Sequence Analysis Primer

Computerized sequence analysis is an integral part of biotechnological research, yet many biologists have received no formal training in this important technology. Sequence Analysis Primer offers the beginner the necessary background to enter this vital field and helps more seasoned researchers to fine-tune their approach. It covers basic data manipulation such as homology searches, stem-loop identification, and protein secondary structure prediction, and is compatible with most sequence analysis programs. A detailed example giving steps for characterizing a new gene sequence provides users with hands-on experience when combined with their current software. The book will be invaluable to researchers and students in molecular biology, genetics, biochemistry, microbiology, and biotechnology.

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.

Presents up-to-date computer methods for analysing DNA, RNA and protein sequences.

A timely book for DNA researchers, Automated DNA Sequencing and Analysis reviews and assesses the state of the art of automated DNA sequence analysis-from the construction of clone libraries to the developmentof laboratory and community databases. It presents the methodologies and strategies of automated DNA sequence analysis in a way that allows them to be compared and contrasted. By taking a broad view of the process of automated sequence analysis, the present volume bridges the gap between the protocols supplied with instrument and reaction kits and the finalized data presented in the research literature. It will be an invaluable aid to both small laboratories that are interested in taking maximum advantageof automated sequence resources and to groups pursuing large-scale cDNA and genomic sequencing projects. The field of automation in DAN sequencing and analysis is rapidly moving, this book fulfils those needs, reviews the history of the art and provides pointers to future development.

This third edition provides new and updated chapters on design PCR primers for successful DNA amplification. Chapters are divided into seven parts, including primer design strategies for quantitative PCR, genotyping, multiplex PCR, in silico PCR primer design, and primer design to identify plant and animal viruses. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and easily accessible, PCR Primer Design, Third Edition aims to be useful for various fields of molecular biology, including biotechnology, molecular genetics, and recombinant DNA technology.

This work pulls together all of the vital information about the most commonly used databases, analytical tools, and tables used in sequence analysis.

In these days of facile cloning and rapid DNA sequencing, it is not uncommon for investigators to find themselves with a DNA sequence that may or may not code for a known gene product. The sequence is 'open' when read in an appropriate frame, which is to say that there is a long run of amino acid codons before the appearance of a terminator codon. How can we find out if this 'unidentified reading frame' (URF) really codes for a genuine protein, and how can we identify it if it exists? There are two general strategies, both of which can also be applied to the characterization of any 'open reading frame' (ORF), whether or not it has been 'identified'. The first and simplest approach involves computer searching and analysis; the second employs antibodies raised against synthetic peptides patterned on the sequence of the expected gene product. Both methods have been used with great success by many investigators. Each has, nonetheless, its pitfalls and frustrations. This primer is meant to guide the researcher past those obstacles as much as possible. Graduate students and researchers interested in amino acid sequencing; molecular biologists, biochemists, chemists, and biotechnologists.

Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as functional genomics. The book provides the reader with an understanding of the principles and approaches of functional genomics and of the potential and limitations of computational and experimental approaches to genome analysis. Sequence - Evolution - Function should help bridge the "digital divide" between biologists and computer scientists, allowing biologists to better grasp the peculiarities of the emerging field of Genome Biology and to learn how to benefit from the enormous amount of sequence data available in the public databases. The book is non-technical with respect to the computer methods for genome analysis and discusses these methods from the user's viewpoint, without addressing mathematical and algorithmic details. Prior practical familiarity with the basic methods for sequence analysis is a major advantage, but a reader without such experience will be able to use the book as an introduction to these methods. This book is perfect for introductory level courses in computational methods for comparative and functional genomics.