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Gene discovery for disease models /
This book provides readers with new paradigms on the mutation discovery in the postgenome era. The completion of human and other genome sequencing, along with other new technologies, such as mutation analysis and microarray, has dramatically accelerated the progress in positional cloning of genes fr...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
Wiley,
©2011.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 037 | |a 10.1002/9780470933947 |b Wiley InterScience |n http://www3.interscience.wiley.com | ||
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| 245 | 0 | 0 | |a Gene discovery for disease models / |c edited by Weikuan Gu. |
| 260 | |a Hoboken, N.J. : |b Wiley, |c ©2011. | ||
| 300 | |a 1 online resource (xiii, 537 pages, 15 unnumbered pages of plates) : |b color illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |6 880-01 |a Introduction : gene discovery-from positional cloning to genomic cloning -- High throughput gene expression analysis and the identification of expression QTLs -- DNA methylation in the pathogenesis of autoimmunity -- Ccell-based analysis with microfluidic chip -- Missing dimension : protein turnover rate measurement in gene discovery -- Bioinformatics tools for the prediction of gene function -- Determination of genomic locations of targeted genetic loci -- Mutation discovery using high throughput mutation screening technology -- Candidate screening through gene expression profile -- Candidate screening through high-density SNP array -- Gene discovery through direct genome sequencing -- Candidate screening through bioinformatics tools -- Using an integrative strategy to identify mutations -- Determination of the function of a mutant in a gene -- Confirmation of a mutation by multiple molecular approaches -- Confirmation of a mutation by microRNA -- Confirmation of function of a gene by translational approaches -- Confirmation of single nucleotide mutations -- Initial identification and confirmation of a QTL gene -- Gene discovery of crop diseases in the post genome era -- Impact of whole genome genetic element analysis on gene discovery of disease models -- Impact of whole genome protein analysis on gene discovery of disease models. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a This book provides readers with new paradigms on the mutation discovery in the postgenome era. The completion of human and other genome sequencing, along with other new technologies, such as mutation analysis and microarray, has dramatically accelerated the progress in positional cloning of genes from mutated models. In 2002, the Mouse Genome Sequencing Consortium stated that âThe availability of an annotated mouse genome sequence now provides the most efficient tool yet in the gene hunter's toolkit. One can move directly from genetic mapping to identification of candidate genes, and the experimental process is reduced to PCR amplification and sequencing of exons and other conserved elements in the candidate interval. With this streamlined protocol, it is anticipated that many decadesold mouse mutants will be understood precisely at the DNA level in the near future.â? The implication of such a statement should be similar to the identification of mutated genes from human diseases and animal models, when genome sequencing is completed for them. More than five years have passed, but genes in many human diseases and animal models have not yet been identified. In some cases, the identification of the mutated genes has been a bottleneck, because the genetic mechanism holds the key to understand the basis of the diseases. However, an integrative strategy, which is a combination of genetic mapping, genome resources, bioinformatics tools, and high throughput technologies, has been developed and tested. The classic paradigm of positional cloning has evolved with completely new concepts of genomic cloning and protocols. This book describes new concepts of gene discovery in the postgenome era and the use of streamlined protocols to identify genes of interest. This book helps identify not only large insertions/deletions but also single nucleotide mutations or polymorphisms that regulate quantitative trait loci (QTL). | ||
| 546 | |a English. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Medical genetics. | |
| 650 | 0 | |a Mutation (Biology) | |
| 650 | 0 | |a Genomics. | |
| 650 | 0 | |a Genetic disorders. | |
| 650 | 1 | 2 | |a Genetic Association Studies |x methods |
| 650 | 2 | 2 | |a Models, Genetic |
| 650 | 2 | 2 | |a Mutation |
| 650 | 2 | |a Genetics, Medical | |
| 650 | 2 | |a Genomics | |
| 650 | 2 | |a Genetic Diseases, Inborn | |
| 650 | 6 | |a Génétique médicale. | |
| 650 | 6 | |a Mutation (Biologie) | |
| 650 | 6 | |a Génomique. | |
| 650 | 6 | |a Maladies génétiques. | |
| 650 | 7 | |a HEALTH & FITNESS |x Diseases |x Genetic. |2 bisacsh | |
| 650 | 7 | |a MEDICAL |x Genetics. |2 bisacsh | |
| 650 | 7 | |a Genetic disorders |2 fast | |
| 650 | 7 | |a Genomics |2 fast | |
| 650 | 7 | |a Medical genetics |2 fast | |
| 650 | 7 | |a Mutation (Biology) |2 fast | |
| 700 | 1 | |a Gu, Weikuan. | |
| 776 | 0 | 8 | |i Print version: |t Gene discovery for disease models. |d Hoboken, N.J. : Wiley, ©2011 |w (DLC) 2010028355 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=661512 |z Texto completo |
| 880 | 0 | 0 | |6 505-01/(S |g Machine generated contents note: |g 1. |t Gene Discovery: From Positional Cloning to Genomic Cloning / |r Daniel Goldowitz -- |g 1.1. |t Concept of Classic Positional Cloning -- |g 1.2. |t Concept of Gene Discovery in the Post-Genome Era -- |g 1.3. |t Strategies for Gene Discovery in the Post-Genome Era -- |g 1.4. |t Future Direction -- |g 1.5. |t References -- |g 2. |t High-Throughput Gene Expression Analysis and the Identification of Expression QTLs / |r Klaus Schughart -- |g 2.1. |t Concepts in High-Throughput Gene Expression Analysis -- |g 2.2. |t Technologies of High-Throughput Gene Expression Analysis -- |g 2.2.1. |t Gene Expression Microarrays -- |g 2.2.2. |t One-Channel Versus Two-Channel Microarrays -- |g 2.2.3. |t Oligonucleotide Versus Spotted Microarrays -- |g 2.2.4. |t Whole-Transcript Arrays -- |g 2.2.5. |t Genome Tiling Arrays -- |g 2.2.6. |t MicroRNA Arrays -- |g 2.3. |t Protocols -- |g 2.3.1. |t Image Analysis -- |g 2.3.2. |t Normalization -- |g 2.3.3. |t Quality Control -- |g 2.4. |t Applications and Limitations -- |g 2.4.1. |t Identification of Expression QTL and Gene Regulatory Networks -- |g 2.4.2. |t Identification of Differentially Expressed Genes -- |g 2.4.3. |t Identification of Cell-Type-Specific Genes -- |g 2.4.4. |t Determination of the Downstream Effects of a Mutation -- |g 2.4.5. |t Determination of the Downstream Effects of a Signaling Molecule -- |g 2.4.6. |t Predicting Vaccine Efficacy -- |g 2.4.7. |t Determination of Host Responses after Infection -- |g 2.4.8. |t Limitations -- |g 2.5. |t Questions and Answers -- |g 2.6. |t Acknowledgments -- |g 2.7. |t References -- |g 3. |t DNA Methylation in the Pathogenesis of Autoimmunity / |r Cong-Yi Wang -- |g 3.1. |t Introduction -- |g 3.2. |t General Information for DNA Methylation in Mammals -- |g 3.3. |t DNA Methyltransferases and Methyl-CpG-Binding Domain (MBD) Proteins -- |g 3.3.1. |t DNA Methyltransferases -- |g 3.3.2. |t MBD Proteins -- |g 3.4. |t DNA Methylation in T and B Cell Development -- |g 3.4.1. |t DNA Methylation of IFN-γ Locus in Th1 Cell Development -- |g 3.4.2. |t DNA Methylation of Th2 Cytokine Locus in Th2 Cell Development -- |g 3.4.3. |t DNA Methylation in Regulatory T Cell and Th17 Development -- |g 3.4.4. |t DNA Methylation in B Cell Maturation and Functionality -- |g 3.5. |t Implication of DNA Methylation in Autoimmune Diseases -- |g 3.5.1. |t DNA Methylation in Systemic Lupus Erythematosus -- |g 3.5.2. |t DNA Methylation in Rheumatoid Arthritis -- |g 3.5.3. |t DNA Methylation in Type 1 Diabetes -- |g 3.6. |t Common Technological Approaches for Assay of DNA Methylation -- |g 3.6.1. |t Methylation-Specific PCR -- |g 3.6.2. |t Bisulfite PCR -- |g 3.6.3. |t Arbitrary Primed PCR -- |g 3.6.4. |t Methylated DNA Immunoprecipitation Chip -- |g 3.7. |t Summary -- |g 3.8. |t Acknowledgments -- |g 3.9. |t References -- |g 4. |t Cell-Based Analysis with Microfluidic Chip / |r Zhao Long -- |g 4.1. |t Introduction -- |g 4.2. |t Fabrication of the Microfluidic Chip and Cell Culture -- |g 4.2.1. |t Fabrication of the Microfluidic Chip -- |g 4.2.2. |t Cell Culture and Analysis -- |g 4.3. |t Application of the Cell-Based Microfluidic Chip -- |g 4.3.1. |t Genomic Analysis on Chip -- |g 4.3.2. |t Protein Analysis on Chip -- |g 4.3.3. |t Analysis of Chemotherapy Resistance in Tumor Cells -- |g 4.4. |t Conclusions and Future Prospects -- |g 4.5. |t Acknowledgments -- |g 4.6. |t References -- |g 5. |t Missing Dimension: Protein Turnover Rate Measurement in Gene Discovery / |r Gary Guishan Xiao -- |g 5.1. |t Protein Turnover as Significant Missing Dimension in Gene Function and Discovery -- |g 5.2. |t Determination of the Rate of Turnover of Specific Proteins -- |g 5.3. |t Future Direction -- |g 5.4. |t Questions and Answers -- |g 5.5. |t Acknowledgments -- |g 5.6. |t References -- |g 6. |t Bioinformatics Tools for Gene Function Prediction / |r Yan Cui -- |g 6.1. |t Gene Ontology: Description of Gene Function with Controlled and Structured Vocabulary -- |g 6.2. |t Sequence-Based Function Prediction -- |g 6.2.1. |t Annotation Transfer by Sequence Homology -- |g 6.2.2. |t Phylogenomic Methods for Function Prediction -- |g 6.2.3. |t Function Prediction Using Sequence Motif -- |g 6.3. |t Structure-Based Function Prediction -- |g 6.3.1. |t Protein Structure Comparison for Function Prediction -- |g 6.3.2. |t Predicting Functional Sites on Protein Surface -- |g 6.4. |t Function Prediction Using Integrated Data -- |g 6.4.1. |t Types of Data for Facilitating Function Prediction -- |g 6.4.2. |t Integrative Methods for Function Prediction -- |g 6.5. |t Questions and Answers -- |g 6.6. |t References -- |g 7. |t Determination of Genomic Locations of Target Genetic Loci / |r Bo Chang -- |g 7.1. |t Concepts of Genomic Location -- |g 7.2. |t Genetic Loci of Eye Diseases in Human and Animal Models -- |g 7.2.1. |t Discovery of Eye Diseases -- |g 7.2.2. |t Determination of the Mode of Inheritance -- |g 7.2.3. |t Single Versus Multiple Gene Traits -- |g 7.2.4. |t Examples -- |g 7.3. |t Genetic Markers for the Localization of Disease Loci -- |g 7.3.1. |t Background -- |g 7.3.2. |t Types of Genetic Markers -- |g 7.3.3. |t Uses of Genetic Markers -- |g 7.4. |t Defining Genomic Regions of Disease Loci Using Genetic Markers -- |g 7.4.1. |t Defining a Disease Locus Using Microsatellite Markers -- |g 7.4.2. |t Defining a Disease Locus Using SNP Markers -- |g 7.4.3. |t When Markers Are Missing in the Genome -- |g 7.4.4. |t Limitations and Alternative Procedures -- |g 7.5. |t Gene Identification Based on a Defined Genomic Region -- |g 7.5.1. |t Collection of Genetic Elements within a Targeted Region -- |g 7.5.2. |t Gene Screening and Discovery -- |g 7.6. |t Questions and Answers -- |g 7.7. |t Acknowledgments -- |g 7.8. |t References -- |g 8. |t Mutation Discovery Using High-Throughput Mutation Screening Technology / |r Jia Zhang -- |g 8.1. |t Introduction -- |g 8.2. |t Classical Technologies for High-Throughput Mutation Analysis -- |g 8.2.1. |t Hybridization-Based Assays -- |g 8.2.2. |t Configuration-Based Assays -- |g 8.2.3. |t Primer Extension-Based Assays -- |g 8.2.4. |t Sequencing-Based Assays -- |g 8.3. |t Microarray-Based Mutation Detection -- |g 8.4. |t Miscellaneous Technological Advances for Mutation Detection -- |g 8.4.1. |t Restriction Fragment Length Polymorphism -- |g 8.4.2. |t Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry -- |g 8.4.3. |t TaqMan Assay and Real Time PCR -- |g 8.5. |t Summary -- |g 8.6. |t Acknowledgments -- |g 8.7. |t References -- |g 9. |t Candidate Screening through Gene Expression Profile / |r Michal Korostynski -- |g 9.1. |t Concepts in High-Throughput Gene Expression Analysis -- |g 9.2. |t High-Throughput Gene Expression Analysis Technologies -- |g 9.2.1. |t Microarrays -- |g 9.2.2. |t Sequence-Based Sampling Methods -- |g 9.2.3. |t Multigene Quantitative PCR Assays -- |g 9.3. |t Applications and Limitations -- |g 9.4. |t Microarrays: Protocols in Gene Discovery -- |g 9.4.1. |t Before a Microarray Experiment -- |g 9.4.2. |t RNA Quality -- |g 9.4.3. |t Experimental Design -- |g 9.4.4. |t Data Analysis -- |g 9.5. |t Gene Expression Profiling Data Analysis -- |g 9.5.1. |t Microarray Data Preprocessing -- |g 9.5.2. |t Candidate Gene Selection -- |g 9.5.3. |t Identification and Characterization of Co-Expressed Gene Transcription Modules -- |g 9.5.4. |t Advance Transcript and Candidate Gene Analyses -- |g 9.6. |t Questions and Answers -- |g 9.7. |t Acknowledgments -- |g 9.8. |t References -- |g 10. |t Candidate Screening through High-Density SNP Array / |r Kin-Chong Lau -- |g 10.1. |t Introduction -- |g 10.1.1. |t What Is High-Density SNP Array Technology-- |g 10.2. |t Platforms and Protocols of SNP Microarray -- |g 10.2.1. |t First Platform and Protocol of a High-Density SNP Array -- |g 10.2.2. |t Second Generation of High-Density SNP Array Platform and Protocol -- |g 10.2.3. |t Much Advanced Platform and Protocol of an Ultra-High-Density SNP Array -- |g 10.3. |t How a High-Density SNP Array Can Be Used to Localize a Possible Disease Loci -- |g 10.3.1. |t LOH in Cancer -- |g 10.3.2. |t Copy-Neutral LOH in Genetic Diseases due to Consanguinity -- |g 10.3.3. |t LOH in Other Clinical Cytogenetics Analysis -- |g 10.4. |t Discussion -- |g 10.5. |t References -- |g 11. |t Gene Discovery by Direct Genome Sequencing / |r Mainak Sengupta -- |g 11.1. |t Introduction -- |g 11.2. |t Gene Discovery by Direct Genome Sequencing -- |g 11.2.1. |t Discovery of Mutations in Mendelian Diseases -- |g 11.2.2. |t Discovery of QTL or Single Nucleotide Mutations -- |g 11.3. |t Applications and Protocols -- |g 11.3.1. |t Identification and Capturing of the Targeted Genomic Region -- |g 11.3.2. |t Selection of Suitable Platform -- |g 11.4. |t Limitations of Direct Genome Sequencing -- |g 11.5. |t References -- |g 12. |t Candidate Screening through Bioinformatics Tools / |r Wei Zhao -- |g 12.1. |t Introduction -- |g 12.2. |t Computing Environment: R and Bioconductor -- |g 12.3. |t Bioinformatic Databases -- |g 12.3.1. |t Literature Database: PubMed -- |g 12.3.2. |t Biological Ontology Databases -- |g 12.3.3. |t Protein-Protein Interaction |
| 880 | 0 | 0 | |t Databases -- |g 12.4. |t Bayesian Network to Analyze Expression Data: NATbox -- |g 12.5. |t Weighted Gene Co-Expression Network Analysis -- |g 12.5.1. |t Generation of Weighted Gene Co-Expression Network -- |g 12.5.2. |t Detection of Modules -- |g 12.5.3. |t Define Measures of Gene Significance and Module Relevance -- |g 12.5.4. |t Functional Enrichment Studies of Gene Modules -- |g 12.5.5. |t Relating Intramodular Connectivity to Gene Significance -- |g 12.5.6. |t Network-Based Screening Strategy -- |g 12.5.7. |t Brain Tumor Example -- |g 12.6. |t In Silico Screening of Candidate Genes -- |g 12.6.1. |t Input Gene List Preparation -- |g 12.6.2. |t Gene Set Enrichment Analysis -- |g 12.6.3. |t Protein-Protein Interaction Network Analysis -- |g 12.6.4. |t PID Example -- |g 12.6.5. |t Other Bioinformatics Tools -- |g 12.7. |t Future Directions -- |g 12.8. |t Questions -- |g 12.9. |t Acknowledgments -- |g 12.10. |t References -- |g 13. |t Using an Integrative Strategy to Identify Mutations / |r Weikuan Gu -- |g 13.1. |t Introduction -- |g 13.2. |t Identifying Possible Candidate Genes within the Genome Region of Interest -- |g 13.2.1. |t Selection of Genomic Database -- |g 13.2.2. |t Identification of All Genes and Other Genetic Elements -- |g 13.3. |t Identification of Possible Nucleotide Differences/Mutations within the GRI -- |g 13.3.1. |t Confirmation of Genomic Mutations in cDNA. |
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