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001			76607
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007			cr nn 008mamaa
008			100301s2006 sz | s |||| 0|eng d
020			_a9783764373870
024	7		_a10.1007/3-7643-7387-3 _2doi
050		4	_aQH324.2 _b.H383 2006
082	0	4	_a570.285
100	1		_aHaubold, Bernhard _985753 _0Local
245	1	0	_aIntroduction to Computational Biology : _bAn Evolutionary Approach _cby Bernhard Haubold, Thomas Wiehe
260			_aBasel _bBirkhäuser Basel _c2006
300			_a1 recurso en línea (XIV, 328p.) _b170 ilustraciones
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aSequences in Space -- Optimal Pairwise Alignment -- Biological Sequences and the Exact String Matching Problem -- Fast Alignment: Genome Comparison and Database Searching -- Multiple Sequence Alignment -- Sequence Profiles and Hidden Markov Models -- Gene Prediction -- Sequences in Time -- Phylogeny -- Sequence Variation and Molecular Evolution -- Genes in Populations: Forward in Time -- Genes in Populations: Backward in Time -- Testing Evolutionary Hypotheses
520	3		_aMolecular biology has changed dramatically over the past two decades. Until the early 1990s genes were studied one at a time by small teams of researchers; today entire genomes are sequenced by internationally collaborating laboratories. In the bygone gene-centered era the accumulation of data was the rate-limiting step in research. Now that step is often data interpretation. This is increasingly dependent on computational methods and as a consequence, computational biology has emerged in the past decade as a new subdiscipline of biology. This introduction to computational biology is centered on the analysis of molecular sequence data. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models. In the second part evolutionary time takes center stage and phylogenetic reconstruction, the analysis of sequence variation, and the dynamics of genes in populations are explained in detail. In addition, the book contains a computer program with a graphical user interface that allows the reader to experiment with a number of key concepts developed by the authors. Introduction to Computational Biology is intended for students enrolled in courses in computational biology or bioinformatics as well as for molecular biologists, mathematicians, and computer scientists. Bernhard Haubold is associate professor at the University of Applied Sciences, Weihenstephan, Germany. Thomas Wiehe is associate professor at the University of Cologne, Germany
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGERrevisando
650		7	_aBioinformática _0comprobar BNE20022028248 _2embne _9160489
700	1		_aWiehe, Thomas _eeditor literario _985754 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/3-7643-7387-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783764373870
907			_a.b12822255 _b10-10-17 _c01-10-14
998			_am _a_alco _a_vill _b26-05-17 _cm _dz _ek _feng _gsz _h0
945			_aQH324.2 .H383 2006 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i1155146x _z06-04-17
999			_c76607 _d76607 _x1