Current Topics in Computational Molecular Biology

Current Topics in Computational Molecular Biology

by Abhishek



the book would be useful to a broad readership, including students, nonprofessionals, and bioinformatic experts who want to brush up topics related to their own research areas. The 19 chapters are grouped into four sections. The introductory section is a chapter by Temple Smith, who attempts to set bioinformatics into a useful historical context. For over half a century, mathematics and even computer-based analyses have played a fundamental role in bringing our biological understanding to its current level. To a very large extent, what is new is the type and sheer volume of new data. The birth of bioinformatics was a direct result of this new data explosion. As this interdisciplinary area matures, it is providing the data and computational support for functional genomics, which is defined as the research domain focused on linking the behavior of cells, organisms, and populations to the information encoded in the genomes. The second of the four sections consists of six chapters on computational methods for comparative sequence and genome analyses. Liu’s chapter presents a systematic development of the basic Bayesian methods alongside contrasting classical statistics procedures, emphasizing the conceptual importance of statistical modeling and the coherent nature of the Bayesian methodology. The missing data formulation is singled out as a constructive framework to help one build comprehensive Bayesian models and design e‰cient computational strategies. Liu describes the powerful computational techniques needed in Bayesian analysis, including the expectation-maximization algorithm for finding the marginal mode, Markov chain Monte Carlo algorithms for simulating from complex posterior distributions, and dynamic programming-like recursive procedures for marginalizing out uninteresting parameters or missing data. Liu shows that the popular motif sampler used for finding gene regulatory binding motifs and for aligning subtle protein motifs can be derived easily from a Bayesian missing data formulation. Huang’s chapter focuses on methods for comparing two sequences and their applications in the analysis of DNA and protein sequences. He presents a global alignment algorithm for comparing two sequences that are entirely similar. He also describes a local alignment algorithm for comparing sequences that contain locally similar regions. The chapter gives e‰cient computational techniques for comparing two long sequences and comparing two sets of sequences, and it provides real applications to illustrate the usefulness of sequence alignment programs in the analysis of DNA and protein sequences.

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