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Author: Francois Ferre Publisher: Springer Science & Business Media ISBN: 1461241642 Category : Medical Languages : en Pages : 379
Book Description
Geneticists and molecular biologists have been interested in quantifying genes and their products for many years and for various reasons (Bishop, 1974). Early molecular methods were based on molecular hybridization, and were devised shortly after Marmur and Doty (1961) first showed that denaturation of the double helix could be reversed - that the process of molecular reassociation was exquisitely sequence dependent. Gillespie and Spiegelman (1965) developed a way of using the method to titrate the number of copies of a probe within a target sequence in which the target sequence was fixed to a membrane support prior to hybridization with the probe - typically a RNA. Thus, this was a precursor to many of the methods still in use, and indeed under development, today. Early examples of the application of these methods included the measurement of the copy numbers in gene families such as the ribosomal genes and the immunoglo bulin family. Amplification of genes in tumors and in response to drug treatment was discovered by this method. In the same period, methods were invented for estimating gene num bers based on the kinetics of the reassociation process - the so-called Cot analysis. This method, which exploits the dependence of the rate of reassociation on the concentration of the two strands, revealed the presence of repeated sequences in the DNA of higher eukaryotes (Britten and Kohne, 1968). An adaptation to RNA, Rot analysis (Melli and Bishop, 1969), was used to measure the abundance of RNAs in a mixed population.
Author: Francois Ferre Publisher: Springer Science & Business Media ISBN: 1461241642 Category : Medical Languages : en Pages : 379
Book Description
Geneticists and molecular biologists have been interested in quantifying genes and their products for many years and for various reasons (Bishop, 1974). Early molecular methods were based on molecular hybridization, and were devised shortly after Marmur and Doty (1961) first showed that denaturation of the double helix could be reversed - that the process of molecular reassociation was exquisitely sequence dependent. Gillespie and Spiegelman (1965) developed a way of using the method to titrate the number of copies of a probe within a target sequence in which the target sequence was fixed to a membrane support prior to hybridization with the probe - typically a RNA. Thus, this was a precursor to many of the methods still in use, and indeed under development, today. Early examples of the application of these methods included the measurement of the copy numbers in gene families such as the ribosomal genes and the immunoglo bulin family. Amplification of genes in tumors and in response to drug treatment was discovered by this method. In the same period, methods were invented for estimating gene num bers based on the kinetics of the reassociation process - the so-called Cot analysis. This method, which exploits the dependence of the rate of reassociation on the concentration of the two strands, revealed the presence of repeated sequences in the DNA of higher eukaryotes (Britten and Kohne, 1968). An adaptation to RNA, Rot analysis (Melli and Bishop, 1969), was used to measure the abundance of RNAs in a mixed population.
Author: Nalini Raghavachari Publisher: Humana ISBN: 9781493978335 Category : Medical Languages : en Pages : 0
Book Description
This volume provides experimental and bioinformatics approaches related to different aspects of gene expression analysis. Divided in three sections chapters detail wet-lab protocols, bioinformatics approaches, single-cell gene expression, highly multiplexed amplicon sequencing, multi-omics techniques, and targeted sequencing. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Gene Expression Analysis: Methods and Protocols aims provide useful information to researchers worldwide.
Author: Bruce K. Patterson Publisher: Springer Science & Business Media ISBN: 1461213428 Category : Science Languages : en Pages : 157
Book Description
Can the son or daughter of a baseball pitcher or cricket bowler throw a ball 100 miles an hour? Is the son or daughter of an opera singer also an opera singer? Is a house with functional light switches lit? The line of thinking in these rhetorical questions also applies to human genetics. What do baseball pitchers, opera sing ers, light switches, and the Human Genome Project have in common? These questions address the issue of potential versus realization of function. Although sons and daughters of baseball pitchers and opera singers may have inherited the mechanical attributes to be baseball pitchers and opera singers, they may not, at any point in time, be baseball pitchers or opera singers. A house with functional light switches is not lit unless the light switches are on. Similarly, all of the genes discovered and sequenced as a result of the Human Genome Project are not expressed at the same time. Genome project information will allow us to deter mine the repertoire of genes in an individual, which is analogous to determining where the light switches in a house are located and whether they are functional (a mutation or deletion in the Genome Project Model). The pattern of "on" light switches in a house gives us functional information as to what the family inside is doing (e. g. , eating, reading, sleeping). Similarly, the pattern of gene expression (RNA) gives us information on what our bodies are doing (e. g.
Author: S. Meuer Publisher: Springer Science & Business Media ISBN: 3642595243 Category : Science Languages : en Pages : 390
Book Description
The first comprehensive treatise on Rapid Cycle Real-Time PCR. With amplification times of 15-30 minutes of on-line detection and analysis, nucleic acid quantification of mutation analysis finally becomes a routine, powerful and rapid method. Focusing primarily on the LightCycler, an instrument that combines Rapid Cycle PCR with fluorescent monitoring, this technology provides convenient analysis by melting temperatures. PCR products can be identified by product Tm, and single base mismatches can easily be genotyped by probe Tm. Methods chapters detail the theory behind quantification of mutation analysis; the design of synthesis of fluorescent hybridization probes of the preparation of template DNA. Application chapters apply nucleid acid quantification to infectious organisms of intracellular messengers and mutation detection to somatic of acquired mutations.
Author: Bruce K. Patterson Publisher: Springer Science & Business Media ISBN: 9780817640347 Category : Medical Languages : en Pages : 174
Book Description
"Sensitive detection of gene expression is hindered by poor recovery or preservation of target sequences. This book emphasizes methods that are optimized to ensure full recovery and preservation of gene expression targets from specimen acquisition through to detection and final analysis." "Researchers and students in cellular biology will find this book a very useful guide to gene localizing techniques."--BOOK JACKET.
Author: Giovanni Parmigiani Publisher: Springer Science & Business Media ISBN: 0387216790 Category : Medical Languages : en Pages : 511
Book Description
This book presents practical approaches for the analysis of data from gene expression micro-arrays. It describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools. The materials presented cover a range of software tools designed for varied audiences.
Author: Piero Carninci Publisher: Pan Stanford Publishing ISBN: 9814241342 Category : Mathematics Languages : en Pages : 281
Book Description
This book is a guide for users of new technologies, as it includes accurately proven protocols, allowing readers to prepare their samples for experiments. Although examples mainly concern mammalians, the discussion expands to other groups of eukaryotes, where these approaches are complementing genome sequencing.
Author: Pankaj Barah Publisher: CRC Press ISBN: 1000425754 Category : Computers Languages : en Pages : 276
Book Description
Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences
Author: Francisco A. Gómez Vela Publisher: MDPI ISBN: 3039437712 Category : Medical Languages : en Pages : 222
Book Description
In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Author: Altuna Akalin Publisher: CRC Press ISBN: 1498781861 Category : Mathematics Languages : en Pages : 463
Book Description
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
Author: Francois Ferre
Author: Francois Ferre
Author: Nalini Raghavachari
Author: Bruce K. Patterson
Author: S. Meuer
Author: Bruce K. Patterson
Author: Giovanni Parmigiani
Author: Piero Carninci
Author: Pankaj Barah
Author: Francisco A. Gómez Vela
Author: Altuna Akalin