David A Liberles (ed.)
- Published in print:
- 2007
- Published Online:
- September 2008
- ISBN:
- 9780199299188
- eISBN:
- 9780191714979
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199299188.001.0001
- Subject:
- Biology, Evolutionary Biology / Genetics
Ancestral sequence reconstruction is a technique of growing importance in molecular evolutionary biology and comparative genomics. As a powerful tool for testing evolutionary and ecological ...
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Ancestral sequence reconstruction is a technique of growing importance in molecular evolutionary biology and comparative genomics. As a powerful tool for testing evolutionary and ecological hypotheses, as well as uncovering the link between sequence and molecular phenotype, there are potential applications in almost all fields of applied molecular biology. This book starts with a historical overview of the field, before discussing the potential applications in drug discovery and the pharmaceutical industry. This is followed by a section on computational methodology, which provides a detailed discussion of the available methods for reconstructing ancestral sequences (including their advantages, disadvantages, and potential pitfalls). Purely computational applications of the technique are then covered, including whole proteome reconstruction. Further chapters provide a detailed discussion on taking computationally reconstructed sequences and synthesizing them in the laboratory. The book concludes with a description of the scientific questions where experimental ancestral sequence reconstruction has been utilized to provide insights and inform future research.Less
Ancestral sequence reconstruction is a technique of growing importance in molecular evolutionary biology and comparative genomics. As a powerful tool for testing evolutionary and ecological hypotheses, as well as uncovering the link between sequence and molecular phenotype, there are potential applications in almost all fields of applied molecular biology. This book starts with a historical overview of the field, before discussing the potential applications in drug discovery and the pharmaceutical industry. This is followed by a section on computational methodology, which provides a detailed discussion of the available methods for reconstructing ancestral sequences (including their advantages, disadvantages, and potential pitfalls). Purely computational applications of the technique are then covered, including whole proteome reconstruction. Further chapters provide a detailed discussion on taking computationally reconstructed sequences and synthesizing them in the laboratory. The book concludes with a description of the scientific questions where experimental ancestral sequence reconstruction has been utilized to provide insights and inform future research.
Scott Zeger, Peter Diggle, and Kung-Yee Liang
- Published in print:
- 2005
- Published Online:
- September 2007
- ISBN:
- 9780198566540
- eISBN:
- 9780191718038
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198566540.003.0009
- Subject:
- Mathematics, Probability / Statistics
This chapter reviews the biomedical and public health developments that will influence biostatistical research and practice in the near future, such as advances in molecular biology, and measuring ...
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This chapter reviews the biomedical and public health developments that will influence biostatistical research and practice in the near future, such as advances in molecular biology, and measuring DNA sequences and gene and protein expression levels. It is argued that the success of biostatistics will derive largely from a model-based approach, which uses and applies the principle of conditioning. Statistical models and inferences that are central to this model-based approach are described and contrasted with computationally-intensive strategies and a design-based approach. Increasingly complex models, different sources of uncertainty, and clustered observational units are viewed as future challenges for the model-based approach. Causal inference and statistical computing are discussed as topics believed to be central to biostatistics in the near future.Less
This chapter reviews the biomedical and public health developments that will influence biostatistical research and practice in the near future, such as advances in molecular biology, and measuring DNA sequences and gene and protein expression levels. It is argued that the success of biostatistics will derive largely from a model-based approach, which uses and applies the principle of conditioning. Statistical models and inferences that are central to this model-based approach are described and contrasted with computationally-intensive strategies and a design-based approach. Increasingly complex models, different sources of uncertainty, and clustered observational units are viewed as future challenges for the model-based approach. Causal inference and statistical computing are discussed as topics believed to be central to biostatistics in the near future.
Marta Gwinn and Wei Yu
- Published in print:
- 2009
- Published Online:
- May 2010
- ISBN:
- 9780195398441
- eISBN:
- 9780199776023
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195398441.003.0004
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
This chapter presents an overview of evolving methods for tracking and compiling information on genetic factors in disease. It discusses bioinformatics, the Human Genome Epidemiology Network ...
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This chapter presents an overview of evolving methods for tracking and compiling information on genetic factors in disease. It discusses bioinformatics, the Human Genome Epidemiology Network (HuGENet), and genomic databases. Building the knowledge base in human genome epidemiology involves organizing, sharing, mining, interpreting, and evaluating the results of genomic research from a population perspective. This effort faces many technical, scientific, and social challenges, which can be met only by unprecedented levels of interaction across multiple levels of the research enterprise, and by cooperation among individual scientists, research groups, institutions, and agencies.Less
This chapter presents an overview of evolving methods for tracking and compiling information on genetic factors in disease. It discusses bioinformatics, the Human Genome Epidemiology Network (HuGENet), and genomic databases. Building the knowledge base in human genome epidemiology involves organizing, sharing, mining, interpreting, and evaluating the results of genomic research from a population perspective. This effort faces many technical, scientific, and social challenges, which can be met only by unprecedented levels of interaction across multiple levels of the research enterprise, and by cooperation among individual scientists, research groups, institutions, and agencies.
Xun Gu
- Published in print:
- 2010
- Published Online:
- January 2011
- ISBN:
- 9780199213269
- eISBN:
- 9780191594762
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199213269.001.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies
Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under ...
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Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under changing environments. It stems from an integration of high throughput data from functional genomics, statistical modelling and bioinformatics, and the procedure of phylogeny-based analysis. This book summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution. The book reviews the recent developments in methodology from an evolutionary perspective of genome function, and incorporates substantial examples from high throughput data in model organisms. In addition to phylogeny-based functional analysis of DNA sequences, the book includes discussion on how new types of functional genomic data (e.g., microarray) can provide exciting new insights into the evolution of genome function, which can lead in turn to an understanding of the emergence of genome complexity during evolution.Less
Evolutionary genomics is a relatively new research field with the ultimate goal of understanding the underlying evolutionary and genetic mechanisms for the emergence of genome complexity under changing environments. It stems from an integration of high throughput data from functional genomics, statistical modelling and bioinformatics, and the procedure of phylogeny-based analysis. This book summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution. The book reviews the recent developments in methodology from an evolutionary perspective of genome function, and incorporates substantial examples from high throughput data in model organisms. In addition to phylogeny-based functional analysis of DNA sequences, the book includes discussion on how new types of functional genomic data (e.g., microarray) can provide exciting new insights into the evolution of genome function, which can lead in turn to an understanding of the emergence of genome complexity during evolution.
Masashi Sugiyama and Motoaki Kawanabe
- Published in print:
- 2012
- Published Online:
- September 2013
- ISBN:
- 9780262017091
- eISBN:
- 9780262301220
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262017091.001.0001
- Subject:
- Computer Science, Machine Learning
As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the ...
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As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning’s greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the book discusses topics that include learning under covariate shift, model selection, importance estimation, and active learning. It describes such real-world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images.Less
As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning’s greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the book discusses topics that include learning under covariate shift, model selection, importance estimation, and active learning. It describes such real-world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images.
Wolfgang Maret, Joseph A. Caruso, Christopher H. Contag, David P. Giedroc, Peter-Leon Hagedoorn, Andreas Matusch, Eric P. Skaar, and Richard B. Thompson
- Published in print:
- 2015
- Published Online:
- May 2016
- ISBN:
- 9780262029193
- eISBN:
- 9780262327619
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262029193.003.0020
- Subject:
- Public Health and Epidemiology, Public Health
An extensive summary is provided on the methods and methodology available to analyze and image total metals in biological systems as well as to assess their speciation, in particular in ...
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An extensive summary is provided on the methods and methodology available to analyze and image total metals in biological systems as well as to assess their speciation, in particular in metalloproteomes and as free metal ion concentrations. Discussion focuses on instrumental methods for analysis and separation and how they are complemented by genetic and bioinformatics approaches. The treatment of methods follows increased complexity and experimental challenges: from lysates and fluids, to cells and tissues, to living cells and animals, and finally to the prospects of applying extent technology to investigations in humans. Presently, method sensitivity is not sufficient for analysis of metals in the pathogen in vivo. Future analytical needs are presented to address metal ions in host–pathogen interactions for diagnosis and treatment of infectious disease. The material is presented in the context of both redistribution of metals in the host and known mechanisms of warfare to acquire the transition metal ions that are essential for growth and survival of either host or pathogen.Less
An extensive summary is provided on the methods and methodology available to analyze and image total metals in biological systems as well as to assess their speciation, in particular in metalloproteomes and as free metal ion concentrations. Discussion focuses on instrumental methods for analysis and separation and how they are complemented by genetic and bioinformatics approaches. The treatment of methods follows increased complexity and experimental challenges: from lysates and fluids, to cells and tissues, to living cells and animals, and finally to the prospects of applying extent technology to investigations in humans. Presently, method sensitivity is not sufficient for analysis of metals in the pathogen in vivo. Future analytical needs are presented to address metal ions in host–pathogen interactions for diagnosis and treatment of infectious disease. The material is presented in the context of both redistribution of metals in the host and known mechanisms of warfare to acquire the transition metal ions that are essential for growth and survival of either host or pathogen.
Dennis Sherwood and Jon Cooper
- Published in print:
- 2010
- Published Online:
- January 2011
- ISBN:
- 9780199559046
- eISBN:
- 9780191595028
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199559046.003.0013
- Subject:
- Physics, Crystallography: Physics
This chapter describes a range of methods for solving the phase problem when a structure that is similar to the one being analysed has already been determined. These methods generally rely on ...
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This chapter describes a range of methods for solving the phase problem when a structure that is similar to the one being analysed has already been determined. These methods generally rely on comparing the Patterson function of the unknown structure (calculated from its diffraction intensities) with that calculated from the known structure. For computational expedience, the comparison of the two Patterson functions is done in two stages known as the rotation function and the translation function. In the rotation function, the Patterson of the search model is rotated through various angles and compared with the target Patterson — the orientation which gives the highest correlation with the target Patterson indicates a likely solution. The correct position of the search model within the target unit cell can then be determined essentially by calculating the inter-molecular vectors at a series of trial positions and comparing them with the target Patterson — this is known as the translation function. The variables affecting these calculations are discussed and methods for verifying the results are described, as are recent developments in bioinformatics which can be exploited to optimise the search model.Less
This chapter describes a range of methods for solving the phase problem when a structure that is similar to the one being analysed has already been determined. These methods generally rely on comparing the Patterson function of the unknown structure (calculated from its diffraction intensities) with that calculated from the known structure. For computational expedience, the comparison of the two Patterson functions is done in two stages known as the rotation function and the translation function. In the rotation function, the Patterson of the search model is rotated through various angles and compared with the target Patterson — the orientation which gives the highest correlation with the target Patterson indicates a likely solution. The correct position of the search model within the target unit cell can then be determined essentially by calculating the inter-molecular vectors at a series of trial positions and comparing them with the target Patterson — this is known as the translation function. The variables affecting these calculations are discussed and methods for verifying the results are described, as are recent developments in bioinformatics which can be exploited to optimise the search model.
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien (eds)
- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262033589
- eISBN:
- 9780262255899
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262033589.001.0001
- Subject:
- Computer Science, Machine Learning
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in ...
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In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. It first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms which perform two-step learning. It then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of benchmark experiments. Finally, the book looks at interesting directions for SSL research. It closes with a discussion of the relationship between semi-supervised learning and transduction.Less
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. It first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms which perform two-step learning. It then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of benchmark experiments. Finally, the book looks at interesting directions for SSL research. It closes with a discussion of the relationship between semi-supervised learning and transduction.
Ernest Lawrence Rossi
- Published in print:
- 2005
- Published Online:
- November 2011
- ISBN:
- 9780198529415
- eISBN:
- 9780191730344
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198529415.003.0007
- Subject:
- Palliative Care, Patient Care and End-of-Life Decision Making, Pain Management and Palliative Pharmacology
The role of integrative approaches to supportive care in urology is as controversial today as at any time in the history of medicine. At present, however, people are witnessing the emergence of a ...
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The role of integrative approaches to supportive care in urology is as controversial today as at any time in the history of medicine. At present, however, people are witnessing the emergence of a new, unified scientific foundation that brings together the best of modern molecular medicine with the bioinformatics of integrative medicine. This chapter outlines a new bioinformatics model of integrative medicine that complements and is consistent with the molecular dynamics of modern medicine on the genomic, proteomic, physiological, and psychological levels. Much of integrative medicine remains controversial because the typical outcome research purporting to validate the top-down approaches of integrative medicine do not include the entire four-stage bioinformatics cycle of modern molecular medicine. Clinical-experimental research programs that document the efficacy of integrative medicine with the currently emerging standards of validation via genomic and proteomic microarray technology are now needed.Less
The role of integrative approaches to supportive care in urology is as controversial today as at any time in the history of medicine. At present, however, people are witnessing the emergence of a new, unified scientific foundation that brings together the best of modern molecular medicine with the bioinformatics of integrative medicine. This chapter outlines a new bioinformatics model of integrative medicine that complements and is consistent with the molecular dynamics of modern medicine on the genomic, proteomic, physiological, and psychological levels. Much of integrative medicine remains controversial because the typical outcome research purporting to validate the top-down approaches of integrative medicine do not include the entire four-stage bioinformatics cycle of modern molecular medicine. Clinical-experimental research programs that document the efficacy of integrative medicine with the currently emerging standards of validation via genomic and proteomic microarray technology are now needed.
Hallam Stevens
- Published in print:
- 2013
- Published Online:
- May 2014
- ISBN:
- 9780226080178
- eISBN:
- 9780226080345
- Item type:
- book
- Publisher:
- University of Chicago Press
- DOI:
- 10.7208/chicago/9780226080345.001.0001
- Subject:
- History, History of Science, Technology, and Medicine
During the last thirty years, computers have come to play an increasingly important role in biological work. Especially in the new ‘omic’ disciplines, biologists are as likely to be found in front of ...
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During the last thirty years, computers have come to play an increasingly important role in biological work. Especially in the new ‘omic’ disciplines, biologists are as likely to be found in front of a computer screen as they are working at a laboratory bench. Life out of sequence is an account of these transformations: beginning in the 1960s and continuing to the present, it draws on archival research, participant observation in laboratories, and interviews to show how and why the life sciences have been automated, computerized, and digitized. The advent of bioinformatics meant not merely using computers to solve the same old problems. Rather, computers entailed fundamental shifts in training, funding, career paths, work practices, and knowledge production. Computers were originally designed as tools for simulation and data management with applications to military and physics problems. Bringing them from physics into biology meant importing specific modes of practice and specific modes of knowing into biology. This practice and knowledge is centered on data. The prominence of genomics suggests the degree to which twenty-first biology relies on data management, rapid data processing, industrial production, and the management of high-throughput workflows. Biology has become about generating, managing, and analyzing large volumes of biological data.Less
During the last thirty years, computers have come to play an increasingly important role in biological work. Especially in the new ‘omic’ disciplines, biologists are as likely to be found in front of a computer screen as they are working at a laboratory bench. Life out of sequence is an account of these transformations: beginning in the 1960s and continuing to the present, it draws on archival research, participant observation in laboratories, and interviews to show how and why the life sciences have been automated, computerized, and digitized. The advent of bioinformatics meant not merely using computers to solve the same old problems. Rather, computers entailed fundamental shifts in training, funding, career paths, work practices, and knowledge production. Computers were originally designed as tools for simulation and data management with applications to military and physics problems. Bringing them from physics into biology meant importing specific modes of practice and specific modes of knowing into biology. This practice and knowledge is centered on data. The prominence of genomics suggests the degree to which twenty-first biology relies on data management, rapid data processing, industrial production, and the management of high-throughput workflows. Biology has become about generating, managing, and analyzing large volumes of biological data.
Dmitri I. Svergun, Michel H. J. Koch, Peter A. Timmins, and Roland P. May
- Published in print:
- 2013
- Published Online:
- December 2013
- ISBN:
- 9780199639533
- eISBN:
- 9780191747731
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199639533.003.0010
- Subject:
- Physics, Crystallography: Physics
SAS is most powerful when it integrates results from other techniques such as high-resolution X-ray crystallography, nuclear magnetic resonance, electron microscopy or hydrodynamic studies such as ...
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SAS is most powerful when it integrates results from other techniques such as high-resolution X-ray crystallography, nuclear magnetic resonance, electron microscopy or hydrodynamic studies such as analytical ultracentrifugation or light-scattering. A brief overview of some complementary techniques is presented describing the parameters which can be determined and how they complement the information on structure and structural changes obtained from SAS. The role of bioinformatic methods in SAS structure analysis is also discussed, as are aspects of experimental automation and the developments in high-throughput and microfluidic techniques, particularly in SAXS. Finally, recognising the lack of unique solutions to modelling SAS data, a critical discussion of SAS model validation is presented.Less
SAS is most powerful when it integrates results from other techniques such as high-resolution X-ray crystallography, nuclear magnetic resonance, electron microscopy or hydrodynamic studies such as analytical ultracentrifugation or light-scattering. A brief overview of some complementary techniques is presented describing the parameters which can be determined and how they complement the information on structure and structural changes obtained from SAS. The role of bioinformatic methods in SAS structure analysis is also discussed, as are aspects of experimental automation and the developments in high-throughput and microfluidic techniques, particularly in SAXS. Finally, recognising the lack of unique solutions to modelling SAS data, a critical discussion of SAS model validation is presented.
Glenn-Peter Sætre and Mark Ravinet
- Published in print:
- 2019
- Published Online:
- July 2019
- ISBN:
- 9780198830917
- eISBN:
- 9780191868993
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198830917.001.0001
- Subject:
- Biology, Evolutionary Biology / Genetics, Biomathematics / Statistics and Data Analysis / Complexity Studies
Evolutionary genetics is the study of how genetic variation leads to evolutionary change. With the recent explosion in the availability of whole genome sequence data, vast quantities of genetic data ...
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Evolutionary genetics is the study of how genetic variation leads to evolutionary change. With the recent explosion in the availability of whole genome sequence data, vast quantities of genetic data are being generated at an ever-increasing pace with the result that programming has become an essential tool for researchers. Most importantly, a thorough understanding of evolutionary principles is essential for making sense of this genetic data. This up-to-date textbook covers all the major components of modern evolutionary genetics, carefully explaining fundamental processes such as mutation, natural selection, genetic drift, and speciation, together with their consequences. In addition to the text, study questions are provided to motivate the reader to think and reflect on the concepts in each chapter. Practical experience is essential when it comes to developing an understanding of how to use genetic data to analyze and address interesting questions in the life sciences and how to interpret results in meaningful ways. Throughout the book, a series of online, computer-based tutorials serves as an introduction to programming and analysis of evolutionary genetic data centered on the R programming language, which stands out as an ideal all-purpose platform to handle and analyze such data. The book and its online materials take full advantage of the authors’ own experience in working in a post-genomic revolution world, and introduce readers to the plethora of molecular and analytical methods that have only recently become available.Less
Evolutionary genetics is the study of how genetic variation leads to evolutionary change. With the recent explosion in the availability of whole genome sequence data, vast quantities of genetic data are being generated at an ever-increasing pace with the result that programming has become an essential tool for researchers. Most importantly, a thorough understanding of evolutionary principles is essential for making sense of this genetic data. This up-to-date textbook covers all the major components of modern evolutionary genetics, carefully explaining fundamental processes such as mutation, natural selection, genetic drift, and speciation, together with their consequences. In addition to the text, study questions are provided to motivate the reader to think and reflect on the concepts in each chapter. Practical experience is essential when it comes to developing an understanding of how to use genetic data to analyze and address interesting questions in the life sciences and how to interpret results in meaningful ways. Throughout the book, a series of online, computer-based tutorials serves as an introduction to programming and analysis of evolutionary genetic data centered on the R programming language, which stands out as an ideal all-purpose platform to handle and analyze such data. The book and its online materials take full advantage of the authors’ own experience in working in a post-genomic revolution world, and introduce readers to the plethora of molecular and analytical methods that have only recently become available.
Conrad Bessant, Darren Oakley, and Ian Shadforth
- Published in print:
- 2014
- Published Online:
- April 2014
- ISBN:
- 9780199658558
- eISBN:
- 9780191779466
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199658558.001.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Biochemistry / Molecular Biology
This book provides an introduction to three of the main tools used in the development of bioinformatics software — Perl, R, and MySQL — and explains how these can be used together to tackle the ...
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This book provides an introduction to three of the main tools used in the development of bioinformatics software — Perl, R, and MySQL — and explains how these can be used together to tackle the complex data-driven challenges that typify modern biology. The book is intended to provide the reader with the knowledge and confidence needed to create databases, to write programs to analyse and visualise data, and to develop interactive web-based applications. Platform-independent examples are provided throughout, making the book suitable for users of Windows, Mac OS or Linux.Less
This book provides an introduction to three of the main tools used in the development of bioinformatics software — Perl, R, and MySQL — and explains how these can be used together to tackle the complex data-driven challenges that typify modern biology. The book is intended to provide the reader with the knowledge and confidence needed to create databases, to write programs to analyse and visualise data, and to develop interactive web-based applications. Platform-independent examples are provided throughout, making the book suitable for users of Windows, Mac OS or Linux.
Steve Kelling
- Published in print:
- 2012
- Published Online:
- August 2016
- ISBN:
- 9780801449116
- eISBN:
- 9780801463952
- Item type:
- chapter
- Publisher:
- Cornell University Press
- DOI:
- 10.7591/cornell/9780801449116.003.0004
- Subject:
- Environmental Science, Environmental Studies
This chapter examines how bioinformatics can be used to advance citizen science engagement opportunities that provide the framework for research, education, and dissemination of information at global ...
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This chapter examines how bioinformatics can be used to advance citizen science engagement opportunities that provide the framework for research, education, and dissemination of information at global scales. It discusses the practical aspects of creating a sound cyberinfrastructure that will serve as the foundation for developing large-scale citizen science projects. It also considers various strategies for creating novel and enduring applications for delivering citizen science and its data to project participants, professional scientists, and managers over the Internet. Finally, it describes techniques for data management and archiving and explains how the Cornell Lab of Ornithology makes data discoverable via metadata. The chapter provides examples of how bioinformatics and cyberinfrastructure resources make it possible for contributors to explore, synthesize, and visualize citizen science data and for professional scientists to carry out complex analyses on the same data sets.Less
This chapter examines how bioinformatics can be used to advance citizen science engagement opportunities that provide the framework for research, education, and dissemination of information at global scales. It discusses the practical aspects of creating a sound cyberinfrastructure that will serve as the foundation for developing large-scale citizen science projects. It also considers various strategies for creating novel and enduring applications for delivering citizen science and its data to project participants, professional scientists, and managers over the Internet. Finally, it describes techniques for data management and archiving and explains how the Cornell Lab of Ornithology makes data discoverable via metadata. The chapter provides examples of how bioinformatics and cyberinfrastructure resources make it possible for contributors to explore, synthesize, and visualize citizen science data and for professional scientists to carry out complex analyses on the same data sets.
Peter J. Bentley
- Published in print:
- 2012
- Published Online:
- November 2020
- ISBN:
- 9780199693795
- eISBN:
- 9780191918421
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780199693795.003.0012
- Subject:
- Computer Science, History of Computer Science
Our world is digitized. Thanks to pioneers such as Turing, Shannon, and von Neumann, we have the amazing technologies of today. We have strong mathematical foundations ...
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Our world is digitized. Thanks to pioneers such as Turing, Shannon, and von Neumann, we have the amazing technologies of today. We have strong mathematical foundations for remarkable silicon machines that run amazing software. We have connections to everyone in the world through easy-to- use interfaces. We even have intelligent machines that help us in ways we could never have dreamed. The journey has only just begun. Every year new pioneers exploit the technologies of computer science for startling and imaginative applications. Artists and musicians use computers to entertain us, biologists use computers to understand us, doctors use computers to heal us. We even use computers to protect ourselves from crime. It’s an exciting time. . . . The young artist enters the large room, rolls of drawings under his arm. There are twelve large cube-shaped computer monitors on desks surrounding him, apparently connected to mainframe computers on the floor below. Blinds cover all the windows, darkening the room, and allowing strange graphics on the screens to shine brightly. The artist nervously unrolls his drawings on the floor of the computer lab, filling the space with his carefully drawn renderings. One large sheet looks like a strange abstract swirling piece of Hindu art with snakes or tentacles fl owing outwards from the centre. Another looks like a weird bug-collector’s display with a myriad differently shaped bugs placed randomly—except that similar bugs are always next to each other. Another looks like an evolutionary tree of abstract shapes, from Viking helmets to beehives, each morphing into another. The watching group of scientists and computer programmers have never seen anything quite like this. The artist had struggled to gain acceptance for his ideas from his own community. The art world was not ready for his use of computers to help generate his art. Would this audience be any different? William Latham looks up at his audience of scientists, and begins to explain his work. Latham was no ordinary artist. Born in 1961, he grew up in Blewbury, England. It was a rural setting, but because of the nearby Harwell Atomic Energy Research Establishment, he came into contact with scientists throughout his childhood.
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Our world is digitized. Thanks to pioneers such as Turing, Shannon, and von Neumann, we have the amazing technologies of today. We have strong mathematical foundations for remarkable silicon machines that run amazing software. We have connections to everyone in the world through easy-to- use interfaces. We even have intelligent machines that help us in ways we could never have dreamed. The journey has only just begun. Every year new pioneers exploit the technologies of computer science for startling and imaginative applications. Artists and musicians use computers to entertain us, biologists use computers to understand us, doctors use computers to heal us. We even use computers to protect ourselves from crime. It’s an exciting time. . . . The young artist enters the large room, rolls of drawings under his arm. There are twelve large cube-shaped computer monitors on desks surrounding him, apparently connected to mainframe computers on the floor below. Blinds cover all the windows, darkening the room, and allowing strange graphics on the screens to shine brightly. The artist nervously unrolls his drawings on the floor of the computer lab, filling the space with his carefully drawn renderings. One large sheet looks like a strange abstract swirling piece of Hindu art with snakes or tentacles fl owing outwards from the centre. Another looks like a weird bug-collector’s display with a myriad differently shaped bugs placed randomly—except that similar bugs are always next to each other. Another looks like an evolutionary tree of abstract shapes, from Viking helmets to beehives, each morphing into another. The watching group of scientists and computer programmers have never seen anything quite like this. The artist had struggled to gain acceptance for his ideas from his own community. The art world was not ready for his use of computers to help generate his art. Would this audience be any different? William Latham looks up at his audience of scientists, and begins to explain his work. Latham was no ordinary artist. Born in 1961, he grew up in Blewbury, England. It was a rural setting, but because of the nearby Harwell Atomic Energy Research Establishment, he came into contact with scientists throughout his childhood.
David P. Yee and Tim Hunkapiller
- Published in print:
- 1999
- Published Online:
- November 2020
- ISBN:
- 9780195119404
- eISBN:
- 9780197561256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780195119404.003.0017
- Subject:
- Computer Science, Systems Analysis and Design
The Human Genome Project was launched in the early 1990s to map, sequence, and study the function of genomes derived from humans and a number of model ...
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The Human Genome Project was launched in the early 1990s to map, sequence, and study the function of genomes derived from humans and a number of model organisms such as mouse, rat, fruit fly, worm, yeast, and Escherichia coli. This ambitious project was made possible by advances in high-speed DNA sequencing technology (Hunkapiller et al., 1991). To date, the Human Genome Project and other large-scale sequencing projects have been enormously successful. The complete genomes of several microbes (such as Hemophilus influenzae Rd, Mycoplasma genitalium, and Methanococcus jannaschii) have been completely sequenced. The genome of bacteriophage T4 is complete, and the 4.6-megabase sequence of E. coli and the 13-megabase genome of Saccharomyces cerevisiae have just recently also been completed. There are 71 megabases of the nematode Caenorhabditis elegans available. Six megabases of mouse and 60 megabases of human genomic sequence have been finished, which represent 0.2% and 2% of their respective genomes. Finally, more than 1 million expressed sequence tags derived from human and mouse complementary DNA expression libraries are publicly available. These public data, in addition to private and proprietary DNA sequence databases, represent an enormous information-processing challenge and data-mining opportunity. The need for common interfaces and query languages to access heterogeneous sequence databases is well documented, and several good systems are well underway to provide those interfaces (Woodsmall and Benson, 1993; Marr, 1996). Our own work on database and program interoperability in this domain and in computational chemistry (Gushing, 1995) has shown, however, that providing the interface is but the first step toward making these databases fully useful to the researcher. (Here, the term “database” means a collection of data in electronic form, which may not necessarily be physically deposited in a database management system [DBMS]. A scientist’s database could thus be a collection of flat files, where the term “database” means “data stored in a DBMS” is clear from the context.) Deciphering the genomes of sequenced organisms falls into the realm of analysis; there is now plenty of sequence data. The most common form of sequence analysis involves the identification of homologous relationships among similar sequences.
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The Human Genome Project was launched in the early 1990s to map, sequence, and study the function of genomes derived from humans and a number of model organisms such as mouse, rat, fruit fly, worm, yeast, and Escherichia coli. This ambitious project was made possible by advances in high-speed DNA sequencing technology (Hunkapiller et al., 1991). To date, the Human Genome Project and other large-scale sequencing projects have been enormously successful. The complete genomes of several microbes (such as Hemophilus influenzae Rd, Mycoplasma genitalium, and Methanococcus jannaschii) have been completely sequenced. The genome of bacteriophage T4 is complete, and the 4.6-megabase sequence of E. coli and the 13-megabase genome of Saccharomyces cerevisiae have just recently also been completed. There are 71 megabases of the nematode Caenorhabditis elegans available. Six megabases of mouse and 60 megabases of human genomic sequence have been finished, which represent 0.2% and 2% of their respective genomes. Finally, more than 1 million expressed sequence tags derived from human and mouse complementary DNA expression libraries are publicly available. These public data, in addition to private and proprietary DNA sequence databases, represent an enormous information-processing challenge and data-mining opportunity. The need for common interfaces and query languages to access heterogeneous sequence databases is well documented, and several good systems are well underway to provide those interfaces (Woodsmall and Benson, 1993; Marr, 1996). Our own work on database and program interoperability in this domain and in computational chemistry (Gushing, 1995) has shown, however, that providing the interface is but the first step toward making these databases fully useful to the researcher. (Here, the term “database” means a collection of data in electronic form, which may not necessarily be physically deposited in a database management system [DBMS]. A scientist’s database could thus be a collection of flat files, where the term “database” means “data stored in a DBMS” is clear from the context.) Deciphering the genomes of sequenced organisms falls into the realm of analysis; there is now plenty of sequence data. The most common form of sequence analysis involves the identification of homologous relationships among similar sequences.
Bruce A. Shapiro and Wojciech Kasprzak
- Published in print:
- 1999
- Published Online:
- November 2020
- ISBN:
- 9780195119404
- eISBN:
- 9780197561256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780195119404.003.0018
- Subject:
- Computer Science, Systems Analysis and Design
Genomic information (nucleic acid and amino acid sequences) completely determines the characteristics of the nucleic acid and protein molecules that ...
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Genomic information (nucleic acid and amino acid sequences) completely determines the characteristics of the nucleic acid and protein molecules that express a living organism’s function. One of the greatest challenges in which computation is playing a role is the prediction of higher order structure from the one-dimensional sequence of genes. Rules for determining macromolecule folding have been continually evolving. Specifically in the case of RNA (ribonucleic acid) there are rules and computer algorithms/systems (see below) that partially predict and can help analyze the secondary and tertiary interactions of distant parts of the polymer chain. These successes are very important for determining the structural and functional characteristics of RNA in disease processes and hi the cell life cycle. It has been shown that molecules with the same function have the potential to fold into similar structures though they might differ in their primary sequences. This fact also illustrates the importance of secondary and tertiary structure in relation to function. Examples of such constancy in secondary structure exist in transfer RNAs (tRNAs), 5s RNAs, 16s RNAs, viroid RNAs, and portions of retroviruses such as HIV. The secondary and tertiary structure of tRNA Phe (Kim et al., 1974), of a hammerhead ribozyme (Pley et al., 1994), and of Tetrahymena (Cate et al., 1996a, 1996b) have been shown by their crystal structure. Currently little is known of tertiary interactions, but studies on tRNA indicate these are weaker than secondary structure interactions (Riesner and Romer, 1973; Crothers and Cole, 1978; Jaeger et al., 1989b). It is very difficult to crystallize and/or get nuclear magnetic resonance spectrum data for large RNA molecules. Therefore, a logical place to start in determining the 3D structure of RNA is computer prediction of the secondary structure. The sequence (primary structure) of an RNA molecule is relatively easy to produce. Because experimental methods for determining RNA secondary and tertiary structure (when the primary sequence folds back on itself and forms base pairs) have not kept pace with the rapid discovery of RNA molecules and their function, use of and methods for computer prediction of secondary and tertiary structures have increasingly been developed.
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Genomic information (nucleic acid and amino acid sequences) completely determines the characteristics of the nucleic acid and protein molecules that express a living organism’s function. One of the greatest challenges in which computation is playing a role is the prediction of higher order structure from the one-dimensional sequence of genes. Rules for determining macromolecule folding have been continually evolving. Specifically in the case of RNA (ribonucleic acid) there are rules and computer algorithms/systems (see below) that partially predict and can help analyze the secondary and tertiary interactions of distant parts of the polymer chain. These successes are very important for determining the structural and functional characteristics of RNA in disease processes and hi the cell life cycle. It has been shown that molecules with the same function have the potential to fold into similar structures though they might differ in their primary sequences. This fact also illustrates the importance of secondary and tertiary structure in relation to function. Examples of such constancy in secondary structure exist in transfer RNAs (tRNAs), 5s RNAs, 16s RNAs, viroid RNAs, and portions of retroviruses such as HIV. The secondary and tertiary structure of tRNA Phe (Kim et al., 1974), of a hammerhead ribozyme (Pley et al., 1994), and of Tetrahymena (Cate et al., 1996a, 1996b) have been shown by their crystal structure. Currently little is known of tertiary interactions, but studies on tRNA indicate these are weaker than secondary structure interactions (Riesner and Romer, 1973; Crothers and Cole, 1978; Jaeger et al., 1989b). It is very difficult to crystallize and/or get nuclear magnetic resonance spectrum data for large RNA molecules. Therefore, a logical place to start in determining the 3D structure of RNA is computer prediction of the secondary structure. The sequence (primary structure) of an RNA molecule is relatively easy to produce. Because experimental methods for determining RNA secondary and tertiary structure (when the primary sequence folds back on itself and forms base pairs) have not kept pace with the rapid discovery of RNA molecules and their function, use of and methods for computer prediction of secondary and tertiary structures have increasingly been developed.
Martyn Amos and Gerald Owenson
- Published in print:
- 2004
- Published Online:
- November 2020
- ISBN:
- 9780195155396
- eISBN:
- 9780197561942
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780195155396.003.0005
- Subject:
- Computer Science, Mathematical Theory of Computation
The abstract operation of complex natural processes is often expressed in terms of networks of computational components such as Boolean logic gates or ...
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The abstract operation of complex natural processes is often expressed in terms of networks of computational components such as Boolean logic gates or artificial neurons. The interaction of biological molecules and the flow of information controlling the development and behavior of organisms is particularly amenable to this approach, and these models are well established in the biological community. However, only relatively recently have papers appeared proposing the use of such systems to perform useful, human-defined tasks. Rather than merely using the network analogy as a convenient technique for clarifying our understanding of complex systems, it is now possible to harness the power of such systems for the purposes of computation. The purpose of this volume is to discuss such work. In this introductory chapter we place this work in historical context and provide an introduction to some of the underlying molecular biology. We then introduce recent developments in the field of cellular computing. Despite the relatively recent emergence of molecular computing as a distinct research area, the link between biology and computer science is not a new one. Of course, for years biologists have used computers to store and analyze experimental data. Indeed, it is widely accepted that the huge advances of the Human Genome Project (as well as other genome projects) were only made possible by the powerful computational tools available to them. Bioinformatics has emerged as the science of the 21st century, requiring the contributions of truly interdisciplinary scientists who are equally at home at the lab bench or writing software at the computer. However, the seeds of the relationship between biology and computer science were sown long ago, when the latter discipline did not even exist. When, in the 17th century, the French mathematician and philosopher René Descartes declared to Queen Christina of Sweden that animals could be considered a class of machines, she challenged him to demonstrate how a clock could reproduce. Three centuries later, with the publication of The General and Logical Theory of Automata [19] John von Neumann showed how a machine could indeed construct a copy of itself.
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The abstract operation of complex natural processes is often expressed in terms of networks of computational components such as Boolean logic gates or artificial neurons. The interaction of biological molecules and the flow of information controlling the development and behavior of organisms is particularly amenable to this approach, and these models are well established in the biological community. However, only relatively recently have papers appeared proposing the use of such systems to perform useful, human-defined tasks. Rather than merely using the network analogy as a convenient technique for clarifying our understanding of complex systems, it is now possible to harness the power of such systems for the purposes of computation. The purpose of this volume is to discuss such work. In this introductory chapter we place this work in historical context and provide an introduction to some of the underlying molecular biology. We then introduce recent developments in the field of cellular computing. Despite the relatively recent emergence of molecular computing as a distinct research area, the link between biology and computer science is not a new one. Of course, for years biologists have used computers to store and analyze experimental data. Indeed, it is widely accepted that the huge advances of the Human Genome Project (as well as other genome projects) were only made possible by the powerful computational tools available to them. Bioinformatics has emerged as the science of the 21st century, requiring the contributions of truly interdisciplinary scientists who are equally at home at the lab bench or writing software at the computer. However, the seeds of the relationship between biology and computer science were sown long ago, when the latter discipline did not even exist. When, in the 17th century, the French mathematician and philosopher René Descartes declared to Queen Christina of Sweden that animals could be considered a class of machines, she challenged him to demonstrate how a clock could reproduce. Three centuries later, with the publication of The General and Logical Theory of Automata [19] John von Neumann showed how a machine could indeed construct a copy of itself.
Peter J. Bentley
- Published in print:
- 2012
- Published Online:
- November 2020
- ISBN:
- 9780199693795
- eISBN:
- 9780191918421
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780199693795.003.0005
- Subject:
- Computer Science, History of Computer Science
They obey our instructions with unlimited patience. They store the world’s knowledge and make it accessible in a split second. They are the backbone of modern society. ...
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They obey our instructions with unlimited patience. They store the world’s knowledge and make it accessible in a split second. They are the backbone of modern society. Yet they are largely ignored. Computers. They comprise our crowning achievements to date, the pinnacle of all tools. Computer processors and software represent the most complex designs humans have ever created. The science of computers has enabled one of the most extraordinary transformations of our societies in human history. . . . You switch on your computer and launch the Internet browser. A one-word search for ‘pizza’ finds a list of pizza restaurants in your area. One click with the mouse and you are typing in your address to see if this restaurant delivers. They do! And they also allow you to order online. You choose the type of pizza you feel like, adding your favourite toppings. The restaurant even allows you to pay online, so you type in your credit card number, your address, and the time you’d like the delivery. You choose ‘as soon as possible’ and click ‘pay’. Just thirty-five minutes later there is a knock on your door. The pizza is here, smelling delicious. You tip the delivery guy and take the pizza to your table to eat. Ordering pizza is nothing unusual for many of us around the world. Although it may seem surprising, this increasingly common scenario with cheap prices, fast delivery, and access to such variety of food for millions of customers is only possible because of computers. In the situation above you might have spotted just one computer. If we take a look behind the scenes, the number of computers involved in bringing your pizza is astonishing. When you switched on your computer, you actually powered up many computers that all work together to make the display, mouse, keyboard, broadband, and main computer operate. Your computer linked itself to the Internet—which is a worldwide network of computers— with the help of computers of the phone company and Internet service provider. When you searched for ‘pizza’ the request was routed between several computers before reaching the search engine computers.
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They obey our instructions with unlimited patience. They store the world’s knowledge and make it accessible in a split second. They are the backbone of modern society. Yet they are largely ignored. Computers. They comprise our crowning achievements to date, the pinnacle of all tools. Computer processors and software represent the most complex designs humans have ever created. The science of computers has enabled one of the most extraordinary transformations of our societies in human history. . . . You switch on your computer and launch the Internet browser. A one-word search for ‘pizza’ finds a list of pizza restaurants in your area. One click with the mouse and you are typing in your address to see if this restaurant delivers. They do! And they also allow you to order online. You choose the type of pizza you feel like, adding your favourite toppings. The restaurant even allows you to pay online, so you type in your credit card number, your address, and the time you’d like the delivery. You choose ‘as soon as possible’ and click ‘pay’. Just thirty-five minutes later there is a knock on your door. The pizza is here, smelling delicious. You tip the delivery guy and take the pizza to your table to eat. Ordering pizza is nothing unusual for many of us around the world. Although it may seem surprising, this increasingly common scenario with cheap prices, fast delivery, and access to such variety of food for millions of customers is only possible because of computers. In the situation above you might have spotted just one computer. If we take a look behind the scenes, the number of computers involved in bringing your pizza is astonishing. When you switched on your computer, you actually powered up many computers that all work together to make the display, mouse, keyboard, broadband, and main computer operate. Your computer linked itself to the Internet—which is a worldwide network of computers— with the help of computers of the phone company and Internet service provider. When you searched for ‘pizza’ the request was routed between several computers before reaching the search engine computers.
John Ross, Igor Schreiber, and Marcel O. Vlad
- Published in print:
- 2006
- Published Online:
- November 2020
- ISBN:
- 9780195178685
- eISBN:
- 9780197562277
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780195178685.003.0015
- Subject:
- Chemistry, Physical Chemistry
There is enormous interest in the biology of complex reaction systems, be it in metabolism, signal transduction, gene regulatory networks, protein synthesis, and many ...
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There is enormous interest in the biology of complex reaction systems, be it in metabolism, signal transduction, gene regulatory networks, protein synthesis, and many others. The field of the interpretation of experiments on such systems by application of the methods of information science, computer science, and biostatistics is called bioinformatics (see for a presentation of this subject). Part of it is an extension of the chemical approaches that we have discussed for obtaining information on the reaction mechanisms of complex chemical systems to complex biological and genetic systems. We present here a very brief introduction to this field, which is exploding with scientific and technical activity. No review is intended, only an indication of several approaches on the subject of our book, with apologies for the omission of vast numbers of publications. A few reminders: The entire complement of DNA molecules constitute the genome, which consists of many genes. RNA is generated from DNA in a process called transcription; the RNA that codes for proteins is known as messenger RNA, abbreviated tomRNA. Other RNAs code for functional molecules such as transfer RNAs, ribosomal components, and regulatory molecules, or even have enzymatic function. Protein synthesis is regulated by many mechanisms, including that for transcription initiation, RNA splicing (in eukaryotes), mRNA transport, translation initiation, post-translational modifications, and degradation of mRNA. Proteins perform perhaps most cellular functions. Advances in microarray technology, with the use of cDNA or oligonucleotides immobilized in a predefined organization on a solid phase, have led to measurements of mRNA expression levels on a genome-wide scale (see chapter 3). The results of the measurements can be displayed on a plot on which a row represents one gene at various times, a column the whole set of genes, and the time of gene expression is plotted along the axis of rows. The changes in expression levels, as measured by fluorescence, are indicated by colors, for example green for decreased expression, black for no change in expression, and red for increased expression. Responses in expression levels have been measured for various biochemical and physiological conditions. We turn now to a few methods of obtaining information on genomic networks from microarray measurements.
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There is enormous interest in the biology of complex reaction systems, be it in metabolism, signal transduction, gene regulatory networks, protein synthesis, and many others. The field of the interpretation of experiments on such systems by application of the methods of information science, computer science, and biostatistics is called bioinformatics (see for a presentation of this subject). Part of it is an extension of the chemical approaches that we have discussed for obtaining information on the reaction mechanisms of complex chemical systems to complex biological and genetic systems. We present here a very brief introduction to this field, which is exploding with scientific and technical activity. No review is intended, only an indication of several approaches on the subject of our book, with apologies for the omission of vast numbers of publications. A few reminders: The entire complement of DNA molecules constitute the genome, which consists of many genes. RNA is generated from DNA in a process called transcription; the RNA that codes for proteins is known as messenger RNA, abbreviated tomRNA. Other RNAs code for functional molecules such as transfer RNAs, ribosomal components, and regulatory molecules, or even have enzymatic function. Protein synthesis is regulated by many mechanisms, including that for transcription initiation, RNA splicing (in eukaryotes), mRNA transport, translation initiation, post-translational modifications, and degradation of mRNA. Proteins perform perhaps most cellular functions. Advances in microarray technology, with the use of cDNA or oligonucleotides immobilized in a predefined organization on a solid phase, have led to measurements of mRNA expression levels on a genome-wide scale (see chapter 3). The results of the measurements can be displayed on a plot on which a row represents one gene at various times, a column the whole set of genes, and the time of gene expression is plotted along the axis of rows. The changes in expression levels, as measured by fluorescence, are indicated by colors, for example green for decreased expression, black for no change in expression, and red for increased expression. Responses in expression levels have been measured for various biochemical and physiological conditions. We turn now to a few methods of obtaining information on genomic networks from microarray measurements.