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E-raamat: Molecular Data Analysis Using R [Wiley Online]

Csaba Ortutay, Zsuzsanna Ortutay

Teised formaadid

Other digital carrier (Hind: 116,13 €) - 02-Jan-2017

Formaat: 352 pages
Ilmumisaeg: 03-Feb-2017
Kirjastus: Wiley-Blackwell
ISBN-10: 1119165059
ISBN-13: 9781119165057

Teised raamatud teemal:

Wiley Online
Hind: 114,14 €*
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Formaat: 352 pages
Ilmumisaeg: 03-Feb-2017
Kirjastus: Wiley-Blackwell
ISBN-10: 1119165059
ISBN-13: 9781119165057

Teised raamatud teemal:

Rohkem infot Wiley Online kohta

Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119165057

This book addresses the difficulties experienced by wet-lab researchers with the statistical analysis of molecular biology-related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories. Key features include the following:

Broad appeal-the authors target their material to researchers in several levels, ensuring that the basics are always covered.

First book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology.

Focuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes. Further, R is the platform for implementing new analysis approaches; therefore, novel methods are available early for R users.

This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students: "Biological Data Analysis with R" and "High-throughput Data Analysis with R" . The material is divided into chapters based upon the experimental methods used in the labs.

Foreword

xiii

Preface

Acknowledgements

xix

About the Companion Website

xxi

1 Introduction to R statistical environment

(16)

Why R?

(1)

Installing R

(1)

Interacting with R

(2)

Graphical interfaces and integrated development environment (IDE) integration

(1)

Scripting and sourcing

(1)

The R history and the R environment file

(1)

Packages and package repositories

(3)

Comprehensive R Archive Network

(1)

Bioconductor

(1)

Working with data

(1)

Basic operations in R

(2)

Some basics of graphics in R

(2)

Getting help in R

(1)

Files for practicing

(1)

Study exercises and questions

(1)

References

(1)

Webliography

(2)

2 Simple sequence analysis

(24)

Sequence files

(3)

FASTA sequence format

(1)

GenBank flat file format

(1)

Reading sequence files into R

(1)

Obtaining sequences from remote databases

(3)

Seqinr package

(1)

Ape package

(2)

Descriptive statistics of nucleotide sequences

(4)

Descriptive statistics of proteins

(3)

Aligned sequences

(3)

Visualization of genes and transcripts in a professional way

(3)

Files for practicing

(1)

Study exercises and questions

(1)

References

(1)

Webliography

(1)

Packages

(1)

3 Annotating gene groups

(24)

Enrichment analysis: an overview

(5)

Overview of two different methods

(1)

Enrichment analysis results

(1)

Common aspects of the two different approaches

(3)

Overrepresentation analysis

(6)

Hypergeometric test using GOstats

(1)

ORA analysis using topGO

(3)

Enrichment analysis of microarray sets with topGO

(1)

Gene set enrichment analysis

(9)

GSEA with R

(5)

Files for practicing

(1)

Study exercises and questions

(1)

References

(1)

Webliography

(1)

Packages

(2)

4 Next-generation sequencing: introduction and genomic applications

(34)

High-throughput sequencing background

(7)

Experimental background

(1)

Single-end and paired-end sequencing reads

(2)

Assemble reads

(2)

How many reads? Depth of coverage

(1)

Storing data in files

(5)

FASTQ

(4)

SAM and BAM files

(1)

Variant call format files

(1)

General data analysis workflow

(3)

Data processing considerations

(2)

Quality checking and screening read sequences

(4)

Quality checking for one file

(1)

Quality inspection for multiple files in a project

(1)

Quality filtering of FASTQ files

(1)

Handling alignment files and genomic variants

(7)

Alignment and variation visualization

(1)

Simple handling of VCF files

(2)

Genomic applications: low- and medium-depth sequencing

(3)

Aneuploidity sequencing and copy number variation identification

(1)

SNP identification and validation

(1)

Exome sequencing

(1)

Genomic region resequencing

(1)

Full genome and metagenome sequencing

(1)

Files for practicing

(1)

Study exercises and questions

(1)

References

(2)

Webliography

(1)

Packages

(2)

5 Quantitative transcriptomics: qRT-PCR

(26)

Transcriptome

(5)

Polymerase chain reaction

100

(2)

Standards for qPCR

102

(2)

R packages

104

(1)

Understanding delta Ct

104

(6)

Calculation of delta Ct

105

(2)

Requirements for real delta Ct calculations

107

(3)

Absolute quantification

110

(5)

Value prediction, the professional way

114

(1)

Relative quantification using the ddCt method

115

(6)

Comparison of two conditions

116

(2)

Comparison of multiple experimental conditions

118

(3)

Quality control with melting curve

121

(2)

Files for practicing

123

(1)

Study exercises and questions

123

(1)

References

123

(1)

Webliography

124

(1)

Packages

124

(1)

6 Advanced transcriptomics: gene expression microarrays

125

(20)

Microarray analysis: probes and samples

125

(3)

Experimental background

126

(2)

Archiving and publishing microarray data

128

(1)

Minimum information standard

128

(1)

Data preprocessing

128

(5)

Accessing data from CEL files

129

(2)

Quality control

131

(1)

Normalization

132

(1)

Differential gene expression

133

(5)

Annotating results

136

(2)

Creating normalized expression set from Illumina data

138

(2)

Automated data access from GEO

140

(2)

Files for practicing

142

(1)

Study exercises and questions

142

(1)

References

143

(1)

Webliography

144

(1)

Packages

144

(1)

7 Next-generation sequencing in transcriptomics: RNA-seq experiments

145

(22)

High-throughput RNA sequencing background

145

(3)

Experimental background

145

(1)

RNA-seq applications

146

(1)

Differential expression with different resolutions

147

(1)

Preparing count tables

148

(11)

Alignment files to read counts

148

(3)

Differential expression in simple comparison

151

(1)

A naive t-test approach

151

(2)

Single factor analysis with edgeR

153

(3)

Differential expression with DESeq

156

(3)

Complex experimental arrangements

159

(5)

Experimental factors and design matrix

160

(1)

GLM with edgeR

161

(1)

GLMs with DESeq

162

(1)

Heatmap visualization

163

(1)

Files for practicing

164

(1)

Study exercises and questions

164

(1)

References

165

(1)

Webliography

166

(1)

Packages

166

(1)

8 Deciphering the regulome: from ChIP to ChIP-seq

167

(24)

Chromatin immunoprecipitation

167

(2)

Experimental background

168

(1)

Fragment analysis

168

(1)

ChIP data in ENCODE

169

(1)

ChIP with tiling microarrays

169

(7)

High-throughput sequencing of ChIP fragments

176

(6)

Connecting annotation to peaks

181

(1)

Analysis of binding site motifs

182

(4)

Files for practicing

186

(1)

Study exercises and questions

187

(1)

References

187

(1)

Webliography

188

(1)

Packages

189

(2)

9 Inferring regulatory and other networks from gene expression data

191

(24)

Gene regulatory networks

191

(2)

Data for gene network inference

192

(1)

Reconstruction of co-expression networks

193

(8)

Gene regulatory network inference focusing of master regulators

201

(6)

Integrated interpretation of genes with GeneAnswers

207

(4)

Files for practicing

211

(1)

Study exercises and questions

212

(1)

References

213

(1)

Packages

214

(1)

10 Analysis of biological networks

215

(30)

A gentle introduction to networks

215

(8)

Networks and their components and features

215

(5)

Random networks

220

(1)

Biological networks

221

(2)

Files for storing network information

223

(4)

Important network metrics in biology

227

(9)

Distance-based measures

228

(2)

Degree and related measures

230

(1)

Vulnerability

231

(3)

Community structure of a network

234

(2)

Graph visualization

236

(5)

Cytoscape

240

(1)

Files for practicing

241

(1)

Study exercises and questions

241

(1)

References

242

(1)

Webliography

243

(1)

Packages

243

(2)

11 Proteomics: mass spectrometry

245

(16)

Mass spectrometry and proteomics: why and how?

245

(1)

File formats for MS data

246

(3)

Accessing the raw data of published studies

247

(2)

Identification of peptides in the samples

249

(5)

Peptide mass fingerprinting

249

(1)

Peptide identification by using MS/MS spectra

250

(4)

Quantitative proteomics

254

(4)

Getting protein-specific annotation

258

(1)

Files for practicing

259

(1)

Study exercises and questions

259

(1)

References

259

(1)

Webliography

260

(1)

Packages

260

(1)

12 Measuring protein abundance with ELISA

261

(18)

Enzyme-linked immunosorbent assays

261

(3)

Accessing ELISA data

264

(1)

Concentration calculation with a standard curve

264

(7)

Preparing reference data

267

(1)

Fitting linear model

268

(1)

Fitting of a logistic model

269

(1)

Concentration calculations by employing models

270

(1)

Comparative calculations using concentrations

271

(6)

Files for practicing

277

(1)

Study exercises and questions

277

(1)

References

277

(1)

Packages

278

(1)

13 Flow cytometry: counting and sorting stained cells

279

(32)

Theoretical aspects of flow cytometry

279

(8)

Experiment types: diagnosis versus discovery

280

(1)

Measurement arrangements

281

(1)

Fluorescent dyes

281

(4)

Tubes versus plates

285

(1)

Instruments

285

(2)

What about data?

287

(2)

Files

287

(1)

Workflows

288

(1)

Data preprocessing

289

(10)

Handling all samples together

290

(2)

Compensation

292

(1)

Quality assurance

292

(4)

Using workflow objects and transformation

296

(2)

Normalization

298

(1)

Cell population identification

299

(6)

Manual gating

300

(4)

Automatic gating

304

(1)

Relating cell populations to external variables

305

(2)

Reporting results

307

(1)

MIFlowCyt

307

(1)

FlowRepository.org

308

(1)

Files for practicing

308

(1)

Study exercises and questions

309

(1)

References

309

(1)

Webliography

310

(1)

Packages

310

(1)

Glossary

311

(12)

Index

323

Csaba Ortutay is a bioinformatician from Finland who has taught several bioinformatics courses at different European universities (Finland, Ireland, and Hungary) for over a decade. He is also active as a researcher publishing in the field of computational immunology.

Zsuzsanna Ortutay is a molecular immunologist at the University of Tampere, Finland, frequently utilizing diverse molecular lab methods.

Püsilink: https://www.kriso.ee/db/9781119165057_pe.html

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