- 1.3.1. HLA Typing
- 1.3.2. Prediction of Epitopes
- 1.3.3. Sample Heterogeneity
- 1.3.4. Pathway Analysis
- 1.3.5. Adaptive Immune Receptor Repertoire Sequencing
- 1.4. Interactions between Cancer and the Immune System
- 1.4.1. Cancer Antigens
- 1.4.2. Tumor-Intrinsic Immune Escape
- 1.4.3. Local Immune Response
- 1.4.4. Tumor-Extrinsic Immune Escape
- 1.4.5. Systemic Immune Response
- 2. Data Sources
- 2.1. Cancer Databases
- 2.2. Cancer Immunology Related Databases and Web Servers
- 2.3. Epitope Databases
- 3. Tools
- 4. Discussion
- Conflict of Interest
- Acknowledgement
- References
- Chapter 8
- OMICS in Cancer: From Diagnostics
- to Therapeutics
- Abstract
- Introduction
- Genomics
- Therapeutic Approach Using Genomics
- Whole Genome Sequencing
- Identifying Therapeutic Targets
- Genome Instability
- Pharmacogenomics
- Cancer Immunology
- Epigenomics
- Therapeutic Approach Using Epigenomics
- Transcriptomics
- Microarray
- Gene-Chip Microarray
- RNA-Seq
- Therapeutic Approach Using Transcriptomics
- Lipidomics
- Therapeutic Approach Using Lipidomics
- Proteomics
- Conclusion
- References
- Chapter 9
- Role of Network Biology in Cancer Research
- Abstract
- 1. Introduction
- 2. Cancer Biomarkers
- 3. Biological Networks
- 3.1. Protein-protein Interaction Network
- 3.2. Gene Regulatory Network
- 3.3. Gene Co-expression Network
- 3.4. Metabolic Network
- 4. Properties of a Biological Network and Their Significance
- 4.1. Degree and Degree Distribution
- 4.2. Scale-free Network, Hubs and Their Attributes
- 4.3. Clustering Coefficients and Module
- 4.4. Shortest and Average Path Lengths
- 4.5. Diameter
- 4.6. Betweenness Centrality and Bottleneck
- 4.7. Network Density
- 5. Different Levels of Omics Data
- 6. Bioinformatics Resources (Databases, Software and Web Servers) for Cancer Research
- 6.1. Cancer Specific Gene/Protein/Mirna Interaction Databases
- 6.2. Other Protein-Protein and Protein-Chemical Interaction Databases Used for Cancer Research
- 6.3. Tools and Software for Network Construction, Visualization and Analysis
- 7. Challenges of Network-Based Biomarker Discovery
- Conclusion
- Conflict of Interest
- Acknowledgments
- References
- Chapter 10
- Bioinformatics Resources for the Prediction of Cancer Prognosis and Its Recurrence
- Abstract
- Introduction
- Artificial Intelligence
- Role of Artificial Intelligence in the Development of Computational Tools and Algorithms for Cancer Prognosis and Its Recurrence
- Machine Learning, Deep Learning and Neural Networks
- Tools for Cancer Prediction
- Pegasus and Other Gene Fusion Prediction Tools
- PREDICT (www.predict.nhs.uk/)
- CancerMath (www.lifemath.net/cancer/breastcancer/therapy/)
- Neoadjuvant Therapy Outcomes Tool
- The Breast Cancer Risk Assessment Tool (BCRAT, bcrisktool.cancer.gov/)
- NeoMutate (omictools.com/neomutate-tool)
- MutSigCV
- DrGap (omictools.com/drgap-tool)
- ACPred (server.malab.cn/ACPred-FL/)
- Outdated Tools
- CanPredict (omictools.com/canpredict-tool)
- Algorithms for Cancer Prediction
- Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) Models
- DeepSurv (pypi.org/project/deepsurv/)
- DriverML (github.com/HelloYiHan/DriverML)
- IntOGen Mutation (www.intogen.org/search)
- Tools for Cancer Recurrence
- CTS5 (www.cts5-calculator.com/)
- Adjuvant! (www.adjuvantonline.com)
- Oncotype DX recurrence scoring tools (online.genomichealth.com/Login.aspx)
- CORE (rdrr.io/cran/CORE/man/CORE.html)
- Algorithms for Cancer Recurrence
- RECUR algorithm (www.ncbi.nlm.nih.gov/pmc/articles/ PMC4732933/)
- Conclusion
- Conflict of Interest
- Acknowledgment
- References
- Chapter 11
- Computational Metagenomics: Current Status and Challenges
- Abstract
- Introduction
- Next Generation Sequencing (NGS) Methods
- Illumina Sequencing Technology
- Roche 454 Sequencing
- Ion Torrent Platform
- PacBio Sequencing
- Oxford Nanopore Technologies
- Evaluation of Sequencing Libraries and Quality Check
- Taxonomic Classification
- Taxonomic Binning
- Metagenomic Assembly
- Gene Prediction and Function Assignment
- Metabolic Pathway Reconstruction
- Metagenomics Data Analysis Pipeline
- Microbial Genome Resources
- Conclusion
- References
- Chapter 12
- Integrated Omics for Dissecting Host-Pathogen Interaction: Challenges and Opportunities
- Abstract
- Introduction
- Approaches for Generation of Multi-Omics Data with Respect to Host-Pathogen Interaction
- Genomics
- Transcriptomics
- Proteomics
- Metabolomics
- Other Omics Approches
- Data Types for Integrated Omics
- Genetic Variation
- Epigenetics
- Gene Expression
- Proteomics and Metabolomics
- Microbiome
- Opportunities in Integrated Omics Approches for Dissecting Host-Pathogen Interaction
- Genome and Transcriptome Assembly
- Gene Expression Data Analysis
- Gene Set Enrichment Analysis
- Pathway Modeling and Simulation
- Metabolic Flux Analysis
- Network Generation and Analysis
- Protein Modeling
- Molecular Docking
- Pharmacokinetics and Pharmacodynamics
- Molecular Dynamics Simulation
- Computational Resources for Integrated Omics
- Development of Databases for Storage of Big Data
- Development of Tools for Integration of Multi-Omics Data
- Challenges in Integrated Omics
- Data Issues
- Biological Knowledge
- Individual Omics Data Sets
- Integration Issues
- Experimental Challenges
- Future Outlook
- Conclusion
- References
- Chapter 13
- Development and Analysis of Simple Sequence Repeats in Chloroplast Genomes of Genus Saccharum: A Computational Study
- Abstract
- Introduction
- Data and Methodes
- Mining of SSRs in Chloroplast Genomes of Genus Saccharum
- Identification of Common, Polymorphic, and Unique SSRs
- Results
- Frequency and Distribution of cpSSRs in Genus Saccharum
- Common, Polymorphic, and Unique cpSSRs
- Conclusion
- Acknowledgment
- References
- About the Editor
- Index
- Blank Page
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