Description
(Text)
This book presents molecular biology knowledge and mathematical theories of frequently-used algorithms in sequence comparison, gene finding, sequence evolution and structuring phylogenetic tree.
(Table of content)
Table of Content:
Part 1: Background
Chapter 1 Molecular biology primer - DcrNA, RNA, Protein, Gene, Transcription, bioengineering, computing in bioengineering
Chapter 2 Math background
2.1 Linear algebra
2.2 Probabilities - Bayes formula, stochastic process, Markov chain
2.3 Optimization - Lagrange theory
2.4 Statistics - machine learning, key theorem of learning theory
2.5 Growth function
Part 2 Sequence analysis
Chapter 3 Methods for comparing sequences
3.1 Similarity of sequences
3.2 Dot matrix sequence comparision
3.3 Overview for comparing sequences
3.4 Global dynamic programming algorithm
3.5 Local dynamic programming algorithm
3.6 Scoring matrices and gap penalties in sequence alignment - global, local
3.7 CheckPoint algorithm
3.8 FASTA, BLAST
3.9 Multi-sequence alignment - MAS, the Gibbs sampler, progressive methods of multiple sequence alignment
3. 10 Nucleic acid PAM scoring matrix
3.11 Summary
Chapter 4 Parallel computing for comparing sequences
4.1 Parallel programming model
4.2 Parallel computing structure
4.3 Sequence comparing parallelization
4.4 Smith-Waterman algorithms
4.5 Data searching - FASTA, TurboBLAST, mpiBLAST
4.6 Multi-sequence comparision - HMMER, ClustalW, ClusterW-MPI
4.7 Sequence comparing based on specialized hardware - FPGA hardware and parallel computing
Chapter 5 Sequence comparison based on exact matching
Chapter 6 Gene finding
6.1 Algorithms for gene finding and prediction
6.2 Accuracy for prediction algorithm
6.3 Methods of finding DNA sequences - open reading frames
Chapter 7 Markov chain and Hidden Markov model
7.1 Markov chain
7.2 Hidden Markov model
7.3 Forward algorithms and backward algorithms
7.4 Viterbi algorithm
7.5 Baum-Welch algorithm, Mamitsuka algorithm
7.6 HMM - using HMM to modeling
Chapter 8 Models for sequence evolution
8.1 Model for nucleotide replacement
8.2 Continuous time model - Jukes-Cantor, Kimura, Felsenstein, HKY
8.3 Discrete time model - Jukes-Cantor, Kimura, Felsenstein, HKY
Chapter 9 Structuring phylogenetic tree
9.1 What is phylogenetic tree? - concepts
9.2 Phylogenetic inference based on distance methods
9.3 Phylogenetic inference based on statistical methods
9.4 Searching algorithm for tree topological spaces
9.5 Phylogenetic inference using maximum likelihood methods
9.6 Model selection and pretesting
9.7 Phylogenetic tree structure estimation and testing
(Author portrait)
Xianyong Wang, Zhenghua Wang, National University of Defense Technology, Changsha, China
