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A Primer In Biological Data Analysis And Visualization Using R

Author: Gregg Hartvigsen
Publisher: Columbia University Press
ISBN: 0231537042
Size: 79.21 MB
Format: PDF, Docs
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R is a popular programming language that statisticians use to perform a variety of statistical computing tasks. Rooted in Gregg Hartvigsen's extensive experience teaching biology, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio to the organization, computation, and visualization of biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to express data in histograms, boxplots, barplots, scatterplots, before/after line plots, pie charts, and graphs. He covers data normality, outliers, and nonnormal data and examines frequently used statistical tests with one value and one sample; paired samples; more than two samples across a single factor; correlation; and linear regression. The volume also includes a section on advanced procedures and a final chapter on possible extensions into programming, featuring a discussion of algorithms, the art of looping, and combining programming and output.

Primer To Analysis Of Genomic Data Using R

Author: Cedric Gondro
Publisher: Springer
ISBN: 3319144758
Size: 39.80 MB
Format: PDF, Kindle
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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 graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples. 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. At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. 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./p


Publisher: PHI Learning Pvt. Ltd.
ISBN: 9788120328587
Size: 79.40 MB
Format: PDF, Kindle
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Being an interdisciplinary subject, Bioinformatics is today covering a range of interest both among the students and teaching communities. Taking this increasing interest into account, this book gives a comprehensive introduction to the subject. The text not only deals with the basic concepts but it also emphasizes the technical and practical aspects of the subject. The book covers the computational tools in bioinformatics, algorithmic aspects as well as technological aspects. Besides it gives a clear exposition of Viterbi algorithm, Hidden Markov models, UPGMA, FM algorithm, heuristic, developing and using substitution matrices, HMMs and derivation of a number of standard formulae in information theory and statistics. Finally the text focusses on the technological aspects of bioinformatics such as sequencing through shot gun methods, microarrays, with a variety of unsupervised methods in data analysis with examples, as well as interdisciplinary research in systems biology. The book is primarily intended as a text for the students of Computer Science, Information Technology, undergraduate students of Bioinformatics, PGDCA and biological sciences and biotechnology. The book should also be of considerable interest for research scientist in Chemistry and Pharmacy.

A Primer For Computational Biology

Author: Shawn T. O'Neil
ISBN: 9780870719264
Size: 67.50 MB
Format: PDF
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A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the "natural environment" of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful "pipe" operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.

Bioinformatics For Geneticists

Author: Michael R. Barnes
Publisher: John Wiley & Sons
ISBN: 0470026197
Size: 48.32 MB
Format: PDF
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Bioinformatics for geneticists describes the key bioinformatics and genetic analysis processes that are needed to identify human genetic determinants. Including SNP functional analysis and statistical genetics.

Statistical And Evolutionary Analysis Of Biological Networks

Author: Michael P. H. Stumpf
Publisher: World Scientific
ISBN: 1848164335
Size: 14.56 MB
Format: PDF, ePub
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Networks provide a very useful way to describe a wide range of different data types in biology, physics and elsewhere. Apart from providing a convenient tool to visualize highly dependent data, networks allow stringent mathematical and statistical analysis. In recent years, much progress has been achieved to interpret various types of biological network data such as transcriptomic, metabolomic and protein interaction data as well as epidemiological data. Of particular interest is to understand the organization, complexity and dynamics of biological networks and how these are influenced by network evolution and functionality. This book reviews and explores statistical, mathematical and evolutionary theory and tools in the understanding of biological networks. The book is divided into comprehensive and self-contained chapters, each of which focuses on an important biological network type, explains concepts and theory and illustrates how these can be used to obtain insight into biologically relevant processes and questions. There are chapters covering metabolic, transcriptomic, protein interaction and epidemiological networks as well as chapters that deal with theoretical and conceptual material. The authors, who contribute to the book, are active, highly regarded and well-known in the network community.