Bioinformatics with R cookbook : over 90 practical recipes for computational biologists to model and handle real-life data using R / Paurush Praveen Sinha ; cover image by Aniket Sawant.

Format:

Book

Author/Creator:

Sinha, Paurush Praveen, author.

Contributor:

Sawant, Aniket, cover designer.

Series:

Community experience distilled.

Community Experience Distilled

Language:

English

Subjects (All):

Bioinformatics.

R (Computer program language).

Physical Description:

1 online resource (340 p.)

Place of Publication:

Birmingham, [England] : Packt Publishing, 2014.

Language Note:

English

Summary:

This book is an easy-to-follow, stepwise guide to handle real life Bioinformatics problems. Each recipe comes with a detailed explanation to the solution steps. A systematic approach, coupled with lots of illustrations, tips, and tricks will help you as a reader grasp even the trickiest of concepts without difficulty. This book is ideal for computational biologists and bioinformaticians with basic knowledge of R programming, bioinformatics and statistics. If you want to understand various critical concepts needed to develop your computational models in Bioinformatics, then this book is for you

Contents:

Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Starting Bioinformatics with R; Introduction; Getting started and installing libraries; Reading and writing data; Filtering and subsetting data; Basic statistical operations on data; Generating probability distributions; Performing statistical tests on data; Visualizing data; Working with PubMed in R; Retrieving data from BioMart; Chapter 2: Introduction to Bioconductor; Introduction; Installing packages from Bioconductor; Handling annotation databases in R

Performing ID conversionsThe KEGG annotation of genes; The GO annotation of genes; The GO enrichment of genes; The KEGG enrichment of genes; Bioconductor in the cloud; Chapter 3: Sequence Analysis with R; Introduction; Retrieving a sequence; Reading and writing the FASTA file; Getting the detail of a sequence composition; Pairwise sequence alignment; Multiple sequence alignment; Phylogenetic analysis and tree plotting; Handling BLAST results; Pattern finding in a sequence; Chapter 4: Protein Structure Analysis with R; Introduction; Retrieving a sequence from UniProt; Protein sequence analysis

Computing the features of a protein sequenceHandling the PDB file; Working with the InterPro domain annotation; Understanding the Ramachandran plot; Searching for similar proteins; Working with the secondary structure features of proteins; Visualizing the protein structures; Chapter 5: Analyzing Microarray Data with R; Introduction; Reading CEL files; Building the ExpressionSet object; Handling the AffyBatch object; Checking the quality of data; Generating artificial expression data; Data normalization; Overcoming batch effects in expression data; An exploratory analysis of data with PCA

Finding the differentially expressed genesWorking with the data of multiple classes; Handling time series data; Fold changes in microarray data; The functional enrichment of data; Clustering microarray data; Getting a co-expression network from microarray data; More visualizations for gene expression data; Chapter 6: Analyzing GWAS Data; Introduction; The SNP association analysis; Running association scans for SNPs; The whole genome SNP association analysis; Importing PLINK GWAS data; Data handling with the GWASTools package; Manipulating other GWAS data formats

The SNP annotation and enrichmentTesting data for the Hardy-Weinberg equilibrium; Association tests with CNV data; Visualizations in GWAS studies; Chapter 7: Analyzing Mass Spectrometry Data; Introduction; Reading the MS data of the mzXML/mzML format; Reading the MS data of the Bruker format; Converting the MS data in the mzXML format to MALDIquant; Extracting data elements from the MS data object; Preprocessing MS data; Peak detection in MS data; Peak alignment with MS data; Peptide identification in MS data; Performing protein quantification analysis

Performing multiple groups' analysis in MS data

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed July 10, 2014).

ISBN:

1-78328-314-9