Author Profile

Dr. Maysson Ibrahim holds a Ph.D in Bioinformatics and BEng in Software Engineering and Information Systems. After completing her PhD in 2013 at the Buckingham Institute for Translational Medicine (BITM), University of Buckingham, she joined the BITM as a Postdoctoral Research Fellow in Bioinformatics for three years. Her key research interest was focusing on biological data analysis and developing algorithms for pathway enrichment and biomarkers Identification using gene expression data. During and after her PhD (2011-2014), she was teaching "Introduction to Statistics" module at the School of Computing, University of Buckingham. Maysson moved to the Nuffield Department of Population Health, Big Data Institute, University of Oxford in 2016 as a Research Fellow in Bioinformatics and then as a Senior Research Fellow in 2017. Since then, she is leading the Bioinformatics work in a multidisciplinary team where her key research focuses on analysing genetic data from large scale Biobanks (e.g. UK Biobank) and clinical trial studies such as REVEAL, SHARP, and THRIVE to identify and understand genetic determinants of complex diseases such as cardiovascular disease. Her research includes developing and using analytical pipelines that incorporate machine learning algorithms to enhance the analysis and provide better understanding of big data. Maysson joined the School of Computing at Buckingham in 2020 as a part-time Lecturer in Computer Science along with her research role at Oxford. She is the module lead for the Data Exploration and Visualisation, and Systems and Tools for Data Science in the MSc Applied Data Science programme.

About The Book

Bioinformatics is an interdisciplinary field that uses computer science, mathematics, and biology to analyze and interpret biological data, particularly DNA and protein sequences, to understand biological processes. It has applications in medicine, pharmacology, genetics, agriculture, and more, facilitating advancements in drug discovery, personalized medicine, crop improvement, and understanding evolutionary relationships. Bioinformatics involves the collection, storage, and management of vast amounts of biological data, including DNA and protein sequences, gene expression data, and protein structures. It utilizes computational tools and algorithms to analyse biological data, identify patterns, and extract meaningful insights. A key area of bioinformatics involves analyzing DNA and protein sequences to understand gene functions, identify evolutionary relationships, and predict protein structures. Bioinformatics relies on the creation and maintenance of biological databases that store and organize vast amounts of data. Bioinformatics employs various algorithms and computational models to simulate biological processes, predict protein structures, and model biological systems. Bioinformatics plays a vital role in identifying potential drug targets, designing and testing new drugs, and optimizing drug delivery. By analyzing an individual's genetic information, bioinformatics helps in tailoring treatments to specific patient profiles, optimizing drug dosages, and predicting disease susceptibility. Bioinformatics is essential for analyzing and interpreting the vast datasets generated by genomics (the study of genomes) and proteomics (the study of proteins). Bioinformatics tools are used to compare genomes from different species, reconstruct evolutionary relationships, and understand the history of life on Earth.

Table of Contents

Preface............................................................................................................ v
Chapter 1. Introduction to Bioinformatics........................................................... 1
ƒ History of Bioinformatics........................................................................... 3
ƒ Goals of Bioinformatics..............................................................................6
ƒ Importance in Modern Biology.................................................................. 7
ƒ Biological Databases................................................................................. 11
ƒ Sequence Analysis Basics..........................................................................14
ƒ Computational Biology .............................................................................19
ƒ Applications in Healthcare....................................................................... 28
Chapter 2. Biological Databases......................................................................... 31
ƒ GenBank....................................................................................................34
ƒ EMBL-EBI ..................................................................................................38
ƒ DNA Data Bank of Japan (DDBJ).............................................................40
ƒ UniProt.......................................................................................................41
ƒ Protein Data Bank (PDB)..........................................................................45
ƒ KEGG Database.........................................................................................47
ƒ Gene Ontology (GO)..................................................................................51
Chapter 3. Sequence Alignment and Analysis.................................................. 56
ƒ DNA and Protein Sequences....................................................................58
ƒ Pairwise Sequence Alignment.................................................................69
ƒ Multiple Sequence Alignment (MSA)...................................................... 73
ƒ BLAST (Basic Local Alignment Search Tool)............................................80
ƒ FASTA Format and Tools..........................................................................82
ƒ Clustal Omega...........................................................................................83
Contents
Chapter 4. Genomics and Genome Annotation................................................ 87
ƒ Human Genome Project...........................................................................90
ƒ Genome Assembly.................................................................................. 100
ƒ Gene Prediction.......................................................................................103
ƒ Comparative Genomics...........................................................................107
ƒ SNPs and Variations................................................................................ 110
Chapter 5. Transcriptomics and Gene Expression............................................115
ƒ Microarray Technology........................................................................... 118
ƒ RNA-Seq................................................................................................... 121
ƒ Transcriptome Assembly......................................................................... 127
ƒ Gene Expression......................................................................................130
ƒ cDNA and ESTs.........................................................................................133
ƒ Functional Enrichment Analysis..............................................................138
Chapter 6. Proteomics and Protein Informatics.............................................. 143
ƒ Protein Structure ................................................................................... 146
ƒ Protein Structure Prediction.................................................................. 148
ƒ Protein–Protein Interaction....................................................................156
ƒ Protein Mass Spectrometry ...................................................................159
ƒ Swiss-Model.............................................................................................162
ƒ Protein Folding and Misfolding............................................................. 164
Chapter 7. Phylogenetics and Evolutionary Bioinformatics........................... 170
ƒ Phylogenetic Tree Construction.............................................................173
ƒ Molecular Evolution................................................................................175
ƒ Distance-Based Methods (UPGMA, Neighbor-Joining)........................ 180
ƒ Character-Based Methods......................................................................183
ƒ MEGA Software....................................................................................... 191
ƒ Molecular Clocks.....................................................................................195
Chapter 8. Structural Bioinformatics...............................................................198
ƒ Levels of Protein Structure....................................................................200
ƒ Molecular Visualization Tools (PyMOL, Chimera).................................204
ƒ Homology Modeling...............................................................................208
ƒ Molecular Docking..................................................................................218
ƒ Protein–Ligand Interactions................................................................... 221
Chapter 9. Bioinformatics Algorithms and Tools............................................225
ƒ Hidden Markov Models (HMMs)........................................................... 228
ƒ Dynamic Programming...........................................................................231
ƒ Machine Learning in Bioinformatics...................................................... 235
ƒ Neural Networks and Deep Learning....................................................244
ƒ Next-Generation Sequencing (NGS) ..................................................... 247
ƒ Sequence Assembly Algorithms............................................................ 252
Chapter 10. Applications and Future Trends....................................................256
ƒ Bioinformatics in Drug Discovery.......................................................... 259
ƒ Personalized Medicine........................................................................... 265
ƒ Bioinformatics in Agriculture.................................................................268
ƒ Systems Biology......................................................................................271
ƒ Synthetic Biology and CRISPR............................................................... 274
ƒ Big Data in Bioinformatics .....................................................................280
Bibliography................................................................................................283
Index............................................................................................................ 291