All about bioinformatics : from beginner to expert /: from beginner to expert. (2023)

• Record Type: Book
• Further Information: Note: Yasha Hasija.
• Authors: Hasija, Yasha
• Contents: CHAPTER 1 What is bioinformatics? 1.1 Introduction 1.2 History 1.3 Biological databases 1.4 Algorithms in computational biology 1.5 Genetic variation and bioinformatics 1.6 Structural bioinformatics 1.7 High-throughput technology 1.8 Drug informatics 1.9 System and network biology 1.10 Machine learning in bioinformatics 1.11 Bioinformatics workflow management systems 1.12 Application of bioinformatics References CHAPTER 2 Introduction to biological databases 2.1 Introduction 2.1.1 Characteristics of biological data 2.2 Types of databases 2.2.1 Primary database 2.2.2 Secondary database 2.2.3 Composite database 2.3 Models of databases 2.3.1 Flat file 2.3.2 Hierarchical model 2.3.3 Network model 2.3.4 Entity relationship model 2.3.5 Relational database model 2.3.6 Other models 2.4 Primary nucleic acid databases 2.4.1 EMBL 2.4.2 GenBank 2.4.3 DDBJ 2.5 Primary protein databanks 2.5.1 PDB 2.5.2 SWISS-PROT 2.6 Secondary protein databases 2.6.1 CATH 2.6.2 SCOP 2.6.3 Prostate 2.7 Composite sequence databases 2.7.1 Meta-databases 2.8 Genomic, proteomic, and other databases 2.8.1 The search engines for literature 2.9 Genome projects and genomic databases of humans, animals, fungi, and microorganisms 2.9.1 Humans 2.9.2 Animals 2.9.3 Fungi 2.9.4 Microorganisms 2.9.5 Plant and crop genomic database 2.9.6 Organelle database 2.9.7 Pathway databases References CHAPTER 3 Statistical methods in bioinformatics 3.1 Introduction 3.2 Statistics at the interface of bioinformatics 3.3 Measures of central tendency 3.3.1 Mean 3.3.2 Median 3.3.3 Mode 3.3.4 Percentiles, quartiles and interquartile range 3.4 Skewness and kurtosis 3.5 Variability and its measures 3.5.1 Variance 3.5.2 Standard deviation 3.5.3 Standard error 3.5.4 Coefficient of variation 3.6 Different types of distributions and their significance 3.6.1 Probability distributions 3.6.2 Continuous probability function 3.6.3 Discrete probability function 3.6.4 Normal distribution and normal curve 3.6.5 Normal curve 3.6.6 Asymmetrical distribution 3.7 Sampling 3.8 Probability 3.8.1 Laws of probability 3.9 Comparing the means of two or more data variables or groups 3.9.1 Independent samples t-test 3.9.2 One sample t-test 3.9.3 Paired samples t-test 3.9.4 ANOVA 3.9.5 The Chi-square tests 3.9.6 Test of independence 3.9.7 Test of goodness of fit 3.9.8 Correlation and regression 3.9.9 A look into correlation and regression 3.10 Platforms employed for statistical analysis 3.10.1 Downstream analysis and visualization 3.11 Gene ontology & pathway analysis 3.11.1 Singular enrichment analysis (SEA) 3.11.2 Gene set enrichment analysis (GSEA) 3.11.3 Modular enrichment analysis (MEA) 3.11.4 Correlation networks 3.12 Future prospects and conclusion References CHAPTER 4 Algorithms in computational biology 4.1 Sequence alignment 4.1.1 Local alignment 4.1.2 Global alignment 4.1.3 Gap penalty 4.2 Pair-wise alignment 4.3 Dot-matrix method 4.4 Dynamic programming 4.4.1 Needleman-Wunsch 4.4.2 Smith Waterman algorithm 4.5 Scoring matrices 4.5.1 Scoring matrices for amino acids 4.5.2 PAM (point accepted mutation) 4.5.3 BLOcks SUbstitution matrix (BLOSUM) 4.6 Word methods 4.7 Multiple sequence alignment 4.7.1 Progressive alignment 4.7.2 Iterative method 4.7.3 MSA filtering 4.7.4 Filtering techniques’ fundamental principles 4.7.5 Programs and methods for multiple sequence alignment 4.7.6 Representation and structural inference 4.8 Phylogenetics 4.8.1 Molecular phylogenetics 4.8.2 Phylogenetics trees 4.8.3 Properties 4.8.4 Building methods 4.8.5 Distance matrix method 4.8.6 Bayesian inference References CHAPTER 5 Genetic variations 5.1 Introduction 5.2 Types of variations 5.3 Effects of genetic variation 5.4 Biological database 5.4.1 Database of human genetic variation 5.4.2 Predicting the clinical significance of human genetic variation 5.5 Phenotype-genotype association 5.6 Pharmacogenomics 5.6.1 Drug receptors 5.6.2 Drug uptake 5.6.3 Drug breakdown 5.7 Pharmacogenomics and targeted drug development 5.7.1 Personalized medicine 5.7.2 Personalized medicine drivers 5.7.3 Future aspects of pharmacogenomics in personalized medicine 5.8 Computational biology methods for decision support in personalized medicine 5.8.1 Pharmacogenomics information References CHAPTER 6 Structural bioinformatics 6.1 Introduction 6.2 Viewing protein structures 6.3 Alignment of protein structures 6.4 Structural prediction 6.4.1 Use of sequence patterns for protein structure prediction 6.4.2 Prediction of protein secondary structure from the amino acid sequence 6.4.3 Chou Fasman method 6.4.4 GOR method 6.4.5 Prediction of three-dimensional protein structure 6.4.6 Evaluating the success of structure predictions References CHAPTER 7 High throughput technology 7.1 Omics theory 7.2 High-throughput technologies 7.3 Genomics 7.3.1 What is DNA? 7.3.2 DNA microarray 7.3.3 DNA sequencing 7.3.4 Whole exome sequencing (WES) 7.3.5 Single cell DNA-SEQ (sc-DNA-seq) 7.4 Epigenomics 7.4.1 ChIP-seq 7.4.2 Whole-genome shotgun bisulfite sequencing (WGSBS) 7.5 Transcriptomics 7.5.1 RNA-seq 7.6 Proteomics 7.6.1 Reverse phase protein microarrays (RPPA) 7.7 Metabolomics 7.7.1 Different methods for studying metabolomics References CHAPTER 8 Drug informatics 8.1 Introduction 8.2 Computational drug designing and discovery 8.3 Structure based drug designing 8.3.1 Homology modeling 8.3.2 Molecular docking 8.3.3 Molecular simulation 8.4 Ligand-based drug designing 8.4.1 Pharmacophore modeling 8.5 ADMET 8.5.1 Adsorption 8.5.2 Distribution 8.5.3 Metabolism 8.5.4 Excretion 8.5.5 Toxicity 8.6 Drug repurposing References CHAPTER 9 A machine learning approach to bioinformatics 9.1 Introduction to machine learning? 9.2 Types of machine learning systems 9.2.1 Supervised learning 9.2.2 The below are the most commonly used supervised algorithms 9.2.3 Logistic regression 9.2.4 K-nearest neighbor 9.2.5 Decision trees 9.2.6 Support vector machines 9.2.7 Neural networks 9.2.8 Neural networks architecture 9.2.9 Convolutional neural network 9.2.10 Unsupervised learning 9.2.11 K-means clustering 9.2.12 Reinforcement learning 9.3 Evaluation of machine learning models 9.3.1 Accuracy 9.3.2 Cross-validation 9.3.3 Testing and validating 9.4 Optimization of models 9.4.1 Parameter searching 9.4.2 Ensemble methods 9.5 Main challenges of machine learning 9.5.1 Insufficient quantity of training data 9.5.2 Non-representative training data 9.5.3 Quality of data 9.5.4 Irrelevant features 9.5.5 Overfitting or underfitting on training data References CHAPTER 10 Systems and network biology 10.1 Introduction 10.2 Network theory 10.3 Graph theory 10.4 Features of biological networks 10.4.1 The various types of network edges 10.4.2 Network measures 10.4.3 Network models 10.5 Types of biological networks 10.5.1 Cell signaling networks 10.5.2 Gene/transcription regulation networks 10.5.3 Genetic interaction networks 10.5.4 Metabolic networks 10.5.5 Proteineprotein interaction networks 10.6 Sources of data for biological networks 10.7 Gene ontology for network analysis 10.8 Analysis of biological networks and interactomes 10.9 Interaction network construction using a gene list 10.10 Data analysis tools 10.10.1 The InnateDB 10.10.2 Visualization and download of networks 10.10.3 Enrichr 10.10.4 Babelomics 5 10.11 Network visualization tools 10.11.1 Cytoscape 10.11.2 NAViGaTOR 10.11.3 VisANT 10.11.4 CellDesigner 10.11.5 Pathway Studio 10.11.6 Gephi 10.12 Important properties to be inferred from networks 10.12.1 Hubs 10.12.2 Bottlenecks 10.12.3 Modules 10.12.4 Bioinformatics tools to detect modules, bottlenecks and hubs References CHAPTER 11 Bioinformatics workflow management systems 11.1 Introduction to workflow management systems 11.2 Galaxy 11.3 Gene pattern 11.4 KNIME: The Konstanz information miner 11.5 LINCS tools 11.5.1 The program’s overall goal 11.5.2 Test performed under LINCS 11.6 Anduril bioinformatics and image analysis 11.6.1 Anduril image analysis: ANIMA 11.7 NextFlow References CHAPTER 12 Data handling using Python 12.1 Introduction 12.2 Datatypes and operators 12.2.1 Datatypes 12.2.2 Operators 12.3 Variables 12.4 Strings 12.4.1 String indexing 12.4.2 Operations on strings 12.4.3 Methods in strings 12.5 Python lists and tuples 12.5.1 Accessing values in list 12.5.2 Methods with lists 12.5.3 Tuples 12.6 Dictionary in Python 12.7 Conditional statements 12.7.1 Logical operators 12.7.2 If and else statements 12.8 Loops in Python 12.8.1 While loop 12.8.2 “For” loop 12.8.3 Breaking a loop 12.9 File handling in Python 12.9.1 Specify file mode 12.10 Importing functions 12.10.1 Running a t-test in Python 12.10.2 Make a simple scatterplot in matplotlib 12.10.3 Running a simple linear regression in Python 12.11 Data handling References Index
• Publisher Details: Amsterdam : Academic Press
• Publication Date: 2023
• Extent: 1 online resource (200 pages)
• Subjects: 570.285; Bioinformatics; Computational biology; Systems biology
• Languages: English
• ISBNs: 9780443152511
• Related ISBNs: 9780443152504
• Notes: Note: Description based on CIP data; resource not viewed.
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