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genomics --- Gene expression --- Genomics --- Expression génique --- Génomique --- Gene expression. --- Genomics. --- Genome research --- Genomes --- Molecular genetics --- Genes --- Genetic regulation --- Research --- Expression --- Genetics

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Gene expression --- Genetic regulation --- Gene Expression. --- Gene Expression Regulation. --- Gene expression. --- Genetic regulation. --- Gene regulation --- Genes --- Expression Regulation, Gene --- Regulation, Gene Action --- Regulation, Gene Expression --- Gene Action Regulation --- Regulation of Gene Expression --- Expression, Gene --- Expressions, Gene --- Gene Expressions --- Gene expression regulation --- Regulation --- Expression --- Biosynthesis --- Cellular control mechanisms --- Molecular genetics --- RNAi Therapeutics --- Gene Regulatory Networks --- Gene Expression Profiling --- Gene Expression --- Gene Expression Regulation --- Expression génique --- Péridiodiques. --- Régulation génétique --- Expression génique. --- Régulation génétique.

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Genomics --- Genes, Structural --- Genetics, Biochemical --- DNA --- Genomes --- Gene expression --- ADN --- Génomes --- Expression génique --- Structure --- Périodiques. --- Deoxyribonucleic acid --- Desoxyribonucleic acid --- Thymonucleic acid --- TNA (Nucleic acid) --- Genes --- Expression --- Gene expression. --- Genomes. --- Structure. --- Deoxyribose --- Nucleic acids --- Genetic regulation --- Genetics --- Haploidy --- Periodicals. --- Genòmica --- Expressió gènica --- Genètica molecular --- Genètica --- Génomes --- Expression génique --- Périodiques.

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General biophysics --- General biochemistry --- Molecular biology --- Gene expression --- Genetic code --- Expression génique --- Code génétique --- Periodicals --- Périodiques --- Biochemistry --- Genetic Code. --- Gene Expression. --- Code génétique --- Expression génique --- Génétique biochimique. --- Périodiques. --- #ANTILTPNE9510 --- Life Sciences --- Chemistry --- General and Others --- Life Sciences. --- biochemie --- Périodiques --- EJBIOMO ELSEVIER-E EPUB-ALPHA-B EPUB-PER-FT --- Genes --- Genetic regulation --- Nucleotide sequence --- Expression, Gene --- Expressions, Gene --- Gene Expressions --- Gene Expression Profiling --- Code, Genetic --- Codes, Genetic --- Genetic Codes --- Codon --- DNA --- Protein Biosynthesis --- Histone Code --- Expression --- moleculaire biologie --- Biochemical genetics. --- Gene expression. --- Genetic code. --- Gene Code --- Gene Expression --- Genetic Code --- Molecular Biology --- Expression génique. --- Code génétique.

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Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics or for use in lab sessions. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher’s website. Chapters show how to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R. A wide range of R packages useful for working with genomic data are illustrated with practical examples. In recent years R has become the de facto tool for analysis of gene expression data, in addition to its prominent role in the analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. At a time when genomic data is decidedly big, the skills from this book are critical. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection; population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data. .

Statistics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics and Computing/Statistics Programs. --- Gene Expression. --- Microarrays. --- Gene expression. --- DNA microarrays. --- Mathematical statistics. --- Statistique --- Expression génique --- Puces à ADN --- Statistique mathématique --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Genomics --- R (Computer program language) --- Statistical methods. --- GNU-S (Computer program language) --- Genome research --- Genomes --- Research --- Domain-specific programming languages --- Molecular genetics --- DNA biochips --- Microarrays, DNA --- Biochips --- Immobilized nucleic acids --- Genes --- Genetic regulation --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Expression --- Statistics . --- R (Computer program language).