All About Bioinformatics: From Beginner to Expert

Description

All About Bioinformatics: From Beginner to Expert provides readers with an overview of the fundamentals and advances in the field of bioinformatics, as well as some future directions. Each chapter is didactically organized and includes introduction, applications, tools, and future directions to cover the topics thoroughly. The book covers both traditional topics such as biological databases, algorithms, genetic variations, static methods, and structural bioinformatics, as well as contemporary advanced topics such as high-throughput technologies, drug informatics, system and network biology, and Machine Learning. It is a valuable resource for researchers and graduate students who are interested to learn more about bioinformatics to apply in their research work. A Machine Learning algorithm is a statistical computation method used in software to detect hidden patterns that are not obvious in a dataset and make reliable statistical predictions of similar new data. Machine Learning techniques attempt to find a pattern in a particular dataset; using these learned patterns, a similar pattern in a new dataset is identified. Machine Learning processes are somewhat close to statistical modeling and data collection. They look into the data to find trends and accordingly learn the pattern or parameters. We usually know about Machine Learning through social media connection suggestions, online shopping product recommendations, spam filters in our email inboxes etc. Recent advances in the life and Computer Sciences have permitted the use of computational techniques to address critical problems. Python is a free general-purpose programming language that is simple to learn and apply to a wide range of computational tasks. Python may be used to handle a variety of difficulties that research labs confront on a daily basis. Using computers and an appropriate programming language, it is possible to execute tasks such as data manipulation, biological data retrieval and parsing, automation, and simulation of biological problems efficiently. The goal of this chapter is to give a high-level look at the Python programming language, showing how it works and what it can do. The main data structures and flow control statements are shown. Following these fundamental concepts, topics such as file access, functions, and modules are addressed in greater depth. This chapter introduces the reader to Anaconda, a distribution software that includes libraries, packages, and editors (such as Jupyter notebooks) essential for Python development. The datatypes, regularly used operators in each datatype, and a range of helpful built-in data structures, such as strings, lists, tuples, and dictionaries, are introduced. Iterations are also described using terms such as while and for loops. Finally, objects and modules were introduced, and their use in data processing was described. At the end, there are references to more advanced Python topics. - Presents a holistic learning experience, beginning with an introduction to bioinformatics to recent advancements in the field - Discusses bioinformatics as a practice rather than in theory focusing on more application-oriented topics as high-throughput technologies, system and network biology, and workflow management systems - Encompasses chapters on statistics and Machine Learning to assist readers in deciphering trends and patterns in biological data Front Cover All About Bioinformatics All About Bioinformatics: From Beginner to Expert Copyright Contents 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 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 databases 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 Genomics and proteomics databases 2.8.1 The search engines for literature 2.9 Miscellaneous databases 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 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 3.3.2.1 Median for the grouped data 3.3.2.2 Median for the ungrouped data 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 3.5.4.1 Comprehending the source of variability for analysis 3.6 Different types of distributions and their significance 3.6.1 Probability distributions 3.6.2 Continuous probability function 3.6.2.1 Normal distribution 3.6.2.2 Continuous uniform distribution 3.6.2.3 Log-normal distribution 3.6.2.4 Exponential distribution 3.6.3 Discrete probability function 3.6.3.1 Binomial distribution 3.6.3.2 Bernoulli's distribution 3.6.3.3 Poisson distribution 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 3.8.1.1 Addition law of probability 3.8.1.2 Multiplication law of probability 3.8.1.3 Binomial law of probability distribution 3.8.1.4 Probability (chances) from shape of normal distribution or normal curve 3.8.1.5 Probability of calculated values from tables 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 4 - Algorithms in computational biology 4.1 Sequence alignment 4.1.1 Local alignment 4.1.2 Global alignment 4.1.3 Gap penalty 4.1.3.1 Gaps and gap penalties 4.2 Pair-wise alignment 4.3 Dot-matrix method 4.4 Dynamic programming 4.4.1 Needleman-Wunsch 4.4.1.1 Step 1: Initialization table “T” 4.4.1.2 Step 2: Filling the matrix 4.4.1.3 Step 3: Traceback 4.4.2 Smith Waterman algorithm 4.4.2.1 Limitations 4.5 Scoring matrices 4.5.1 Scoring matrices for amino acids 4.5.2 PAM (point accepted mutation) 4.5.2.1 PAM score 4.5.2.2 Example 4.5.3 BLOcks SUbstitution matrix (BLOSUM) 4.5.3.1 BLOSUM62 4.5.3.2 BLOSUM score 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 4.7.5.1 Clustal family 4.7.5.2 DIAlign 4.7.5.3 Tree-based consistency objective function for alignment evaluation (T-coffee) 4.7.5.4 FAlign 4.7.6 Representation and structural inference 4.8 Phylogenetics 4.8.1 Molecular phylogenetics 4.8.2 Phylogenetics trees 4.8.3 Properties 4.8.3.1 Phylogenetic trees and networks 4.8.4 Building methods 4.8.5 Distance matrix method 4.8.5.1 UPGMA 4.8.5.2 Neighbor-joining 4.8.5.3 Fitch Margoliash method 4.8.5.4 Maximum parsimony 4.8.5.5 Maximum likelihood 4.8.6 Bayesian inference References 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 5.7.2.1 Human genome sequencing has been completed 5.7.2.2 Molecular characterization of disease 5.7.2.3 Search for biomarkers of drug response 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 5.8.1.1 Pharmacogenomics knowledgebase (PharmGKB) 5.8.1.2 DrugBank 5.8.1.3 CPIC References 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 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 7.3.2.1 Application of DNA microarray 7.3.3 DNA sequencing 7.3.3.1 Clone-by-clone process 7.3.3.2 Whole-genome shotgun process 7.3.3.3 Assembly of sequencing reads 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 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 8.3.2.1 Sampling algorithm 8.3.2.2 Scoring functions 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 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 9.2.2.1 Linear regression 9.2.3 Logistic regression 9.2.4 K-nearest neighbor 9.2.5 Decision trees 9.2.6 Support vector machines 9.2.6.1 Kernel trick 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 Receiver Operating Characteristic (ROC) Curvature 9.3.3 Cross-validation 9.3.4 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 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 Protein–protein 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 10.10.3.1 PANTHER 10.10.3.2 GESA 10.10.3.3 DAVID 10.10.3.4 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 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 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 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Back Cover