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About this product
Product Identifiers
PublisherHumana Press
ISBN-101588297349
ISBN-139781588297341
eBay Product ID (ePID)60752549
Product Key Features
Number of PagesXiv, 337 Pages
Publication NameGene Function Analysis
LanguageEnglish
Publication Year2007
SubjectLife Sciences / Cell Biology, Life Sciences / Biochemistry, Life Sciences / Genetics & Genomics, Genetics
TypeTextbook
Subject AreaScience, Medical
AuthorMichael F. Ochs
SeriesMethods in Molecular Biology Ser.
FormatHardcover
Dimensions
Item Height0.4 in
Item Weight24.9 Oz
Item Length9 in
Item Width6 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2007-925518
ReviewsFrom the reviews:"'Gene Function Analysis' is volume 408 of the 'Methods in Molecular Biology' series. The aim of the book is to provide both in silico and laboratory methods for the functional analysis of the large amount of gene expression data generated from high throughput technologies such as microarrays. … This book would certainly be a useful addition to a laboratory interested in assessing gene function through both bioinformatic and 'wet' approaches. Definitely one for the more (toxico)genomically challenged amongst us." (James Sidaway, British Toxicology Society Newsletter, Summer, 2008), From the reviews: "'Gene Function Analysis' is volume 408 of the 'Methods in Molecular Biology' series. The aim of the book is to provide both in silico and laboratory methods for the functional analysis of the large amount of gene expression data generated from high throughput technologies such as microarrays. ... This book would certainly be a useful addition to a laboratory interested in assessing gene function through both bioinformatic and 'wet' approaches. Definitely one for the more (toxico)genomically challenged amongst us." (James Sidaway, British Toxicology Society Newsletter, Summer, 2008)
Dewey Edition22
Series Volume Number408
Number of Volumes1 vol.
IllustratedYes
Dewey Decimal572.86
Table Of ContentComputational Methods I.- Gene Function Inference From Gene Expression of Deletion Mutants.- Association Analysis for Large-Scale Gene Set Data.- Estimating Gene Function With Least Squares Nonnegative Matrix Factorization.- From Promoter Analysis to Transcriptional Regulatory Network Prediction Using PAINT.- Prediction of Intrinsic Disorder and Its Use in Functional Proteomics.- Computational Methods II.- Sybil: Methods and Software for Multiple Genome Comparison and Visualization.- Estimating Protein Function Using Protein-Protein Relationships.- Bioinformatics Tools for Modeling Transcription Factor Target Genes and Epigenetic Changes.- Mining Biomedical Data Using MetaMap Transfer (MMTx) and the Unified Medical Language System (UMLS).- Statistical Methods for Identifying Differentially Expressed Gene Combinations.- Experimental Methods.- Gene Function Analysis Using the Chicken B-Cell Line DT40.- Design and Application of a shRNA-Based Gene Replacement Retrovirus.- Construction of Simple and Efficient DNA Vector-Based Short Hairpin RNA Expression Systems for Specific Gene Silencing in Mammalian Cells.- Selection of Recombinant Antibodies From Antibody Gene Libraries.- A Bacterial/Yeast Merged Two-Hybrid System.- A Bacterial/Yeast Merged Two-Hybrid System.- Engineering Cys2His2 Zinc Finger Domains Using a Bacterial Cell-Based Two-Hybrid Selection System.
SynopsisWith the advent of high-throughput technologies following completion of the human genome project and similar projects in model organisms, the number of genes of interest has expanded and the traditional methods for gene function analysis cannot achieve the throughput necessary for large-scale exploration. ?Gene Function Analysis? brings together a number of techniques that have developed recently for looking at gene function, including computational, biochemical and biological methods and protocols., This volume of Methods in Molecular Biology focuses on techniques to determine the function of a gene. Traditionally, the function of a gene was determined following cloning, which provided its DNA sequence and an ab- ity to modify this sequence. Experiments were performed that looked for p- notypic changes in a cell line or model organism following modifications to the sequence, knocking out of the gene, or enhancing expression of the gene. In the 1990's, the growing sequence databases and the BLAST algorithm provided additional power by allowing identification of genes with known function that had similar sequences and potentially similar molecular mechanisms. On the experimental side, methods, such as two-hybrid screening that could directly determine the partners of specific proteins and even the domains of interaction, came into widespread use. With the advent of high-throughput technologies following completion of the human genome project and similar projects in model organisms, the n- ber of genes of interest has expanded and the traditional methods for gene fu- tion analysis cannot achieve the throughput necessary for large-scale exploration. Although computational tools such as BLAST remain a good point of departure, it is often the case that a gene that appears interesting in a hi- throughput experiment shows no obvious similarity to a gene of known fu- tion. In addition, when BLAST does find a similar gene, the process has often only begun.