Scott Markel – författare

Gene sequence data is the most abundant type of data available, and if you're interested in analyzing it, you'll find a wealth of computational methods and tools to help you. In fact, finding the data is not the challenge at all; rather it is dealing with the plethora of flat file formats used to process the sequence entries and trying to remember what their specific field codes mean. If you survive by surrounding yourself with well-thumbed hard copies of readme files or remembering exactly where to look for the details when you need them, then Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases is for you. This book is a handy resource, as well as an invaluable reference, for anyone who needs to know about the practical aspects and mechanics of sequence analysis. Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases pulls together all of the vital information about the most commonly used databases, analytical tools, and tables used in sequence analysis. The book is partitioned into three fundamental areas to help you maximize your use of the content.The first section, "Databases" contains examples of flatfiles from key databases (GenBank, EMBL, SWISS-PROT), the definitions of the codes or fields used in each database, and the sequence feature types/terms and qualifiers for the nucleotide and protein databases. The second section, "Tools" provides the command line syntax for popular applications such as ReadSeq, MEME/MAST, BLAST, ClustalW, and the EMBOSS suite of analytical tools. The third section, "Appendixes" concentrates on information essential to understanding the individual components that make up a biological sequence. The tables in this section include nucleotide and protein codes, genetic codes, as well as other relevant information. Written in O'Reilly's enormously popular, straightforward "Nutshell" format, this book draws together essential information for bioinformaticians in industry and academia, as well as for students. If sequence analysis is part of your daily life, you'll want this easy-to-use book on your desk.

The pharmaceutical industry relies on numerous well-designed experiments involving high-throughput techniques and in silico approaches to analyze potential drug targets. These in silico methods are often predictive, yielding faster and less expensive analyses than traditional in vivo or in vitro procedures.In Silico Technologies in Drug Target Identification and Validation addresses the challenge of testing a growing number of new potential targets and reviews currently available in silico approaches for identifying and validating these targets. The book emphasizes computational tools, public and commercial databases, mathematical methods, and software for interpreting complex experimental data. The book describes how these tools are used to visualize a target structure, identify binding sites, and predict behavior. World-renowned researchers cover many topics not typically found in most informatics books, including functional annotation, siRNA design, pathways, text mining, ontologies, systems biology, database management, data pipelining, and pharmacogenomics. Covering issues that range from prescreening target selection to genetic modeling and valuable data integration, In Silico Technologies in Drug Target Identification and Validation is a self-contained and practical guide to the various computational tools that can accelerate the identification and validation stages of drug target discovery and determine the biological functionality of potential targets more effectively.Daniel E. Levy, editor of the Drug Discovery Series, is the founder of DEL BioPharma, a consulting service for drug discovery programs.

The pharmaceutical industry relies on numerous well-designed experiments involving high-throughput techniques and in silico approaches to analyze potential drug targets. These in silico methods are often predictive, yielding faster and less expensive analyses than traditional in vivo or in vitro procedures. In Silico Technologies in Drug Target Ide