Bøger i Chapman & Hall/CRC Computational Biology Series serien

Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.

This second edition incorporates important advancements made in bioinformatics over the last decade. Focusing on practical problem solving, it works through sequence analysis from simple gene family analysis to expression analysis with microarrays and next-gen sequencing.

This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources. It addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments, and solves data combination problems that can arise within the same basic framework. It demonstrates the combined analysis of different data types, showing data combination approaches in action across a wide variety of genome-scale investigations.

The unprecedented amount of data produced with high-throughput experimentation forces biologists to employ mathematical representation and computation to glean meaningful information in systems-level biology. Applying this approach to the underlying molecular mechanisms of tumorgenesis, cancer research is enjoying a series of new discoveries and biological insights. Unique in its dualistic approach, this book introduces the concepts and theories of systems biology and their applications in cancer research. It presents basic cancer biology and cutting-edge topics of cancer research for computational biologists alongside systems biology analysis tools for experimental biologists.

Written by SeqAn project founders, this book describes the general library design of SeqAn. It introduces biological sequence analysis problems, discusses the benefit of using software libraries, summarizes SeqAn design principles and goals, details SeqAn programming techniques, and demonstrates the application of these techniques in various examples. Focusing on SeqAn components, the second part explores basic functionality, sequence data structures, alignments, pattern and motif searching, string indices, and graphs. The last part describes applications of SeqAn to genome alignment, consensus sequence, suffix array construction.

An invaluable resource for computational biologists and researchers from other fields seeking an introduction to the topic, Chromatin: Structure, Dynamics, Regulation offers comprehensive coverage of this dynamic interdisciplinary field, from the basics to the latest research. Computational methods from statistical physics and bioinformatics are detailed whenever possible without lengthy recourse to specialized techniques.

This book is an invaluable tool for students and researchers in bioinformatics and computational biology, biologists seeking to process and manage the data generated by next-generation sequencing, and as a textbook or a self-study resource.

Microarrays allow researchers to simultaneously monitor the expression of thousands of genes. Independent of the platform and analysis methods used, the result of a microarray experiment is a list of differentially expressed genes. Presenting a unified analysis of the field, this book explores the tools available to better understand the underlying biological phenomena of differentially expressed genes. It focuses on two major analytic approaches: 1) ontological profiling and 2) gene interaction networks and known pathways. The author presents the fundamentals and tools for each approach.

The book covers several of the major data analysis techniques used to analyze data from high-throughput molecular biology and genomics experiments. It also explains the major concepts behind most of the popular techniques and examines some of the simpler techniques in detail.

Written for students and researchers in systems biology, the second edition of this best-selling textbook continues to offer a clear presentation of design principles that govern the structure and behavior of biological networks, highlighting simple, recurring circuit elements that make up the regulation of cells and tissues.

This book is a practical guide to the nuts and bolts of Big Data, enabling readers to quickly and effectively harness the power of Big Data to make groundbreaking biological discoveries, carry out translational medical research, and implement personalized genomic medicine. Contributing to the NIH Big Data to Knowledge (BD2K) initiative, the book enhances readers¿ computational and quantitative skills so that they can exploit the Big Data being generated in the current omics era.

This self-contained guide enables researchers to examine differential expression at gene, exon, and transcript levels and to discover novel genes, transcripts, and whole transcriptomes. It takes readers through the whole data analysis workflow. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools and practical examples. Accessible to both bioinformaticians and nonprogramming wet lab scientists, the examples illustrate the use of command-line tools, R, and other open source tools.

Suitable for graduate-level researchers in statistics and biology, this book presents a snapshot of current trends in Bayesian phylogenetic research. It emphasizes model selection, reflecting recent interest in accurately estimating marginal likelihoods. The book discusses new approaches to improve mixing in Bayesian phylogenetic analyses in which the tree topology varies. It also covers divergence time estimation, biologically realistic models, and the burgeoning interface between phylogenetics and population genetics.

This book shows how to perform computations with Python scripts in the Chimera environment. It covers topics that deal primarily with protein structure and encourages mathematical analysis by providing a firm foundation for computations. Through more than 60 exercises that involve the development of Python scripts, the book gives readers concrete guidance on using the scripting capabilities of Chimera. Python scripts and other material are available on a supplementary website.

Replaces the commercial software with the open source R computing environment. This title contains chapters on cutting-edge microarray topics and provides the R code on an accompanying CD-ROM. It bridges the gap between an introduction to data analysis and advanced material for performing data analysis.

Applying complex systems science to biology, this book develops mathematical models for understanding biological systems. Based on his one-semester course, the author suggests appropriate control strategies to mediate the effects of past and future pandemics, assuming no prior knowledge of mathematics. Each chapter presents exercises with worked solutions as well as computational and research projects. Topics covered include pattern formation and flocking behavior, the interaction of autonomous agents, hierarchical and structured network topologies, epidemiology, biomedical signal processing, computational neurophysiology, and population dynamics. A solutions manual is available for qualifying instructors.

Drawn from the authors¿ decade-long work in the cancer computational systems biology laboratory at Institut Curie, this self-contained guide explains how to apply computational systems biology approaches to cancer research. Suitable for readers in both computational and life sciences, the book provides proven techniques and tools for cancer bioinformatics and systems biology research. It explores how computational systems biology can help fight cancer in three essential aspects: categorizing tumors, finding new targets, and designing improved and tailored therapeutic strategies.

The unprecedented amount of data produced with high-throughput experimentation forces biologists to employ mathematical representation and computation to glean meaningful information in systems-level biology. This book introduces the concepts and theories of systems biology and the applications of systems biology in cancer research.

Focusing on protein classification and meta-organization, this book describes methods for detecting self-organization in complex biological systems. It presents the analysis of biological entities and their cellular counterparts and discusses methods for detecting the building blocks of proteins and for prediction of protein-protein interactions.

Suitable for advanced undergraduates in computer science programs, this title covers major themes of bio-inspired computing, including cellular automata, molecular computation, genetic algorithms, and neural networks. It provides theoretical and coding exercises.

Describing the characteristics and limitations of key algorithms, this book covers various aspects of chemoinformatics, including structure representation, molecular descriptors, similarity search, virtual screening, and structure-property model generation and validation.

The SeqAn project was initiated to offer access to the algorithms needed by researchers in computational biology and bioinformatics. This book helps you in rapid prototyping of algorithms in the field. It is suitable for bioinformaticians.

A guide for applying basic informatics algorithms to medical data sets. It includes examples using three most common programming languages (Perl, Python, and Ruby). It provides basic methods for retrieving, organizing, merging, and analyzing their data sources. It covers building blocks, primary tasks of medical informatics, and medical discovery.

Presents an introduction to cluster analysis and algorithms in the context of drug discovery clustering applications. This book provides an understanding of the applications in clustering large combinatorial libraries for compound acquisition, HTS results, 3D lead hopping, gene expression for toxicity studies, and protein reaction data.

Presents techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources. This book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments.