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.
David M. Williams, Malte C. Ebach, and Quentin D. Wheeler
- Published in print:
- 2010
- Published Online:
- March 2012
- ISBN:
- 9780520267725
- eISBN:
- 9780520947993
- Item type:
- chapter
- Publisher:
- University of California Press
- DOI:
- 10.1525/california/9780520267725.003.0010
- Subject:
- Biology, Evolutionary Biology / Genetics
In his account of the history of numerical taxonomy, P. H. A. Sneath argued that Hennigian cladistics is a side issue that has not proven its value, and that numerical taxonomy is “the greatest ...
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In his account of the history of numerical taxonomy, P. H. A. Sneath argued that Hennigian cladistics is a side issue that has not proven its value, and that numerical taxonomy is “the greatest advance in systematics since Charles Darwin or perhaps since Carl Linnaeus.” Phenetics and cladistics might very well be viewed as contrasting methodologies, situated within the larger phenomenon of numerical taxonomy. Comparing phenetics with cladistics can be rendered simple, relating to a concept of “naturalness”: phenetics discovers taxa (classification) via the parameter “overall similarity” derived from an assessment of “similarities,” cladistics discovers taxa (classification) via the parameter “relationship,” expressed in its simplest form as A(BC), derived from an assessment of particular homologies. A significant event in the development of numerical taxonomy was the publication of J. Felsenstein's book Inferring Phylogenies. Felsenstein's forty intervening years — the “statistical, computational, and algorithmic work” — amounts to the dismissal of classification and homology, reinventing and remodeling phenetics and christening it bioinformatics, thereby reducing the subject of systematics to a set of methodologies, a mere technique.Less
In his account of the history of numerical taxonomy, P. H. A. Sneath argued that Hennigian cladistics is a side issue that has not proven its value, and that numerical taxonomy is “the greatest advance in systematics since Charles Darwin or perhaps since Carl Linnaeus.” Phenetics and cladistics might very well be viewed as contrasting methodologies, situated within the larger phenomenon of numerical taxonomy. Comparing phenetics with cladistics can be rendered simple, relating to a concept of “naturalness”: phenetics discovers taxa (classification) via the parameter “overall similarity” derived from an assessment of “similarities,” cladistics discovers taxa (classification) via the parameter “relationship,” expressed in its simplest form as A(BC), derived from an assessment of particular homologies. A significant event in the development of numerical taxonomy was the publication of J. Felsenstein's book Inferring Phylogenies. Felsenstein's forty intervening years — the “statistical, computational, and algorithmic work” — amounts to the dismissal of classification and homology, reinventing and remodeling phenetics and christening it bioinformatics, thereby reducing the subject of systematics to a set of methodologies, a mere technique.
Frank Rosenzweig and Gavin Sherlock
- Published in print:
- 2009
- Published Online:
- March 2012
- ISBN:
- 9780520247666
- eISBN:
- 9780520944473
- Item type:
- chapter
- Publisher:
- University of California Press
- DOI:
- 10.1525/california/9780520247666.003.0013
- Subject:
- Biology, Evolutionary Biology / Genetics
This chapter explores recent examples where experimental evolution, empowered by genomic technologies and increasingly informed by systems biology, has fundamentally advanced our understanding of ...
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This chapter explores recent examples where experimental evolution, empowered by genomic technologies and increasingly informed by systems biology, has fundamentally advanced our understanding of adaptive evolution. It focuses on genomically enabled insights into the following issues: how prevalent adaptive parallelism is; what the relative contributions of structural versus regulatory mutations are to the adaptive process; whether trade offs inevitably result from pleiotropy; how and why loss of function occurs under strong selection; and how much and how often adaptations arise from large-scale structural changes, such as gene duplications, deletions, and translocations. The chapter also offers perspective on how whole-genome sequencing and bioinformatics have been and can be fruitfully applied in the context of experimental evolution.Less
This chapter explores recent examples where experimental evolution, empowered by genomic technologies and increasingly informed by systems biology, has fundamentally advanced our understanding of adaptive evolution. It focuses on genomically enabled insights into the following issues: how prevalent adaptive parallelism is; what the relative contributions of structural versus regulatory mutations are to the adaptive process; whether trade offs inevitably result from pleiotropy; how and why loss of function occurs under strong selection; and how much and how often adaptations arise from large-scale structural changes, such as gene duplications, deletions, and translocations. The chapter also offers perspective on how whole-genome sequencing and bioinformatics have been and can be fruitfully applied in the context of experimental evolution.
Conrad Bessant, Darren Oakley, and Ian Shadforth
- Published in print:
- 2014
- Published Online:
- April 2014
- ISBN:
- 9780199658558
- eISBN:
- 9780191779466
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199658558.003.0001
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Biochemistry / Molecular Biology
This chapter provides a high level overview of the field of bioinformatics, covering the main areas of interest in contemporary bioinformatics and introducing the pre-eminent software tools and ...
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This chapter provides a high level overview of the field of bioinformatics, covering the main areas of interest in contemporary bioinformatics and introducing the pre-eminent software tools and databases used in the field. The importance of programmatic automation of bioinformatics tasks is highlighted, and the concept of building bioinformatics solutions is explained.Less
This chapter provides a high level overview of the field of bioinformatics, covering the main areas of interest in contemporary bioinformatics and introducing the pre-eminent software tools and databases used in the field. The importance of programmatic automation of bioinformatics tasks is highlighted, and the concept of building bioinformatics solutions is explained.
Arlindo Oliveira
- Published in print:
- 2017
- Published Online:
- September 2017
- ISBN:
- 9780262036030
- eISBN:
- 9780262338394
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262036030.003.0007
- Subject:
- Computer Science, Artificial Intelligence
This chapter describes how computers played a decisive role in the sequencing and understanding of the genomes of humans and other organisms. The discovery of the structure of DNA, by Watson and ...
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This chapter describes how computers played a decisive role in the sequencing and understanding of the genomes of humans and other organisms. The discovery of the structure of DNA, by Watson and Crick, opened the way to a systematic approach to biology, based on the study of the way DNA encodes the proteins in all living cells. The area that became known as computational biology, or bioinformatics, aims at modelling, simulating, and understanding the behaviour of biological systems, using computer models and algorithms. Such a deep understanding will enable us to create computer models of cells and of entire organisms and even, in the future, open the doors to the creation of entirely new lifeforms, a field known as synthetic biology.Less
This chapter describes how computers played a decisive role in the sequencing and understanding of the genomes of humans and other organisms. The discovery of the structure of DNA, by Watson and Crick, opened the way to a systematic approach to biology, based on the study of the way DNA encodes the proteins in all living cells. The area that became known as computational biology, or bioinformatics, aims at modelling, simulating, and understanding the behaviour of biological systems, using computer models and algorithms. Such a deep understanding will enable us to create computer models of cells and of entire organisms and even, in the future, open the doors to the creation of entirely new lifeforms, a field known as synthetic biology.
Thomas F. Turner, Thomas E. Dowling, Trevor J. Krabbenhoft, Megan J. Osborne, and Tyler J. Pilger
- Published in print:
- 2021
- Published Online:
- May 2021
- ISBN:
- 9780226694337
- eISBN:
- 9780226694504
- Item type:
- chapter
- Publisher:
- University of Chicago Press
- DOI:
- 10.7208/chicago/9780226694504.003.0014
- Subject:
- Biology, Aquatic Biology
At its core, conservation genetics aims to characterize genetic diversity in space and time, infer processes that change diversity, and use this information to prescribe actions that maintain and ...
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At its core, conservation genetics aims to characterize genetic diversity in space and time, infer processes that change diversity, and use this information to prescribe actions that maintain and enhance diversity and self-sustainability of imperiled species. The field of conservation genomics, fueled by rapid advances in DNA sequencing and genotyping technologies, offers considerable promise for informing conservation of desert fishes. A whole-genome perspective permits deeper insight into lineage identity, roles for natural and artificial selection for shaping diversity, and scope for response to environmental change. Similarly, high-throughput genotyping technology allows unique identification of thousands of individuals at low cost, thereby enabling genetic monitoring programs that explicitly link demographic and genetic processes in recovery planning and implementation. Dense temporal and spatial sampling enhances accuracy and precision of key metrics like genetic effective population size and effective migration rates. Rapid developments in epigenetics and remote sensing will continue to revolutionize the field. Yet, as new technology is applied to conservation problems, it is important to be mindful of limitations and pitfalls. Interpretation based on principles of ecological, evolutionary, and genetic theory is essential when translating findings to action to benefit desert fishes.Less
At its core, conservation genetics aims to characterize genetic diversity in space and time, infer processes that change diversity, and use this information to prescribe actions that maintain and enhance diversity and self-sustainability of imperiled species. The field of conservation genomics, fueled by rapid advances in DNA sequencing and genotyping technologies, offers considerable promise for informing conservation of desert fishes. A whole-genome perspective permits deeper insight into lineage identity, roles for natural and artificial selection for shaping diversity, and scope for response to environmental change. Similarly, high-throughput genotyping technology allows unique identification of thousands of individuals at low cost, thereby enabling genetic monitoring programs that explicitly link demographic and genetic processes in recovery planning and implementation. Dense temporal and spatial sampling enhances accuracy and precision of key metrics like genetic effective population size and effective migration rates. Rapid developments in epigenetics and remote sensing will continue to revolutionize the field. Yet, as new technology is applied to conservation problems, it is important to be mindful of limitations and pitfalls. Interpretation based on principles of ecological, evolutionary, and genetic theory is essential when translating findings to action to benefit desert fishes.
Susan P. Harrison
- Published in print:
- 2013
- Published Online:
- January 2014
- ISBN:
- 9780520275546
- eISBN:
- 9780520954731
- Item type:
- chapter
- Publisher:
- University of California Press
- DOI:
- 10.1525/california/9780520275546.003.0006
- Subject:
- Biology, Ecology
This chapter reviews innovative practices that have originated or been advanced in California for the protection of biological diversity. California has been one of the nation's leaders in initiating ...
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This chapter reviews innovative practices that have originated or been advanced in California for the protection of biological diversity. California has been one of the nation's leaders in initiating new ways of implementing endangered-species laws, carrying out conservation actions on public and private lands, using biodiversity information in planning conservation and mitigation actions, and adapting conservation plans to the reality of climate change. While they are not entirely successful, these important innovations recognize the value of California's unique biological diversity.Less
This chapter reviews innovative practices that have originated or been advanced in California for the protection of biological diversity. California has been one of the nation's leaders in initiating new ways of implementing endangered-species laws, carrying out conservation actions on public and private lands, using biodiversity information in planning conservation and mitigation actions, and adapting conservation plans to the reality of climate change. While they are not entirely successful, these important innovations recognize the value of California's unique biological diversity.
