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Comparative Gene Finding Models, Algorithms and Implementation

:	Marina Axelson-Fisk
:	Comparative Gene Finding Models, Algorithms and Implementation
:	Springer-Verlag
:	9781849961042
:	1
:	CHF 116.30
:

:	Biologie
:	English

:	304
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Comparative genomics is a new and emerging ?eld, and with the explosion of ava- able biological sequences the requests for faster, more ef?cient and more robust algorithms to analyze all this data are immense. This book is meant to serve as a self-contained instruction of the state-of-the-art of computational gene ?nding in general and of comparative approaches in particular. It is meant as an overview of the various methods that have been applied in the ?eld, and a quick introduction into how computational gene ?nders are built in general. A beginner to the ?eld could use this book as a guide through to the main points to think about when constructing a gene ?nder, and the main algorithms that are in use. On the other hand, the more experienced gene ?nder should be able to use this book as a reference to different methods and to the main components incorporated in these methods. I have focused on the main uses of the covered methods and avoided much of the technical details and general extensions of the models. In exchange I have tried to supply references to more detailed accounts of the different research areas touched upon. The book, however, makes no claim on being comprehensive.

	Preface	7
	Acknowledgments	9
	Contents	10
	Acronyms	14
	Introduction	15
	Some Basic Genetics	15
	The Central Dogma	17
	The Structure of a Gene	19
	How Many Genes Do We Have?	21
	Problems of Gene Definitions	25
	The Gene Finding Problem	26
	Comparative Gene Finding	28
	History of Algorithm Development	29
	To Build a Gene Finder	32
	References	35
	Single Species Gene Finding	41
	Hidden Markov Models (HMMs)	41
	Markov Chains	42
	Discrete-Time Markov Chains	42
	Stationarity and Reversibility	48
	Continuous-Time Markov Chains	50
	Hidden Markov Models	53
	Dynamic Programming	56
	Silent Begin and End States	58
	The Forward Algorithm	59
	The Backward Algorithm	59
	The Viterbi Algorithm	61
	EasyGene: A Prokaryotic Gene Finder	63
	Posterior Decoding	65
	Statistical Significance of Predictions	65
	Generalized Hidden Markov Models (GHMMs)	66
	Preliminaries	66
	The Forward and Backward Algorithms	68
	The Forward Variables	68
	The Backward Variables	70
	The Viterbi Algorithm	70
	Genscan: A GHMM-Based Gene Finder	71
	Sequence Generation Algorithm	74
	Reducing Computational Complexity	74
	Exon Probabilities	78
	Interpolated Markov Models (IMMs)	81
	Preliminaries	81
	Linear and Rational Interpolation	82
	GLIMMER: A Microbial Gene Finder	83
	Gene Prediction	84
	Training the IMM	85
	GlimmerM	86
	Neural Networks	86
	Biological Neurons	87
	Artificial Neurons and the Perceptron	88
	Multi-Layer Neural Networks	90
	GRAIL: A Neural Network-Based Gene Finder	91
	Decision Trees	93
	Classification	94
	Decision Tree Learning	95
	MORGAN: A Decision Tree-Based Gene Finder	99
	References	100
	Sequence Alignment	103
	Pairwise Sequence Alignment	103
	Dot Plot Matrix	105
	Nucleotide Substitution Models	106
	The Jukes-Cantor Model	108
	The Kimura Model	109
	The Felsenstein Model	110
	The Tamura and Nei Model	111
	General Time-Reversible (GTR) Model	111
	Amino Acid Substitution Models	112
	The PAM Matrix	113
	The BLOSUM Matrix	117
	The GONNET matrix	120
	Gap Models	120
	The Needleman-Wunsch Algorithm	122
	Needleman-Wunsch Using Affine Gaps	124
	The Smith-Waterman Algorithm	126
	Pair Hidden Markov Models (PHMMs)	128
	Preliminaries	128
	The Forward, Backward, and Viterbi Algorithms	130
	Database Similarity Searches	132
	FASTA	132
	BLAST	134
	Gapped BLAST	136
	PSI-BLAST	136
	The Significance of Alignment Scores	137
	Multiple Sequence Alignment	138
	Scoring Schemes	139
	Sum-of-Pairs (SP)	141
	Weighted Sum-of-Pairs (WSP)	141
	Minimum Entropy	141
	Gap Costs	142
	Phylogenetic Trees	143
	The Neighbor-Joining Method	143
	Fitch-Margoliash	144
	Dynamic Programming	145
	The MSA Package	145
	Progressive Alignments	147
	Iterative Methods	150
	Hidden Markov Models	153
	SAM-Sequence Alignment and Modeling	153
	Genetic Algorithms	155
	Simulated Annealing	158
	Alignment Profiles	161
	Standard Profiles	161
	Profile HMMs	163
	Scoring a New Sequence	164
	References	165
	Comparative Gene Finding	171
	Similarity-Based Gene Finding	171
	GenomeScan: GHMM-Based Gene Finding Using Homology	172
	Twinscan: GHMM-Based Gene Finding Using Informant Sequences	174
	Heuristic Cross-Species Gene Finding	176
	ROSETTA	176
	Pair Hidden Markov Models (PHMMs)	177
	DoubleScan: A PHMM-Based Comparative Gene Finder	178
	The State Space	178
	The Stepping Stone Algorithm	180
	Generalized Pair Hidden Markov Models (GPHMMs)	181
	Preliminaries	181
	The Forward, Backward and Viterbi Algorithms	182
	SLAM: A GPHMM-Based Comparative Gene Finder	184
	The State Space	184
	Reducing Computational Complexity	186
	Gene Mapping	188
	Projector: A Gene Mapping Tool	188
	GeneMapper-Reference Based Annotation	189
	Multiple Sequence Gene Finding	190
	N-SCAN: A Multiple Informant-Based Gene Finder	191
	References	193
	Gene Structure Submodels	195
	The State Space	195
	The Exon States	196
	Splice Sites	198
	Introns and Intergenic Regions	199
	Untranslated Regions (UTRs)	200
	Promoters and PolyA-signals	201
	State Length Distributions	202
	Geometric and Negative Binomial Lengths	202
	Empirical Length Distributions	205
	Acyclic Discrete Phase Type Distributions	206
	Sequence Content Sensors	210
	GC-Content Binning	210
	Start Codon Recognition	211
	Codon and Amino Acid Usage	212
	K-Tuple Freq