Gene Quantification

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.

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.

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.

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.

Focusing on Saccharomyces cerevisiae, the second edition of Yeast Gene Analysis represents a major reworking of the original edition, with many completely new chapters and major revisions to all previous chapters. Originally published shortly after completion of the yeast genome sequence, the new edition covers many of the major genome-wide strategies that have been developed since then such as microarray analysis of transcription, synthetic gene array studies, protein microarrays and chemical genetic approaches. It represents a valuable resource for any research laboratory using budding yeast as their experimental system in which to identify new yeast gene functions. The chapters are written in a readable style with useful background information, technical tips and specific experimental protocols included as appropriate, enabling both the novice and the experienced yeast researcher to adopt new procedures with confidence. New chapters on: Strain construction; genome-wide two-hybrid approaches; use of microarrays for transcript analysis; real-time analysis of chromosome behaviour and FRET; synthetic gene array technology and protein arrays; chemical genomics and yeast prions; RNA gene analysis and mitochondrial gene function analysis; phylogenetic footprinting; discovering human gene function and predicting yeast gene function

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.

This practical compendium provides clinical scientists with an essential guide to the basic techniques of molecular medicine. It serves as a laboratory manual and a source of reference. It is suitable for those wishing to perform basic semi-quantitative experiments such as Northern or Southern blots and also those wishing to undertake more specialised genetic manipulations such as gene cloning, expression and creation of DNA libraries. It will give clinical scientists a unique insight into the potential of these techniques. As stated by Sir David Weatherall: 'It should be of great value to both established research workers and young scientists coming into the field for the first time. It deserves every success.'