Aims, methods, data sources and applications bioinformatics

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1 12. 1of Aims, methods, data sources and applications bioinformatics Bioinformatics can be defined in many ways. Broadly, we can think of it as a computer-based discipline applied to the life. More specifically, it is a science that uses computer technology to capture, store, represent, retrieve and analyse biological information and/or to simulate biological systems. As such, as well as and aspects of computer science, it involves mathematics, statistics, software engineering, genetics, molecular evolution, systems, and so on. This topic guide will look at the aims, applications and methods employed in bioinformatics as well as the impact that this area has on the biological. On successful completion of this topic you will: understand the aims, methods, data sources and applications of bioinformatics (LO1). To achieve a Pass in this unit you need to show that you can: explain the aims and applications of bioinformatics (1.1) discuss methods employed in informatics (1.2) discuss the impact of bioinformatics in the biological (1.3). 1

2 1 Introduction to bioinformatics Figure : The many facets of bioinformatics. Pharmacokinetics Highthroughput chemistry Pharmacogenomics Metabolomics Highthroughput Engineering Computer science Proteomics Genomics Bioinformatics Biomedical Biomedical literature Genetics Evolutionary Systems Biological Clinical Mathematics Statistics Before looking at the aims, methods, data sources and applications of bioinformatics, it is important to appreciate that bioinformatics involves a complex interplay between many different disciplines, which fit together like pieces of a puzzle and build more complete holistic pictures of. As the field is so wide-ranging (see Figure ), it is impossible to cover all areas fully, so this topic guide, and the subsequent topic guides in this unit, will focus on understanding the more general aims and applications of bioinformatics. 2 Aims and applications of bioinformatics Sequence: A biomolecular sequence is the linear sequence of building blocks (such as amino acids or nucleotide bases) that are joined together to create biological polymers, such as proteins or DNA. Whole-genome sequencing: The process of determining the complete DNA sequence of an organism s genome. Personalised medicine: A customised health-care approach tailored to individual patients (for example, in which therapeutic decisions take into account an individual s genetic profile). The origins of bioinformatics can be traced to pioneering scientists who were trying to understand the stories hidden in biomolecular sequences. For example, they wanted to know how amino acid sequences create protein folds, what their sequences reveal about protein evolutionary histories, and what they tell us about protein and gene functions. A major ambition was to understand the roles of genes and proteins in disease processes, and to discover how such information could be used to design better drugs. A breakthrough occurred with whole-genome sequencing and eventual elucidation of the human genome. Bioinformatics then became more ambitious, aiming to revolutionise medicine by making sequencing a diagnostic tool. The goal was to develop new approaches to eradicate diseases like cancer, and to pave the way towards personalised medicine where individuals genetic blueprints are used to optimise their own drug therapies, by targeting specific genes and minimising toxic side effects. The quest became to integrate all such biomedical data, to be able to model biochemical pathways all the way up to complete biological systems to 2

3 understand not only how individual cells work, but also how complete assemblies of cells work in whole organs, including the brain. Applications of bioinformatics are now many and varied they also extend to: development of vaccines and herbicides genetic variation and biodiversity data management genetic testing genetic engineering of crops and livestock. In short, wherever biological, medical, veterinary, agricultural, and even food safety research, etc., make use of information in genomes, genes, proteins, cells and systems, bioinformatics has become vital for managing, analysing and, ultimately, helping us to understand the data. Activity Read more about the goals of bioinformatics in this short history: What do you think have been the main achievements of bioinformatics to date? Watch the video to see how the use of computers and information technology is transforming the future of medicine. Genomics: The systematic sequencing, functional characterisation and study of organisms complete genomes. Proteomics: The large-scale experimental analysis of the complete set of proteins within an organism or system. Transcriptomics: The large-scale analysis of patterns of transcription and how these are influenced by development, disease, environmental factors, etc. 3 Sources of biological data and the need for systematic data storage The collection of biological data (protein and DNA sequences, protein structures, etc.) was initially very slow indeed: a single protein sequence, the first protein structure, then the first nucleotide sequences. This was partly because the methods were manual and partly also because sequence and structure determination had never been done before. This was ground-breaking science. The revolution came with robotisation of sequencing procedures. This allowed whole genomes to be sequenced and all the proteins they encode (their proteomes ) to be deduced. Lab automation thus brought in a system-wide culture of biomedical data collection, and gave rise to the omics revolution (to genomics, proteomics, transcriptomics, etc.). The resultant overwhelming data deluge demanded systematic ways of storing the accumulating information so that it could be analysed and re-used by other researchers. This need led to the creation of the first databases. 3

4 Wet lab: A laboratory designed to allow experiments on biological materials to be conducted in liquid and/or volatile gaseous phases. The term wet lab distinguishes experiments performed at the bench from so-called dry informatics experiments performed by computers, in silico. High-throughput technologies: Scaled-up lab techniques, usually made possible by increased automation using robots. 4 The impact of bioinformatics on 21st century Computers have profoundly changed how is conducted it is no longer a purely wet-lab or bench science. Computers are being used to capture more information more quickly, to store and analyse data more efficiently, and even to transform how biologists can interact with published articles, by breathing life into data described in the biomedical literature. Watch the SciVee video to discover how bioinformatics data are being used to animate static PDF files. As the outputs of high-throughput technologies have increased, so too has the level of cooperation needed to manage the data. For example, many genome projects are overseen by multi-disciplinary teams that work together across the globe, and the project to create an encyclopaedia of DNA elements from the human genome (ENCODE) involved international collaboration between hundreds of scientists. Managing such global teams is logistically complicated, but imperative if science is to reap the benefits of big science and ultimately teach us more about our genomes. ENCODE, for example, revealed that large regions of our genomes, once called dark matter or junk, play pivotal roles in regulating how our genes behave. This discovery, impossible without bioinformatics, has opened the door to a better understanding of disease processes, creating new opportunities for the pharmaceutical industry to design better, more efficacious drugs in future. Find out more about how ENCODE relates to you (watch this video: and to all of us, and discover in more detail what ENCODE has taught us about our genomes from this video: The volume and richness of worldwide data collection permits more informed analyses, allowing questions to be asked at the level of entire systems rather than individual molecules. Understanding complete systems, and their perturbations, promises to transform biomedical science, and is already allowing researchers to study the effects of therapeutic interventions from single genes up to the level of whole organisms. Bioinformatics is thus playing a key role in this heroic age of genome sequencing, and firmly taking its place in the new. Activity Read the article describing the grand challenges in bioinformatics and computational. What event was considered to mark Day 1 of the Heroic Age of the Era of Complete Genomes? 4

5 Watch the video to learn more about the role of bioinformatics in the new. Further reading Attwood, T. and Parry-Smith, D. (1999) Introduction to Bioinformatics, Prentice Hall. Chapter 1 contains more information about the origins of bioinformatics. Higgs, P. and Attwood, T. (2005) Bioinformatics and Molecular Evolution, Wiley-Blackwell. Chapter 1 contains additional information on the biological data revolution. Find out more about the history of bioinformatics in this short feature: Find out more about the grand challenges in bioinformatics and computational from the article Checklist At the end of this topic guide you should be familiar with the following ideas about bioinformatics: bioinformatics aims to help understand biological phenomena, processes and systems using computers vast quantities of data emanate from system-wide studies (of whole genomes, proteomes, transcriptomes, etc.) the volume and complexity of data demand systematic means of storage to allow the information to be interrogated and analysed efficiently bioinformatics approaches provide the computational means to do this bioinformatics is transforming biological, coupling dry informatics methods (sometimes called computational or in silico ) with wet-lab science (or bench ), allowing more holistic, global perspectives on biological and biomedical data capture and analysis global collaborative projects are using bioinformatics methods to learn more about our genomes, to uncover defects in biological processes that lead to disease, to improve drug discovery and, ultimately, to make contributions to personalised medicine, to veterinary medicine, to agriculture, and to many other life science fields and industries. Acknowledgements The publisher would like to thank the following for their kind permission to reproduce their photographs: PhotoDisc: Lawrence Lawry All other images Pearson Education Every effort has been made to trace the copyright holders and we apologise in advance for any unintentional omissions. We would be pleased to insert the appropriate acknowledgement in any subsequent edition of this publication. 5