Principles of Statistical Genomics [Paperback]

Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle on the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach.

Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data.

Resourceful and easy to use, Principles of Statistical Genomics is?a comprehensive reference for researchers and graduate students studying statistical genomics.?

This book synthesizes current statistical methodologies applied to genome study, exploring statistical models and methods behind major bioinformatics software. Helps researchers choose the optimal algorithm to analyze data and to better interpret results.Part I Genetic Linkage Map
1 Map Functions
1.1 Physical map and genetic map
1.2 Derivation of map functions
1.3 Haldane map function
1.4 Kosambi map function
2 Recombination Fraction
2.1 Mating designs
2.2 Maximum likelihood estimation of recombination fraction
2.3 Standard error and significance test
2.4 Fishers scoring algorithm for estimating
2.5 EM algorithm for estimating
3 Genetic Map Construction
3.1 Critelóä