Genetic analysis : genes, genomes, and networks in eukaryotes / Philip Meneely.Material type: TextPublication details: Oxford : Oxford University Press, 2014.Edition: Second editionDescription: xxvi, 552 pages : color illustrations ; 27 cmContent type:
- 576.5 23
- QH440 .M47 2014
- 2014 L-741
- QU 450
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|Compact Disk||Centeral Library Second Floor - Biotechnology||616.042 M.P.G 2014 (Browse shelf(Opens below))||Available||2 BIO||900046180|
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Revised edition of: Advanced genetic analysis : genes, genomes, and networks in eukaryotes.
Includes bibliographical references and index.
Machine generated contents note: Unit I Genes and genomes -- 1. The logic of genetic analysis -- Topic Summary -- Introduction -- 1.1. The logic of genetic analysis: a historical overview -- 1.2. Genes are the units of inheritance -- 1.3. Genes are found on chromosomes -- 1.4. One gene-one protein -- Alternative splicing results in `one gene-many proteins' -- Some genes have RNA rather than a protein as their functional product -- 1.5. Genes consist of DNA -- Text Box 1.1 The analysis of gene expression: reporter genes -- New features in the structure and organization of genomes are emerging routinely -- Text Box 1.2 The analysis of gene expression: microarrays and genome-wide transcriptional analysis -- Gene families are extremely common in the genome -- Family relationships are detected from aligning the nucleotide or amino acid sequences -- Genomes of widely divergent species share many of the same genes -- The functions of some genes cannot be inferred from their sequence.
Contents note continued: 1.6. Summary: genetic analysis -- Chapter Capsule: Genetic analysis -- Further reading -- 2. Model organisms and their genomes -- Topic Summary -- Introduction -- 2.1. Model organisms: an overview -- Model organisms and human biology -- 2.2. The awesome power of yeast genetics -- Yeast is often grown as a haploid -- Transformation in yeast involves naturally occurring plasmids -- Protein trafficking is one example of a fundamental process in eukaryotic cells studied by genetic analysis In yeast -- 2.3. Caenorhabditis elegans: `amenable to genetic analysis' -- C. elegans has two sexes, but no females -- Nematodes have precisely denned cell lineages -- Transformation of C. elegans is done by microinjection -- Mutations and the cell lineage patterns are used together for genetic analysis in C. elegans -- These mutations identified two different cellular pathways involved in human cancers -- 2.4. Drosophila melanogaster. If you have to ask ...
Contents note continued: D. melanogaster has some peculiar features -- Transformation of Drosophila uses a transposable element -- Sex determination In Drosophila involves a pathway of alternative splicing -- Both universal and taxon-specific properties are revealed by sex determination in Drosophila -- 2.5. Arabidopsis thaliana: a weed with a purpose -- The Arabidopsis life cycle Is shorter than many flowering plants -- Transformation of Arabidopsis is done using Agrobacterium tumefaciens -- The brassinosteroid hormone biosynthetic and signaling pathways have been delineated by genetic analysis in Arabidopsis -- Mutations demonstrated the presence of brassinosteroid pathways in Arabidopsis -- 2.6. Mus musculus: mighty mouse -- 2.7. Five model organisms: a summary -- 2.8. Other useful model organisms -- Are more model organisms needed? -- Genetic analysis In non-model organisms -- Chapter Capsule: Model organisms -- Case Study 2.1 Regeneration in planaria -- Further reading.
Contents note continued: 3. Genomes, chromosomes, and epigenetics -- Topic Summary -- Introduction -- 3.1. Genomes, chromosomes, and epigenetics: an overview -- 3.2. An overview of eukaryotic gene expression -- Transcription initiates at specialized sites -- Initiation of transcription is regulated by promoters and enhancers -- Transcription pauses during elongation -- The primary transcript is modified and spliced -- Regulation of gene expression and protein activity also occurs post-transcriptionally -- Regulation by microRNAs is now observed for many genes in multicellular organisms -- Long non-coding RNAs are also widespread -- Not all annotated sequence elements are transcribed -- Gene regulation In a nutshell -- 3.3. Genome-wide methods to analyze sequence elements -- Microarrays record the expression profile of many genes simultaneously -- Microarrays can provide answers for other questions about gene structure and expression.
Contents note continued: Massively parallel or `next-generation' sequencing is much faster and less expensive than previous sequencing methods -- Text Box 3.1 Massively parallel or `next-generation' sequencing -- Large-scale sequencing is an alternative to microarrays -- None of the sequencing methods is designed to look at protein expression -- 3.4. Gene structure and gene expression: some examples -- 3.5. Chromatin structure -- Histones and the nucleosome core particle -- Histone modifications and a histone code -- Histone modifications are targets for other chromatin-associated proteins -- DNA cytosine methylation is a regulatory mechanism in many species -- 3.6. Methods to annotate chromatin -- DNase hypersensitive sites were among the first indications that chromatin structure affected gene expression and function -- Chromatin immunoprecipitation (ChIP) is a protein-based approach to survey genome-wide binding sites.
Contents note continued: Bisulfite sequencing is used to identify DNA methylation sites genome-wide -- Formaldehyde-assisted isolation of regulatory elements (FAIRE) detects regions with few nucleosomes -- 3.7. Chromatin structure and functions: some examples -- Transcribed genes and replication origins have characteristic but different chromatin signatures -- Regulatory regions are identified by transcription-factor binding -- HOT regions are an unexpected and unexplained feature of metazoan genomes that were found during the ENCODE projects -- Heterochromatin re-examined -- 3.8. Limitations of the ENCODE-related projects -- Chapter Capsule: Genomes, chromosomes, and epigenetics -- Case Study 3.1 Imprinting -- Further reading -- Unit II Genes and mutations -- 4. Identifying and classifying mutants -- Topic Summary -- 4.1. Finding mutations: an overview -- Genetic analysis can begin with a mutant or with a gene -- 4.2. Producing mutations.
Contents note continued: Chemical mutagens modify or replace nucleotide bases -- Radiation induces chromosome breaks and structural rearrangements -- Insertional mutagens are among the most useful laboratory mutagens -- Transposable elements are effective mutagens for inducing single gene mutations -- A summary of mutagenic effects -- 4.3. Finding mutations -- `Genetic tricks' are simply well-established principles of genetics -- Selections can be used to reduce the number of individuals that have to be examined -- Selection can be done using linked lethal mutations -- Lethal mutations can still be maintained in culture -- Text Box 4.1 Conditional mutations and the time of gene activity -- Balanced heterozygotes -- Mutations can affect more than one phenotype, or may not produce a mutant phenotype -- Text Box 4.2 Balancer chromosomes and genetic screens -- 4.4. Mapping genes -- A mutant can be mapped using recombination -- Text Box 4.3 Mapping genes in humans.
Contents note continued: A mutation can be mapped using deletions and duplications -- 4.5.Complementation tests to assign mutants to genes -- Complementation tests define a gene but complementation tests are not perfect -- Complementation tests can be used to screen for new mutant alleles of a gene -- The total number of genes that could be found by a mutagenesis procedure can be estimated by statistical methods -- Text Box 4.4 Estimating the number of genes -- Gene names are usually but not always based on mutant phenotypes -- 4.6. Classifying mutants -- Recessive mutations generally have a loss of or a reduction In the normal function of the gene -- Text Box 4.5 Dominance and the evolution of gene expression -- Null and hypomorphic alleles of a gene can be placed in an allelic series -- Conditional mutations are often hypomorphs -- Dominant mutants generally arise from over-producing a normal function -- Mutations producing an unexpected function can also be dominant.
Contents note continued: Haplo-insufficient mutants are dominant and define dose-dependent genes -- Summarizing the effects of mutations with an analogy -- 4.7. Summary, a mutant provides a crucial starting part for genetic analysis of a biological process -- Chapter Capsule: Classifying mutants -- Case Study 4.1 Find a mutant: segmentation in Drosophila embryos -- Further reading -- 5. Connecting a phenotype to a DNA sequence -- Topic Summary -- Introduction -- 5.1. Cloning genes: an overview -- 5.2. Identifying candidate genes: map position -- Locating a gene with respect to cloned markers and genes -- Cloning the region between two molecular markers -- 5.3. Identifying candidate genes: expression pattern -- Genes can be cloned based on their expression patterns -- RNA-based expression cloning -- An intermediate method: homology-based cloning -- Text Box 5.1 Cloning genes based on homology -- 5.4. Evaluating the candidate genes: complementation and mutations.
Contents note continued: Complementation testing or transformation rescue can be used in model organisms to identify the best candidate gene -- Mutations, or DNA sequence variations, are the most widely used property to confirm the relationship between the cloned gene and the mutant phenotype -- Tiling arrays allow direct comparisons between wild-type and mutant organisms -- 5.5. Direct searches for causative mutations: exome sequencing -- Exome sequencing -- Miller syndrome was among the first rare genetic diseases to be analyzed by exome sequencing -- Refinements to the procedure used for Miller syndrome have made exome sequencing even more powerful -- Relatively few individuals are needed to Identify the gene, even If no other information is available -- Diagnosis can be done more accurately -- Different disorders can be caused by the same gene -- Similar disorders can arise from distinct genes -- Exome sequencing is also used for somatic mutations.
Contents note continued: 5.6. Summary: connecting a phenotype to a DNA sequence -- Chapter Capsule: Cloning a gene -- Case Study 5.1 Positional cloning of the cystic fibrosis gene in humans -- Case Study 5.2 Cloning the patched gene from Drosophila -- References and further reading -- 6. Finding mutant phenotypes for cloned genes -- Topic Summary -- Introduction -- 6.1. An overview of reverse genetics: targeted gene disruptions and targeted gene replacements -- Reverse genetics addresses different questions than forward genetics -- In reverse genetics in yeast and mice, a cloned gene is inserted at a specific site in the genome -- 6.2. Targeted gene insertions in yeast -- 6.3. Targeted gene knockouts in the mouse -- Embryonic stem cells can give rise to mouse embryos -- ES cells can be targeted for gene insertion using both positive and negative selections -- The ability to target gene knockouts has changed mouse genetics.
Contents note continued: Genes can also be targeted by Cre-lox or other recombination systems -- Tissue-specific mutations can be made by regulating Cre expression -- Knock-in mutations replace the coding region with an altered function -- Knockouts and knock-ins have proved to be versatile tools In mouse genetics -- I hear you knocking ... -- 6.4. Summary: reverse genetics allows specific types of mutations to be made and analyzed -- Chapter Capsule: Reverse genetics -- Case Study 6.1 patched knockout mutations in mice -- Further reading -- 7. Genome-wide mutant screens -- Topic Summary -- Introduction -- 7.1. Genome-wide mutant screens: an overview -- Genome-wide screens identify new genes affecting well-described biological processes -- 7.2. Identifying the genes to be mutated -- 7.3. Disrupting and perturbing genes -- Genome-wide mutant screens In yeast have been performed by targeted gene disruption.
Contents note continued: Molecular bar codes allow identification of individual genes from among a pool -- Text Box 7.1 Molecular bar codes -- Gene disruptions are targeted by recombination -- 7.4. RNAi and large-scale mutant analysis -- The antecedents of RNAi lie in other experiments -- Text Box 7.2 Antisense experiments using morpholinos -- Text Box 7.3 MicroRNAs: a brief history -- The method to introduce dsRNA depends on the cells and organisms -- The mechanism of RNAI relies on normal cellular functions -- RNAi has some known limitations -- The problem of cross-reactivity -- 7.5. Screening for mutant phenotypes -- 7.6. Confirming the effects -- 7.7. Lessons from genome-wide screens -- 7.8. Summary: genome-wide screens attempt to identify phenotypes for every gene in the genome -- Chapter Capsule: Genome-wide screens -- Further reading -- Unit III Genes and populations -- 8. Genome-wide associations -- Topic Summary -- Introduction -- 8.1. Genome-wide associations: an overview.
Contents note continued: 8.2. Variation in the human genome -- Some locations in the human genome are observed to vary in predictable frequencies -- Different types of polymorphisms are found in the genome -- 8.3. The strategy of GWAS -- Polymorphisms have been compiled and assayed throughout the human genome -- Text Box 8.1 Detecting a SNP using microarrays -- The genome structure reflects human history -- Haplotypes and LD are conceptually similar to balancer chromosomes on a population scale -- Selection can have a larger effect than genetic drift on local population structures -- 8.4. The process of GWAS -- Microarrays can be used to assess common polymorphisms throughout the genome -- The Common Disease Common Variant hypothesis is used to find disease-gene associations -- 8.5. Summary: genome-wide associations -- Chapter Capsule: Genome-wide associations -- Further reading -- 9. Genetic analysis of complex traits -- Topic Summary -- Introduction.
Contents note continued: 9.1. Genetics of complex traits -- Complex traits do not follow simple inheritance patterns -- Quantitative complex traits have both genetic and environmental variance -- Twin studies have been used in humans to estimate genetic and environmental variance -- Text Box 9.1 Heritability -- 9.2. Genome-wide association studies -- Crohn's disease is an example of a complex trait -- GWAS have been used for hundreds of complex traits -- GWAS are now a common and powerful tool in human genetics -- 9.3. Limitations and the interpretation of GWAS -- Population stratification -- Gene deserts -- Gene deserts may not be functional deserts -- The missing heritability -- 9.4. Summary: genomes and disease gene identification -- Chapter Capsule: Complex traits -- References and further reading -- 10. Genetic analysis using natural variation -- Topic Summary -- Introduction -- 10.1. Genetic analysis using natural variation: an overview.
Contents note continued: 10.2. Evolutionary forces and natural variation -- Mutation is the original source of all natural genetic variation -- Migration and genetic drift are related to variation In population structure -- Selection has profound effects on shaping natural variation -- Selection can be detected from genome sequencing -- Adaptation is genetic variation seen In local populations -- 10.3. Flowering time adaptations in Arabidopsis -- The photoperiod depends on the transcription of the FT gene -- Vernalization requires inhibition of repressors of the flowering response -- Arabidopsis can be used to study changes in flowering times In other plants -- 10.4. Coat color adaptations in deer mice -- Coat color differences show a selective advantage -- Distinct coloration patterns correlated with specific changes at the Agouti locus -- 10.5. EDAR and the evolution of hair texture in humans -- EDAR has been under strong positive selection in some human populations.
Contents note continued: EDAR V370A also affects hair morphology in transgenic mice -- The adaptive advantage from EDAR V370A may arise from sweat glands -- 10.6. Summary: genetic analysis using natural variation -- Chapter Capsule: Natural variation -- Further reading -- Unit IV Genes and pathways -- 11. Using one gene to identify functionally related genes -- Topic Summary -- Introduction -- 11.1. Using one gene to find more genes involved in the same biological process: an overview -- 11.2. Using a mutant phenotype as a tool to find related genes -- Suppressors and enhancers modify the phenotype of another mutation -- Suppressor and enhancer gene nomenclature can be extremely confusing -- 11.3. Suppressor mutations: more similar to wild-type -- Suppressor mutations can be either intragenic or extragenic -- Text Box 11.1 General strategy for mapping a suppressor -- Extragenic suppressors fall into three main functional classes.
Contents note continued: Interactional suppressors are specific to both the gene and the allele -- Informational suppressors affect the molecular lesion in the specific allele but not the function of the gene -- Gene-specific suppressors affect many different alleles of a gene, but no or only a few other genes -- High-copy suppression involves the use of a wild-type cloned gene -- Text Box 11.2 Suppression of dominant mutations in Arabidopsis -- 11.4. Synthetic enhancers: modifying mutations that make a mutant phenotype more severe -- Synthetic enhancers are mutations that exacerbate the effect of the original mutation -- Synthetic enhancement can involve duplicate or paralogous genes -- Synthetic enhancement and bypass suppression can be indications of the same effect -- Synthetic enhancement can involve parallel biological pathways -- Non-allelic non-complementation Is a type of synthetic enhancement that occurs when both mutations are heterozygous.
Contents note continued: Text Box 11.3 Non allelic non-complementation -- 11.5. Summary: finding related genes using mutant phenotypes -- 11.6. Using a cloned gene to find interacting genes -- Yeast two-hybrid assays use a genetic approach to discover protein-protein interactions -- AY2H assay was used to examine brassinosteroid signaling In Arabidopsis -- Co-Immunoprecipitation is a physiological standard for protein-protein interactions -- The power of protein interactions: an example from plant pathology -- 11.7. Summary: finding more genes -- Chapter Capsule: Finding related genes -- Case Study 11.1 Genetic analysis of spindle morphogenesis in budding yeast -- Further reading -- 12. Epistasis and genetic pathways -- Topic Summary -- Introduction -- 12.1. Epistasis and genetic pathways: an overview -- The logic of epistasis requires close attention -- 12.2.Combining mutants and molecular expression assays.
Contents note continued: A ptc mutation changes the expression pattern of other proteins expressed in the wing -- 12.3. Epistasis and genetic pathways -- General amino acid control in budding yeast Is regulated by two types of genes -- Sex determination in C. elegans involves both positive and negative genetic interactions -- Negative pathways involve mutations with opposite phenotypes -- Positive pathways Involve mutations with similar phenotypes -- Using subtle differences in the mutant phenotype -- Illustrating positive pathways using non-biological examples -- Returning to somatic sex determination -- Using a dominant allele to confirm the results -- The pathway inferred from epistasis informs further experiments to understand sex determination -- Epistatic analysis has known limitations -- 12.4.A branched pathway involving dauer larva formation in C elegans -- Suppression analysis was used to find additional genes affecting dauer formation.
Contents note continued: Interactions among the dauer mutations are not always simple to interpret -- Synthetic enhancers support the presence of two pathways -- The two pathways share some common steps -- 12.5. The pathways unveiled -- Chapter Capsule: Epistasis and genetic pathways -- Case Study 12.1 Epistasis and the patched pathway -- Further reading -- 13. Pathways, networks, and) systems -- Topic Summary -- Introduction -- 13.1. Pathways, networks, and systems: an overview -- Some properties in a network emerge from the interactions of its components -- 13.2. Properties of networks: background and definitions -- The network can be used to infer the functions of its components -- Paths, degree distributions, and hubs describe biological networks -- 13.3. The interactions between transcription factors and DNA sequences -- The interactions between transcription factors and their binding sites are often found by ChIP experiments.
Contents note continued: Genome-wide ChIP assays so far have given the most insights into transcriptional regulation -- A yeast one-hybrid assay is a gene-centered approach to identify the transcription factors that bind to specific regulatory regions -- Different types of transcriptional regulation are seen among the networks -- 13.4.microRNA and mRNA interactions -- Transcriptional regulation of microRNA might reflect timing of events -- 13.5. The interactions between proteins -- Interactomes reveal possible functions for unknown genes -- The protein interactome is also characterized by hubs -- Types of hubs can be distinguished by the times of their interactions -- 13.6. Gene regulation networks -- The secretory pathway shows many of the basic principles of synthetic Interactions -- A nearly complete and unbiased set of gene interactions reveals the genetic landscape of the yeast cell -- 13.7. Summary, pathways, networks, and systems.
Contents note continued: Chapter Capsule: Pathways, networks, and systems -- Case Study 13.1 A systems analysis of TGF-[beta] signaling in C. elegans -- References and further reading -- 14. Genes, systems, and phenotypes -- Topic Summary -- Introduction -- 14.1. Genes, systems, and phenotypes: an overview -- 14.2. Interpreting mutant phenotypes -- Mutant phenotypes tend to affect particular developmental stages and tissues -- Terminal phenotypes might not indicate the time or location of gene activity -- Most genes have pleiotropic effects -- The mutant phenotype may not reflect when and where the gene is expressed -- 14.3. Types of mutation -- Dominant mutations have not been widely studied on a genome-wide scale -- 14.4. Gene expression levels and biological noise -- How much change in gene expression is biologically significant? -- The level of biological noise varies among genes affecting different processes -- Biological noise has several different origins.
Contents note continued: The phenotypic consequences of biological noise -- 14.5. Genetic regulatory networks, robustness, and risk factors: a speculation -- Genetic interactions could affect our risk for many genetic diseases -- Chapter Capsule: Genes, systems, and phenotypes -- Further reading.
How do we know what role a particular gene has? How do some genes control the expression of others? How do genes interact to form gene networks? This text explores these fascinating questions, detailing how our understanding of key genetic phenomena can be used to understand biological systems. Opening with a brief overview of key genetic principles, model organisms, and epigenetics, the book goes on to explore the use of gene mutations and the analysis of gene expression and activity. A discussion of the genetic structure of natural populations follows, before the interaction of genes during suppression and epistasis, how we study gene networks, and personalized genomics are considered. Drawing on the latest experimental tools, including microarrays, RNAi screens, and bioinformatics approaches, this book provides a state-of-the-art review of the field in a truly student-friendly manner. It uses extended case studies and text boxes to augment the narrative, taking the reader to the forefront of contemporary research with exceptional clarity.