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008			150903s2005 xxu| o |||| 0|eng d
020			_a9780387288079 _99780387288079
024	7		_a10.1007/0387288074 _2doi
035			_avtls000330656
039		9	_a201509030724 _bVLOAD _c201404120511 _dVLOAD _c201404090252 _dVLOAD _c201401311347 _dstaff _y201401301151 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQH324.2-324.25
100	1		_aDeonier, Richard C. _eautor _9301784
245	1	0	_aComputational Genome Analysis : _bAn Introduction / _cby Richard C. Deonier, Michael S. Waterman, Simon Tavaré.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_axx, 535 páginas, 102 ilustraciones, 15 en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aBiology in a Nutshell -- Words -- Word Distributions and Occurrences -- Physical Mapping of DNA -- Genome Rearrangements -- Sequence Alignment -- Rapid Alignment Methods: FASTA and BLAST -- DNA Sequence Assembly -- Signals in DNA -- Similarity, Distance, and Clustering -- Measuring Expression of Genome Information -- Inferring the Past: Phylogenetic Trees -- Genetic Variation in Populations -- Comparative Genomics.
520			_aComputational Genome Analysis: An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field. This book features:Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation. Presentation of fundamentals of probability, statistics, and algorithms. Implementation of computational methods with numerous examples based upon the R statistics package. Extensive descriptions and explanations to complement the analytical development. More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature. Exercises at the end of chapters. Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels. Simon Tavaré holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavaré's research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavaré is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics. Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aWaterman, Michael S. _eautor _9301785
700	1		_aTavaré, Simon. _eautor _9301786
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387987859
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/0-387-28807-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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